I understand so many people want to know. What happened to your eye? Why do you cover your eye? I bring my eye, yeah. You know they call me Ruga. Understand? Yeah, and shooting from afar, you use a sniper, yeah? And what does a sniper do? Covers one eye. So that's what I'm doing. Shooting from afar, I don't need to come close. Shooting and aiming my target in life. That's it. Ladies and gentlemen, welcome back to SPM bars. Bars with the stars. And today I have the Afrobeat star, fast-riding Nigerian superstar. Goes by the name Michael Adebayo, aka Ruga. Yeah, yeah, yeah. What's happening with me? I'm doing good. Good to see you man. Good to see you too, Maggie. Welcome to Kenya. I'm happy to be here. Thanks so much for having me. Welcome, sir. And can we say, Karibu Kenya, Karibu Nyumbani. Karibu Kenya, Karibu Nyumbani. Hakuna Matata. Means no worries. Good. You're a good student. So far, how's the going? Yeah, man, I've enjoyed myself so far. Even though it has been a little bit busy, but I've been trying to have fun along with it. And Kenyan people are amazing people. Beautiful set of people. Beautiful women. And nice men too, you know. So like, everyone has been accommodating. The hospitality is good. The scenery, everything, you know. I'm just enjoying myself on a mad way. Do you know that Kenyan women have been brought in the most beautiful women in Africa? Actually, I've been seen some nice... You don't even know if you're sure. You don't even know if you're sure. Yeah, yeah, yeah, yeah, yeah. Yeah, what I've seen so far is impressive. What do you look into, Agar? What do you look into? What do you look into, Agar? I don't like to put that out. It's good to know maybe your taste. You know what? Let me tell you why. Because, you know, if I say that now, it makes a lot of girls can actually pretend to be what they are not. You know? So when I see what I want in the girl, I'll take it. I don't need to tell anyone about that. But you have your taste. It's good to know. No, no. Nobody should know. Dark skin, light skin. I'll go for what I want. I might go for anything you want, I know. So your name, Ruga? Yeah. What is it all about your name? How did you get your name? Ruga means a gun. Ruga is a gun. It's a gun. It's a dangerous gun, you know? Right from time, there was this way I used to record. When I'm recording the studio, it was like a reflex. I was always like this when I'm recording. When I'm recording on the mic, I was like, I'm saying that. So actually, it was given to me by my boss, the CEO of Johnson World. So he saw me record several times and just called me Ruga. Well, first of all, when he gave me that name, I didn't know how to own up to the name because I was skeptical about it. But as time went on, as my sound was evolving during my grooming process, I owned up to the name and I knew that, yes, I'm ready to take on this name and that's why I'm here. Yeah. You're being signed under Johnson World Records. Yeah. By one of the best, I can say best, CEO of Johnson World, Prince. How do you feel to be mentored and inspired by Prince? It's a very amazing feeling. I got to meet the Prince and actually share almost everything he has. I've lived with him personally and I know almost everything about him. He is the kind of boss that is very, very relatable. You can talk to him anytime you want. He just comes to your house and just takes you out. He's not that serious, but he's still serious. They call us Johnson World, but you don't know when you say someone is Johnson. You feel like the person is not serious, but we're actually doing the serious thing in a playful way. You understand what I'm saying? So being with him is a very playful, serious feeling. You get to talk, you get to put out how you feel. It tells you how it feels and comes in agreement. It's actually fun, not every CEO does that. I feel like every boss should actually emulate that character of relating with whoever they're working with. It will help the organization for the betterment of what you're actually growing in the process that's it. You being signed and the Jocelyn World Records, do you feel pressure to produce, to write a good music? I don't feel any pressure. Music is already in me. I let it out. So there's no pressure because I thank God for the kind of talent he has given me. The talent I have is a kind of rushed talent. I just put it out how I feel in respect to my experience at that moment.  So there's no pressure. I do it when it's time and I hit the nail on the head. That's it. I understand you coming from Lagos, Nigeria. There's always pressure to carry a Nigerian vibe in your music. Do you feel that pressure? No. The Nigerian vibe in your music is already in you already. I don't think there's any pressure to actually do it.  You're Nigerian.  So I don't think there's any pressure to actually do that or anyone is forced to actually do that. No. You're Nigerian. It shows your Kenyan. It shows your Ugandan. It shows your African. It shows. You don't have to force you to be what you're not. That's it. I understand. So many people want to know what happened to your eye. Why do you cover your eye? Cover my eye. You know they call me Ruga. Understand? Yeah. And shooting from afar, you use a sniper, yeah? And what does a sniper do? Cover one eye. So that's what I'm doing. Shooting from afar. I don't need to come close. Shooting and aiming my target in life. That's it. You have your aims. You have your goals in music. Yeah. What do you want to achieve? I would say anywhere. I want to be trusted for good music as the years come by. I'm not really bothered about if it's doing numbers or it's not doing numbers.  I want everyone to know that I do good music and good music to me is a genre on its own. That's the passion. That's the way I'm so passionate about my music and about music as a whole. Because I listen to music. I listen to other people's music. I listen to my music too. I draw inspiration from everywhere. And so I want to be known for that good music. I want to be known as someone that doesn't disrespect the art and the craft of music. You're well known for writing good music. Let's talk about your writing skills. What inspired you to write your song Bounce? Okay. Actually it was based on vibes. Just male vibes. I can't remember the experience though. But I knew I can just tell you the experience I had recording it. So me and the producer were in the studio because I have a studio in our house. So we were in the studio and there was a party going on at the rooftop of my house. So while the party was going on, me and the producer were actually downstairs recording. And that's actually kind of… You didn't want to join them? No, I don't know. I don't know whether I didn't want to join them. But that was the vibe I was feeling at that time. So as we recorded, we just took it up to the rooftop where my guys then were partying and we played it there and boom. And we said, yeah, this is going in the EP. That's the thing. Tom, just to know, maybe you have similar looks with Whiskid. From your eyes, from the way you dye your hair, your height and everything. Did Whiskid inspired you to become who you are today? Whiskid inspired everybody. Whiskid inspired us, inspired every Afrobeat artist. Davido has inspired every Afrobeat artist, same as Bonobo has inspired every Afrobeater and everyone that has been there, Don Jersey and everyone. So we have looked up to these people for a very long time. And that's why we are making them proud and making Nigeria proud and Africa proud as old. I can say with the height, yeah, I look like Whiskid. That's it. Let's talk about Bonobo concert in London and go to Arena. How was the experience? Man, I want to thank God and I want to thank Bonobo for putting me on that way. Getting to UK for the first time and performing at the O2. I don't know if I would say I'm the first person that that would ever happen to. That was very amazing. It was an amazing feeling. Getting in there and people screaming my name and people were actually vibing with what I had to give. That was an amazing feeling and I'm grateful to everyone supporting me around the world. Be it Europe, Africa, America, everywhere. I love you guys so much. I want to thank everyone supporting me. Rue Nation for life. That's what I call my fan base. Rue Nation for life. Thanks so much. Rue Nation, we represent everywhere we are. They go hard everywhere I go to and that's an amazing feeling and I can't wait to share more music with you guys. That's it. I do understand during your performance there was a problem with the feedback. So you had to tell the DJ, hold on. Let me do the acapella way. Let me show you how I do my way. Do you have skills? Say it like you have to get it going, like you have to show people that now this is me, hear my voice. First of all, the fans are the only ones that can give it that type of energy. If the fans are not vibing, I don't think you have the confidence to actually pull that. If the fans are not singing your song, you'll not be able to sing the acapella like you just. So when I got there, I entered with Ruga, my last petifier to the mic from day one. I found out that the feedback was not good. So I said, okay, let me just finish. Then let's get to half of this song, then get to bounce immediately without wasting time. I would have done, I would have been next. But I said, I shouldn't waste time on this because of this problem. Let's get to bounce bounce out quickly. So I just sang the acapella with the crowd, the crowd we're singing to. I made sure I finished the whole song with acapella with the crowd singing with me. Then immediately I was done. I told the DJ to play and I was just dancing. I didn't have to do much anymore. Just my, eh, eh, eh, that's all. Ruga, I understand you are a good-fearing man. What is the relationship with you with God? Strong. It is what it is because I grew up in a church background. I got to live in a church. I wasn't a choir. I sang and I played the drum set. So like, I tried as much as possible, even with the fame to just still keep in touch with God. It's very important.  So you don't have to stress yourself about anything because God is in control. That's the way I work. How do you keep in touch with God?  When was the last time you went to church? Um, I attend online services. Before real? Yeah, yeah, yeah. I attend online services. Not every time. Can lie. I understand you are busy. Yeah, not every time. But I make sure I pray every day.  I make sure I read my Bible every day. So, Rema, a fellow artist signed under Joselyne Moore Records, has the same, has a song named Bombs. Yeah. It had, it brought up reactions, fans were saying it will bring clash between you and Rema. Yeah. What's the relationship? What's the working relationship with Rema? And then how did you handle that? It was actually funny. We're actually laughing together when we're seeing all those comments because what we're just saying, let's just bounce along. That's what me and Rema were just saying. Let's just bounce along. It's just fun to see how they're actually talking about us. You know, that means when people's faces we're, we're, we're in their minds where people are really focusing on us. It's hard for two people from a record label to blow up. That's a very hard thing, but that is it with Joselyne Moore. That's what we do and I'm happy about that. I do understand for an artist to go to another label, he or she needs a management. Maybe you could tell me Joselyne Moore Records has done to you so far. Yeah. Like I say, we are Joselyne Moore. Joselyne Moore can be, can mean playful, but we're actually serious in the process. We don't play, but we play.  So like the personal good, there's this character we're built to showcase.  Go hard or go home. That's it. You have to just go hard in everything you do. Make sure you just go hard. Make sure that what you're doing is drawing attention.  What you're doing is in a positive way.  That's it. All right. Understand, big fans of you are ladies. Yeah. How do you handle fame?  Just have fun with it. I'll tell everyone, have fun with it. Don't see it as something that is stressing you. Have fun with it. Enjoy yourself. Have time for your fans. You know, talk to your fans. Take pictures. You know, see it as something that you enjoy.  But still, you can get tired. You understand in the process, but as much as possible to like enjoy it too. That's it. Where do you see yourself in the next five years? I understand you have pressure to leave a good music. Where I see myself, I just see greatness and grace. That's all. I don't talk too much. Just greatness and grace. More greatness and grace, that's it. Any plans to your fans in Kenya? Any plans to stay in Kenya? Yeah, I could. I love Kenya. I could come here and get a house or something. Are you planning to work with any Kenyan artist so far? Yeah, yeah, yeah, yeah. Let's see how that goes. You just fingers crossed. Let's just see how that goes still. All right. There's always a feeling for an artist to win awards, to perform at the international stages, international level, to travel, to tour. Do you feel that pressure? No. I, first of all, I say what would be would be if I'm not at that stage in the next five years, I'm not going to bother myself. I'm not going to work myself up.  I continue till it happens. I feel like I appreciate as an artist or anybody doing anything. I appreciate the little things you are actually getting now. If you don't appreciate and thank God for that little thing that you are actually getting now, you won't be able to scale through and get into the next phase. So I try as much as possible to just be in no rush and just I see life as a process. Just go with the process. What will be will be there's no pressure on that.  What will be will be that's how I roll. You winning awards, can awards define a success of an artist? Yes, it can. It can. Do you want to win awards? I want to win awards. But if you never get a chance to win an award? Yeah, it's fine. It's fine. I keep working till I get one. You understand, I feel like eventually if I don't get an award, eventually I will get if I keep working. But there's no pressure at all.  Now I get broken down. That's not right. That's not how you get to the next level. You see it as, okay, I didn't win this. We move. Be happy for the person that won it and move forward. Keep working. Whatever will be will be. That's how. Okay. Kenyans have good music, have good artists. Yeah. What is the favorite music in Kenya? Have you heard of any? Do you have any of your best music? I enjoy a lot of South iso though. Like South iso I listen to a lot. Any other artists you know from Kenya? I can't remember the names where I've been listening to some sounds though. I can't remember the names, but I'll go, I'll go, make more findings and get back to people again. Any plans to work with any Kenyan artists? Like I said, let's see how that goes. Fingers crossed. We have the best artists.  Let's see how that goes. No, you can say a name. Maybe someone is favorite. Somebody we've been listening to. I would like to work with South iso still. Can I suggest some names? Yeah, you can. Major. We have Major. We have South iso. We have Kali Graf. We have Otile Brown. We have Trio Mio. We have Female Artist. We have... Yeah, those are the names that I can't even remember. The Kali Graf. Yes. The Major. Yeah. But still we keep moving and I feel like Africans repush each other. So that's why we're here to like help each other to grow. And I see Africans winning Grammys like being nominated in 10 categories and the Grammys, you understand, in the next two years because Africa is taking over. So that's the thing. Let's just support each other. There's always a concern that Nigerian artists, Nigerians support their own artist. There was... Once a time there was a comment on social media that Kenyan music is not well played in Nigeria because Nigerians want to support their own. But you see, most of Nigerian artists come to Kenya for tours, for shows, for music collaboration, for business. And it's vice versa to Kenyan artists. We don't get air plays in Nigeria. We don't get shows to perform in Nigeria. What's your take on that? Well, that... I don't know. But I feel like now if you check the Kenyan playlist on Apple and all this, you see a lot of Nigerian music, I feel like the people should actually listen to their music and push the music too by themselves too. I feel like if just like in some countries, you see where my situation whereby they're just listening to American artists every time and all of that, I don't know why it's like that. But I feel like if we are proud of what we have, that will actually help. Is it that Kenyan music is not good enough? No, it's good. It's good. It's good. What's your Kenyan artists do? Maybe your advice to them? What do you need to do to stand out? Well... Yeah, you can advise them that they're listening. They're ready to hear from you. Collaborations will work. Try to work more with artists from other countries, you know. That will help. You can't do it alone. You can't... No man is an island, you understand. When they come to Nigeria, they should try to work with Nigerian artists. And when Nigerians call me just like I'm here, I could try to work with a Kenyan artist too. So that's the way we push each other and make our names known in other countries. Same way in America, Americans try to collaborate with us just for their names to be known here. So I feel like working together will help. No beef, no fights. Don't feel like one country is better than one country. Just work. Let's just work together and that will build the momentum, build the growth. Okay. Lastly, as we finish up, I've seen you were a big fan of Manchester United. What inspired you to love football and support Manchester United? Life on a smile. I don't know, it just happened. What happened? I don't know. Why not ask? What's that? No, like there was this place where when I was young, I used to go film rentals. I always go there. It happened that almost every time they were always...   It just happened like so. Like I just loved the red and everything, right from the start. And I just adapted it and I loved Manchester United from there. Who's your favorite player in Manchester United? We're not doing that. We're not doing that. So you guys think yourself winning this season? We will. Okay, there's the camera. We're on top of the table already. For now? Don't worry, we still have more games to go. No, we will. Don't worry. For now, they're out there, you know, the goods. So to your Kenyan fans, maybe one of the advice to them? Keep loving each other, keep working, keep believing in each other and stay rugged.


OK, our next presentation will be from Andrea Frittoli. He is an open source developer advocate at IBM. And he will tell us about his experience using machine learning for continuous integration. Please, go. Thank you for the introduction. So welcome, everyone. So yeah, my name is Andrea Frittoli. So I present today a work done with a couple of colleagues, so Matt Trinich and Kira Hulfert. So the idea is that we wanted to really learn more about machine learning. And with open source, there's a lot of open source tools for machine learning. So there's a lot of documentation, a lot of books you can find, and you have very powerful tools at your disposal. So we thought, OK, let's make something that is interesting to us, and let's apply machine learning to it. We have been working with the OpenStack community. And there is a very large scale CI system there. So because the tools are kind of there, the open source tools, but what is hard to find is an open source data set, if you will. And you really need a good data set if you want to do something meaningful. And it's hard to get good quality data. But because we have this CI system in OpenStack and all the logs, everything is available publicly. So we thought, well, let's look at this data that we get from CI. So here we are showing in million of tests. So it's one for 10 power to seven. So it's the number of tests that run daily. And this is a graph. I got it updated until the beginning of March. And so we do continuous integration, continuous, continuously log data there in OpenStack. So we have lots of data. And one of the problems in the community there has always been to have enough engineers to triage all the failures and understand everything that is going on there. So we thought, well, we might use AI to try and solve this problem and help us as human and scale better. As you know, in open source community, you may have a limited amount of engineers. And they may have a limited amount of time that they can dedicate to the project. So we thought, OK, maybe AI can help us here looking at this log files from CI and understanding what is going on. So what we do there for OpenStack is integration testing in virtual machines. So every time a patch is submitted to OpenStack, OpenStack, if you don't know, it's a cloud management system. So it allows you to manage this cloud and virtual networks and storage and so forth. So we have public cloud donors that donate resources to the OpenStack project so we can spin up VMs. And every time you want to run tests, so we spin up VMs, we install the entire OpenStack on top of this virtual machine. And then we run end-to-end tests, so which means we actually create virtual machines, storage, networks, and so forth in the VM. And all these generate loads on the virtual machine. And we gather then system logs. We get our application logs. And we also gather what the logs that are produced by a tool which is called this that allows you to store in kind of CSV type format information about key statistics of your operating system, like the CPU utilization, the disk IO, the memory utilization, the average load on a minute or five minutes and so forth. So there are a large number of statistics. And we get this data for all the CI jobs, the million of tests that are run daily. So we thought we could use the distal data because this is numerical data as well, so it will make life a little bit easier in terms of normalization. And it's also interesting because we get this data running test on an open stack, but it's not necessarily open stack specific because it can, the same kind of system machine learning model you could be applied to any distal data generated in any kind of use case. Sorry about that. So one of the things that we recognized in the beginning maybe a bit late is that, yeah, so you need data. Actually, if you want to do something, as I was saying in the beginning, so even if you're not sure exactly what kind of machine learning model you're going to use and you're still considering things, if you have a chance to have data to start collecting it, do it as soon as possible because the more data you will be able to gather, the better it will be the outcome for your machine learning work. So what we have done, because the storage space from the CI system on an open stack side is limited space and they regularly clean up the logs there. So we set up a system that regularly fetches the logs that are interesting to us and stores them as raw data in S3 storage. We use a function that is run periodically, so a couple of times a day, I think, and we just get the latest logs and we store them in S3. So we built an application that let us create what we call a dataset, which is a normalized dataset that is taken out of the raw data that we created. So actually the first step before you dive into writing Python code or doing things is actually to explore your data and try to visualize it if possible. If there are multiple dimensions, you may try to cut it in some way or to aggregate it in some format so that you can find where there is more information in your data. Sometimes you have a lot of redundancy, like if you look at the system performance, like the different indicators, like in our case, you may see that the graph for the CPU matches very much the graph of the average load, for instance, and then maybe using both of them is redundant because it's the same kind of information. And because the more information you have, the more processing you have to do, so it's better to try and get rid of the information that is not relevant. So gather a lot of data first, but then focus on the one that is actually relevant for your model. This is an example. It's a Python tool. We call it CML, CI Machine Learning, that allows us from the raw dataset to select a number of features so that we can have a tool away from the raw data that we collected over time to extract a dataset that we know is a specific format. So we do two things. We filter it. So we filter in terms of features that we select. We want to use the CPU or the memory. And then we select the size of the dataset. And we do normalization. Normalization, because this is numeric already, means mostly that we want to make sure that the numbers are between minus one and one because it makes a computation much less CPU intensive, if you will. So this is an example of how the desktop later looks like, as I said, is a kind of CSV. So it's a big table. And you have timestamps. And for every timestamp, you have a lot of columns. So what we do here, we do a selection of which columns that are interesting for us. So this is the CPU utilization for user space. And this is the average load over one minute system load. And then we also do down sampling because we have this data for every second. And we realize, and I will get in more details about this later, that you don't really actually need that kind of granularity. So you may get good results with lower granularity. And then we do data normalization because we have a matrix as a result of the filtering we've done before. But this matrix is actually one sample for us because it's a result of one CI test. So we want to do unrolling, which means that this matrix actually becomes a vector, a long vector of numbers, and normalization. So this is a sample of unrolled data. So you see the CPU that was here. This free value, 6, 1, 1, 7, 5, 1, 2, 6, become actually three different columns. USR 1, USR 2, USR 3. So we have this for every instant in time that we selected. And these lines now, they correspond to different samples. So this is a CI job 1, CI job 2, and CI job 3. This makes it a bit more clear. And you see the numbers. They have random values. And after normalization, they all belong between minus 1 and 1. Another thing that you may want to take care when you build the data set is do not use everything for training. It's important to train your model. But it's also important to have an evaluation phase where you test how accurate is your model in predicting the things that you want to predict. And you may also want to have a small depth data set that you can use for fine-tuning the hyperparameters of your model. Again, all this, the data set, sorry, something I wanted to mention is that the labels term there is, labels is a bit overloaded as a word. In there, we just need the name of the features that we have, like this USR 1, USR 2. And this is the data and the classes is actually the values of the things that we want to predict. I'll get more details about that in a moment. Data sets are also stored in S3 storage. The next thing is that we wanted to do is to be able to define an experiment. So again, we have a tool written in Python that we created that allows us to define the hyperparameter of an experiment and store them in a format so that we can rerun experiments in a repeatable way. So we have a data set that we can recreate. And then we can run experiments. So there are multiple hyperparameters. We can select what is the structure of the neural network, for instance, how many hidden layers, how many neural layers, how many steps, and so forth.  So the reason we did the split, the separation in different steps. So preparation of the data, normalization, and then training is that in our experience here, we use TensorFlow as a model and TensorFlow has tools and APIs that allows you to do data normalization. But we wanted to do that directly with Python tools because when we started, we didn't know for sure whether we wanted to stick with TensorFlow, maybe switch to other different frameworks. So we wanted to be able to normalize our data and get it to a clean state that then we could use to feed to any kind of machine learning framework that we wanted to use. So we are able to run the training on your laptop locally with this, but we also integrated this with FIDL, FDL, which is an open source project by mainly contribution by IBM. And that basically allows you to take a model and do the training, distribute the training on a Kubernetes cluster. So this is a bit more about the training infrastructure. This is the picture is the architecture of FIDL itself. I will not get into the details of that. We use the estimator API in TensorFlow, which is actually based on Keras, which is another open source library, which allows very high level of abstraction it's very good if you want to start defining models, very simple. So we created the Semel wrapper, which is Python script that invoked the TensorFlow API and do the training, run the training, we do the normalization, the preparation of experiments. So as I was saying, that the machine learning framework is interchangeable and the training options is local or we can also, we have a help chart to deploy your application so then running container or we can use FIDL. So for the prediction side, the model that we have in mind that we implemented here is event driven. So because we have CI jobs that are executed, the event that are generated is when a CI job is completed, then new data is generated and we want to run inference on that data. So based on the model that we were trained. So we don't have a request for inference based on that model, but we have an event that is triggered because of that we've written a Kubernetes application that basically includes an MQTT client. OpenStack CI system generates MQTT events when jobs are completed. So we can listen to these events, download the logs and then run inference and predict the parameters that we want to predict on that. Because the CI job from OpenStack is considered for us a trusted source of data, we can use the new data that comes in as well to continue training the model with new data. Okay, so this is all about the kind of infrastructure and how we structure the project. So I will get a bit more now in details what kind of inference we've done, what kind of training. So we've done two main type of experiments. The first one is a binary classification. Basically we want to predict very simply if the tests are passed or failed. So we've seen enough logs over the years and we thought, well, let's make a bet. I think just looking at the system load, we are able to predict whether a test was passed or failed. So let's build the model and see if we are able to predict that. So it's a supervised training because we actually know whether the test is passed or failed. We use the DNN classifier with two classes from TensorFlow. We picked a specific CI job, which is called Tempestful. In OpenStack, there are several, but two main CI pipeline. One is the test check pipeline, which is executed when the code is initially submitted to the project before it's actually reviewed by developers. So there is a lot of variability in there and we didn't want to use that data because we didn't want to try and predict failures that are due to a typo in the code or something like that. Instead, we took data from the gate pipeline so the gate pipeline is executed on code that's been tested already by the automatic system, has been reviewed by the contributors and has been approved. So just before the code is merged, the tests are executed again and this is the gate pipeline and it's clean data and the failures are related maybe to infrastructure issues and maybe some new version of a library is released and it's pulled in the test and it fails. So maybe there is a race condition as that it was not picked up during the check testing and so forth. We have 3,000 examples and we split them in 2,000 hundred for training and then hundred for test. In terms of hyper parameter, we use kind of the standard settings, the real activation function for the output layer, sigmoid, which worked very well with the binary classification. Default optimizer again, so the adaptive gradient descent. It's a good one because basically it's adaptive. So it doesn't really matter the learning rate, the initial learning rate that you set because it will adapt itself and select a good value for that. And so it's a neural network. We have, we started with five hidden layers and 100 units per layer. And we run 500 epochs, which means that we go 500 times over our input cell. We have 2,100 samples for training so we repeat that 500 times and then we use an input function that randomizes the order to make it more effective. So then we run a different exam, different test. So our main, sorry, key metric is accuracy that we looked at and we tried different combination of features. So for instance, just looking at the CPU, just looking at the memory or the average load or combination of different features on the DSTAT data. And we saw that in terms of accuracy, the combination of CPU and average system load was the most effective one. So this looks a bit the other way around because accuracy, the better, the closer to one, the better, right? So all these tests that we did, they had reasonable accuracy but they were close enough that if you would display them with bars that from between zero and one, you wouldn't notice the difference enough. So we actually displayed one minus the accuracy so that you can appreciate the difference in accuracy between the different experiments. We also looked at the loss. The loss was slightly worse in this case but still acceptable and not very much different from the other one, so. And with this we achieved an accuracy of 0.992. Which is pretty good, we think. It's pretty reasonable. On a 900 evaluation set, it means about seven mistakes, which is pretty good. Another thing that we wanted to see if it's how well this training model could work with a different CI job. So there are several CI jobs that are executed for OpenStack and for each we run end-to-end test which is the tempest-full job and then there is a Python 3 version of that which is very similar but slightly different because one of the components, Swift, doesn't support Python 3. So those tests are not executed and that service is not running which affects the overall system matrix, sorry. So what we need to train the model with the tempest-full dataset around the evaluation of the tempest-full Python 3. So, and the result was kind of okay. So the accuracy went down from 0.994 to 0.953 which is still reasonable. But the loss doubled and the precision against recall area, it's much worse which means that it may mean that our data on the LCI job maybe was too biased or maybe there was a little bit of overfitting that was happening there. So to summarize for the binary classification we found that with 10 seconds sampling is the best but one minute maybe enough that we can get a very good accuracy and a very good precision versus recall curve. The graph on the right side is the training loss while the training is done so it goes down smoothly and it's pretty consistent. The second experiment is a multi-class type of classification that we did. As I was saying in the beginning, the VMs are donated by different cloud providers around the world. So one thing that we wanted to see if you're able just looking at the system profile to detect which cloud provider is hosting the test. So the classes in this case are 10 because they are 10 different that are, sorry, providing resources. So we use a similar setup, the data set is the same type of hyperparameters there and again we used a resolution of one minute, the same features, the loss converges but the accuracy we got was 0.6 which is not really good. Not good enough, definitely. So we went back to doing different experiments. Again, we tried different combinations and we tried okay, let's put more data in it. Maybe we need more system features and we added the memory, we tried even with this scale but there was no significant improvement in the accuracy. The top value there is 0.6 of the one minus accuracy. So it's not, sorry, the bottom one is 0.4 so which again is the 0.6 accuracy. So we said, well, maybe we are down sampling too much. So we tried changing the resolution. We tried 10 seconds, 30 seconds. Maybe we tried even down sampling more, going to one minute and 10 minutes but again the best we could get was not very good with slightly improvement here going down to 10 seconds but it's not really significant. We tried changing the hyperparameters. Maybe we need a more complex or a simpler network there so it turns out that actually using three layers instead of five layers is likely better. We got 0.668 but still not really useful. We couldn't really do a good prediction there. So what changed thing was actually going back to the data and this is one of the key outcome of the work that we've done and a good takeaway is that the data is really important so you should know your data and should look at your data and understand what is going on as much as possible. So as I said before, we had 10 classes but it turns out that some of these classes were actually could be converged because we had several regions from the same cloud provider. So for example, OVA each had two different regions and another cloud providers had three different regions so we thought well maybe because they're the same cloud provider even if they're in different geographies they will use a similar type of setup. They will use similar hardware so maybe we can collapse them into a single class. This way we reduced classes to six and we tried running our experiments again and what we saw is that the accuracy was increasing dramatically so we managed to achieve 0.9 as an accuracy. So basically the takeaway from here was that we were trying to separate things that were actually not possible to separate because they're very similar. So that's why we couldn't get a good accuracy because it was kind of random for the model whether to detect that a certain test ran in region A or region B for a certain time cloud provider. So we did some extra tuning we tried different network topologies and we managed to get to 0.9 to five in terms of accuracy with a one minute resolution and again using a CPU and a system average. We tried again and then doing this for the multiclass applying to a different CI job so we used the same similar setup as before but this time it didn't work really that well. So the best accuracy we could get doing this kind of training with one CI job and evaluation with a different CI job was around 0.77 so not really good and also the average loss and the loss increased very much. So to summarize with a multiclass example again we had this user CPU and the one minute load average was the best selection in terms of features from the DSA data to use to predict this. So it's an interesting takeaway that just looking at a CPU and a system average load every minute on a system you can actually identify what is the underlying cloud provider that is providing the VM. So this looks, it makes us think that this could be used maybe in future for doing things like creating specific benchmarks that could be then applied to different cloud providers and see and recognize different type of issues in there. So to conclude, I'm not sure how we're doing the time. Yeah, so to conclude as I was saying a key takeaway, collect data, you need a data set. And even if there is a lot of open source software I think a big challenge will be open source data. And a lot of data, a lot of company has a lot of closer data. The good thing with large open source communities that they have this ability to produce a lot of data which is open source, the data itself available for everyone and you can actually use that to run machine learning experiments. The other thing is you need domain specific knowledge on the data that you're using. If you are a lawyer and you try to do a machine learning project for something related to accounting you probably won't work. So in our case we worked for several years on tuning the QA and CI system and reading logs for open stack. So we knew the data so that was why it was a good fit. I strongly recommend, I mean in our case because it's just a simple neural network that we used it worked well just running on CPU. So we tried running on GPU but we didn't see very much improvement. But nonetheless, we recommend work with cloud tools, run your training on the cloud. There are several ways you can do that. But you don't want to have a dependency like you start your training on your laptop and then battery runs out or you have to go somewhere. You want to have this running in the cloud and you want to have repeatable experiments and the ability to collect the data and have it on the cloud. Especially because if it's just you but maybe then some other colleagues or some friend joining the project and you want to share data and collaborate. So if you have everything set up in the cloud it makes things much easier. And also, well, we were able to confirm that the system load plays a role in the failures. Of course with the binary classification we actually got the information about that system is the tests that failed or they're passed which we know already but you get a level of confidence as well with the prediction. So in future we think we could use this confidence level that you can get about the failure of the test to build a system that allows you to see that a test even if it's reported as passed it's not identified as passed with high confidence by the system which might mean, which might able, which might let you that detect early that something is going wrong. So situation, real life situation we had with the CI system is that page after page maybe some tests are added which add race condition maybe the performance in one of the services degrades so it starts using more memory unnecessarily or simply there are more services. So the CI jobs quality of it slightly degrades over time until it gets to a point where the failure rate for tests that should actually pass becomes too high so it's not good anymore for a CI system because it brings you a very bad developer experience. So having this kind of confidence evaluation whether the test is passed or failed may be used to detect that something is getting worse in your test system so that you can use this to predict that something is going wrong in the wrong direction. Other future work that we might do is to look at using similar techniques for other type of data like the logs from application for instance. For other features we could look at predicting other things like the type of failure that happened. The difficult bit with that is that you really then need a human created data set. So basically if you have engineers that usually look at the logs and they say oh well this failed, yes but it failed because I don't know Clive provider X had an outage or because there was a network issue somewhere. So you could use this information, start collecting this information and label your experiment with this information and then you could use this to train in a similar way a model to detect the type of failure but this requires a lot of work because you need to connect to collect all this human input basically. And other things that we thought about for future work is that you could try to define some metric that allows us trying to do clustering on the data. Clustering is interesting, it's very difficult though but if you manage to find the right metric you may be able then to display your data in some n dimensional space or two or three dimensional space and see the specific test runs leave in a certain cluster in certain areas of the space and that allows you to identify that this CI job or this data samples they have something in common and this allows you basically, it's a way that you can use to automatically build classes without doing the human created data set before. Other things that we want to do explore more for the job portability and if we are able to move forward with getting interesting information maybe from the clustering we could use this data also in the real CI system. So the talk is available on GitHub and all the code that we wrote for CML project is also available on GitHub and if you have any question there might be some. Sorry, could you explain, go through again why the prediction of the pass and failure is useful? From what I could tell the input data for prediction is only the load levels of the test being run. Is that right, did I miss something? So as it is now it's information we already have so in that sense it's useful in a way in the sense that it proves us that there is information that is embedded in system data from this load profiles we are able to infer something that is whether the test is passed or failed with a very good accuracy. And so this basically it was the beginning in the beginning what prompted us to continue and do other kind of experiments and look at the multi-class classification for instance and try to extract more data from this load data that we have on the system that's basically it. I mean the other thing that I mentioned, I mean it's not something that we really tried but because you can get an estimation of the confidence of the prediction you could build a system where you get you can say well the system predicts that this is passed or failed with a certain level of confidence and then you could use this confidence level in your CI system to say well if I'm running the same test the same type of CI jobs and the confidence degrades it means that something is changing in the system we are testing. And so you could use this information as a kind of warning to look at what is going on. It could be that for instance the memory utilization of your service is increased and that changed the load profile and that degrades the level of the prediction the confidence in the prediction. Okay, got it now, thanks.


Okay, oh wow, it just tastes like a peach candy rink. I'm sorry. I'm going to put that in the video now. Are you ready to go? Yeah, everything's been set down. We did the last couple this morning, so they're in the room. Perfect, perfect. Ian, when the athletes get there, all the hotels obviously booked, have we sorted it out, got everything ready for the rooms? Yes, we actually do. So just to give everyone context, we actually kind of set up a little bit of an athlete just so everyone can feel immersed in the energy brand. They can really enjoy themselves in the weekend because we know that these trips, a lot of people think of the holidays, but what really, really needs to remember is that they're working on these trips as well. So we need to make sure that the brand is front and centre there as well. So the first thing that we actually have set up is actually a PR box that we've been hard at work. So it is a fine new peach candy rink limited edition. One of the main things Auditors and everything is booked Christmas party as well. He's booked in for US staff and athletes over there. Hallie, that huge banner from the ceiling that we were trying to get from China. I know we didn't have much luck over Christmas. Do you have the tracking? What do we do for the Sydney Expo? We had like... Oh yeah, there was that big inflatable shaker. We still have that? We actually do still have that. We haven't used that since the pandemic of things. Was that like four years ago? Like 2018, it sits in the storage room out there. Well, let's check it out as well. I don't know how big it is. It's like, I don't know, it may or may not fit in this big. Like actually it'd be good to use, but I don't know, far out, go. Do you want me to go check? Yeah, do you want me to get that? Yeah. I didn't know you'd blow it up. He invited us to blow it out of our mouths. I'm not happy with that. Look what I found. I mean, why wouldn't it work? Yeah, it doesn't work. That's true. It's true to America. I think that's true. They're going to bring a stick in it actually. This is actually better than Halley's banner. It's sorted, so athletes are ready to go. Tickets are booked. We have something else instead of the banner now. So we just have to see how everything unfolds. So fingers crossed, it's a smooth one. We're back in action, back in the expo. So yeah, watch this space. All right, guys. So I've got a big cat behind the camera. Chanel is going to get us some food. Just finalizing some things for the LA Fitness Show 2023. It's 8.30 here on a Saturday night in Sydney. We're about to head out to LA. Can't wait to take you guys through this epic, epic journey we're about to be on. It's going to be a huge year. We're going to try to do as many expo tales as we can. We really want to be able to connect with our community. It's what the brand is all about. Connecting with our people, uplifting people. We rise forward to others. And yeah, it's going to be an epic, epic journey. I'm coming for you guys to see it. So we just got to LA. We checked in and we've made our way down to Manhattan Beach. So we've just done a bit of a walk around trying to find somewhere to eat and we've decided on premises. It looks the most like a 5E. And yeah, we just came for some food on the right side. I'm just happy this heat is here because I'm freezing after coming from summer. So, it's a good thing we have burger. I'm going to make you. Do you want to go to butter? Yeah. Butter cover? Yeah. Like the mutual leaves. Design this for the expo. Is that the hoodie? Yeah. I like the color. I like the vibe. Pre-workout flavor, which you guys will see very soon. We've got a few hours until the event now. So what should we do? Get food. Show them the VIP. VIP lounge? Yeah, we should. Okay, let's go. It's top secret, but we'll show y'all. Let's do it. No one's allowed up here. It's top secret. So we're going to take you guys to show you. Way too much time at those parties for the athlete. You've got to have a bar at a gym, right? So we've also gotten really good at basketball, because we play every day. We play every single day. Let's see it. Amanda, you go. Go. Sorry, I didn't start yet. All right, so that's our arcade. We have one more place to show you. It's our content room. So that's over this way. This is where we get ready for photo shoots. We thumb a lot of reels and TikToks and stuff like that. This is a good room to film content. This would be a really good room to film content for the new Oxy Shred Peach Candy Room Slammer. Somebody, Peach Candy Ring for some Oxy Shred. Got the new flavor right here. Why don't you test it out? Oh my God. Wow, this is perfect. Then we got our peach candies, too. So we got our peach candy free and our actual candies. So we're going to do a comparison. Yep. This is so good. This is my favorite flavor of the energy drink. Oh, this is fire. I'm so happy they made it into an Oxy Shred flavor. Mmm. Oh, this is gas. That's really good. To go with our candy. This will be our pre-workout routine, like every day. We'll come up here and we'll drink this, then we'll eat some candy. Yeah. 10 out of 10. 10 out of 10. For sure. All right, guys. We just passed the bottom top here. So we're pulled over and Houston, Texas. We have one of our new athletes, Hannah Lee. We've got Amanda Beck and Shanell with us as well. We're going to drop in here today to load Hannah up on some products because they haven't arrived yet. So let's go shopping. Oh, here it is. All right, so Kiwi Strawberry. This is actually voted number one flavor. It is. Kiwi Strawberry Oxy Shred. Best to take first thing in the morning when you get up instead of your morning coffee, if you want. You can use it before you work out as well. Mid-afternoon, kind of pick me up. So what it's going to do is going to give you natural clean energy. It's also going to help suppress your appetite so you can kind of control your calorie intake during a cut. And it's going to make you sweat like crazy in the heat of the summer. This one here is really cool as well. So this one's got no caffeine in it. So if you do want to train at night time, I'll recommend this. Which flavor? I liked raspberry kisses. Raspberry kisses? Yeah, although without. I trust Amanda's opinion. Raspberry kisses. Boom. All right. What's in here? Protein? Can you say that? It's a staple, obviously. You can mix this with all our products. You know how to use it. You've been training for a while. It will help build the booty. Every gym girl needs this. For the booty gains. Hey guys, we are here at Alphalands. Our here sampling the new flavor of Oxy Shred is Peach Candy Ring. And we both tried it earlier and it is so good. We are handing out free shakers and free samples of the new flavor. So come try it and meet some of the athletes with the HP Labs. Starts at six. That was pretty good. You can do the staker too to take with it. Yeah, of course. Throw this around your stomach. Yeah, you too. Yeah, they were here last time. Completed all three challenges. Got themselves a boo-doo pride. Yeah, they crushed it. This is really good. Read it, ten out of ten. I give it like a nine point five. Nine point five out of ten. This is good. This is actually good. You've never had something like that before. It's a stem free free. If this wasn't free, I would drink this throughout the day. It tastes like a juice. You can have it first thing in the morning instead of a coffee. You can also have it at two p.m. in the afternoon picking me up. And it doesn't have any fat alanine. This is delicious. That was really good. Nine point two out of ten. Follow me back here. I'll say it's going to be dropping soon. Thank you, everybody. Thank you so much for coming to our event. The new flavor is dropping in the next few weeks here. It was a huge hit. We got all of our tubs. We got a lot of good feedback. Thank you all.


SCP-492 was formerly an animatronic dummy for a pirate attraction in the now-abandoned R.I.P. amusement park. SCP-492 is primarily made of low-quality synthetic cloth, and all of the hydraulic and electronic components inside it had been removed. However, SCP-492 has gained motor skills and sentience. Medical scans of SCP-492 have shown that it is completely hollow, and is for all purposes, a sapient cloth dummy. It is not known how SCP-492 can standard move without internal or external support structures, though tests have shown that SCP-492's cloth body can expand and contract in a manner similar to human muscle.


What is your why? My why? My why is to dramatically improve or expand the exponential growth of intellectual thought globally. You walk me through that. Sure. And there are a few passion projects that right now. Yeah, yeah, yeah. So, Loki is in exchange, I think, the eBay for intellectual property or intellectual thought itself, right? So, through our system, things don't actually need to be patentable in order to be liquid and have value, right? I've always had a problem with that old adage that an idea is worthless, it's the execution that matters. We spoke earlier about a second passion project that you have. Yeah, yeah, yeah. I'd like to try to inspire a couple of new fields, right? So, one of the first fields that I really want to do is this thing called ethical engineering, or sorry, ethical economics, so, ethnomics for short. Ethical economics. Yes. And then the other side of it is a field which I'm calling economics engineering, right? So, for the longest time, economists, finance people, right, they focused on how to eke the best or the most out of any economic structure, any economic model, any finance. The problem with that is that you're essentially trying to figure out how to divvy up the pie in slightly smaller and smaller increments, right? And then, worse yet, you get into things like mortgage-backed securities, credit default swaps, and now you're taking a pie that was cut so small and you're leveraging it to offer it to somebody else without actually creating a new pie, right? What I find really intriguing is that with blockchain and cryptocurrency, now we're designing new economic systems. And just like designing or building a plane, a car, anything else, I want to know that that economics model that is my basis, right, everything that my value, my determination value, that that has redundancies in it, right, that it's airworthy, that it has precautions, that it has stop gaps and failure opportunities, right? That things are thought of, economics is thought of as an engineering perspective, right? How do we do the mechanical design or the engineering for how this thing can work, regardless of people being involved, right? How does this work? How does it fail properly without hurting too many people? How do we mitigate risk, right? How do we leverage risk to go forward, right? So it's all about engineering for my favorite thing, which is mutually aligned incentives. The trouble that we have thus far is one fundamental flaw with crypto that we're finally starting to fix, purchasing power, right? Real value, like what does it mean in the real world? This is where asset-backed currencies come in, asset-backed cryptocurrencies, right? Because we had this network, we had this network of kind of economic perpetual motion where contribution to the network or contribution to the civilization or society, right, ends up getting value back. That value then is used to buy the consumption of things, right? Production equals and determines the value for consumption. That makes sense, but if there's no outside recognition, then there's no purchasing power and there's no leverage, right? So you had to figure out, or I had to figure out how we were going to bridge the gaps. The way that we did that was a couple of fairly, fairly simple ways in my eyes, but these asset-backed cryptocurrencies means that if a bank recognizes the asset already as a collateralized opportunity, right, whether it's a physical property or in our case intellectual property, IP is a recognized asset. If you back all of the currency, all of it with intellectual property, now you can use the coin itself as collateral, right? Which means savings accounts, mortgages, annuities plays, right? Buying groceries, right? Well, I appreciate that. Absolutely. I appreciate the time. Thank you once again. Thank you.


Welcome once again. Our first conversation this morning is on the local government elections that took place in Lagos State on Saturday. Vota turnout was low and it was very peaceful. Some parties still are complaining of Vota suppression and the beating up of party agents with pictures that we can't show because they're bloody. And we still of course are verifying them but we have this clip. That I've been in Lekki we only just got, I just got to vote at exactly almost 3.15 p.m. The election materials at 013 Admiralty in Lekki did not turn up till 15 minutes ago. Before then I had gone to about 4 different locations within Lekki. I was told it was in VI, I went to 1004, went to about 5 polling booths. Ladies, friends, we need to ensure that going forward this doesn't happen. And yesterday the Youth Party addressed a news conference pointing out specific instances of rigging. Specifically there were three sets of challenges that we saw. In Ikate, from polling units 3 to 8, the polling units were moved into the palace of the oba. Disenfranchising any voters who wanted to vote for anyone other than the ruling party. It's a terrible affront to democracy to suggest that polling booths should be placed in places where they are unreachable by people who want to vote for any other parties beyond the ruling party. Beyond that in Igboko Shu we saw a series of violence and harassment of voters, of our polling agents and of anybody again who wished to vote for anyone beyond the ruling party. Lastly in Lekki several polling units did not receive materials until very very late in the day. The latest of these were received at 3.15 p.m. Polling should have stopped at 3 p.m. but the polling materials did not actually arrive until 3.15 p.m. And so many people who had come out in the morning, who had come out earlier in the afternoon to vote did not have the opportunity to cast their votes. And also a report by PlansTV's Annetta Felix. Full and ad hoc staff of the Lagos State Independent Electoral Commission, LISEC receive election materials at the Ikoi Obalinde Local Council Development Area Secretariat. With the necessary tools and materials including card readers and ballot boxes received, they board bosses designated to take them to different polling units in Etiosa local government area. It's past 9 a.m. and election supervisors of the People's Democratic Party get these issues off their chest. At the moment I think it's very slow. Most places still don't have their polling unit sets. Most of the polling booths that are close to the barrack, they said they should bring it opposite the road. It's not done. That's where INEC had their election. Why are you bringing it too far away from the people? At polling unit 2 ward 10 in Ije, voting begins slowly with a set up of electoral materials as voters trickle in. So we are going to actually be on a single line and we are starting now. Residents of Ije, including a chairmanship candidate, commence the election as peaceful and the environment calm. It's going to be very cool and calm. No stress. The only issue I had is my card is broken and instead of it to be registered automatically, then registered it manually. The vote is okay. It's going well. Everything is normal. So no fights. I just want peaceful for Nigeria. The Lasek are trying. They came in earlier. But beyond to the calm and outside the gates, we'll witness some shady activities including vote buying. They gave you 1000 naira and I was wondering why? Why did you decide to collect the 1000 naira? I don't know. Instead of the crowds to build, less people show up to cast their votes, just as in other polling units in the local government area. Security experts have the opinion that public disaffection may be responsible for the low turnout. Aneta, Felix, plus TV Africa. And that's a report by Aneta Felix from the elections on Saturday. We also had spoken with Deji Doherty later on that day to share his thoughts on the events on that day. Most of the things that we, of course, witnessed and reported with regards to low turnout. Even the governor of the state, Babaji Desong, who had mentioned pretty much the same thing, that it was a low turnout of persons and the electorate at the election. Talking about the election being peaceful, it's interesting seeing that that is something that we have to notice that the elections were peaceful. Maybe because of what Nigerians are used to from elections in the past, elections are peaceful. They normally should be peaceful. If you look at elections in other parts of the world, they are expected to be peaceful. But when you hear people say, well, the election has been peaceful so far, there's been no crisis. It means that Nigerians are used to a situation where there is either ballot box snatching or there's dogs sent to come disrupt polling units and some of all of that. But some other thing that I believe we will also bring up is the relevance of the local government elections. One of the things that I had to speak with Deji Doherty and the APC spokesperson also on Saturday about, how relevant are local government elections to Nigerians across the country? How many people recognize or even remember that there are local government elections? How many people are willing to participate in the local government elections? And my theory really, besides the fact that people had said that people didn't turn out or the voters didn't turn out simply because they don't trust the process. I might say, okay, yes, there is that point. But I feel like another reason there's not a lot of people who showed up at the local government elections is because they haven't been able to see the actual relevance of the local government elections and the relevance of the local government itself. How many Nigerians remember where their local government office is? How many Nigerians know who their local government chairman is? How many Nigerians know what the local government should do for them? Including, you know, in their marketplaces and in the streets and their gutters and little bits of infrastructure here and there. Besides paying local government salaries at the end of every month. How many Nigerians know the relevance of the local government itself? It might be one of the reasons not very many people are eager to vote. The governorship elections, the House of Assembly elections seem to have more popularity than the local government elections in Nigeria. Local council elections also. The governorship and, of course, the presidential elections get at all the publicity. They get all, you know, the publicity that is necessary. All the conversations concerning elections in Nigeria. If you ask the next ten Nigerians you see on the streets, I'm very sure they got seven or eight of them. I'm only, you know, going to remember that there is a governorship election and then there is a presidential election and the, you know, House of Assembly, State House of Assembly and National House of Assembly, National Assembly rather, elections. But the local government doesn't get that much publicity.  And that's one of the things that I feel is a problem with the local government elections in Nigeria. It hasn't been relevant. There's the NFIU directive, you know, that I think it was in 2016 that made it, you know, a compulsory that states and local governments receive their funds directly from the federal government. That has been in, you know, in process since. But it doesn't seem like a lot has changed even since then.  Has any local government chairman been able to show the relevance of the local government in the lives of the electorate to do the things that the local government should be responsible for in the lives of the electorate? How many marketplaces? How many roads have been built? How many streets have been fixed? How many streetlights have been fixed?  You know, that's the one that is most relevant to the people. But have they truly been relevant to the people of the state in every single level? Look across local governments in northern Nigeria that continue to receive allocations every month. And like I said, an average of 150 to 250 million, sometimes more, every month, those local government areas have had security challenges for so long, they've basically not functioned in the last four, five, six, maybe even ten years because of security challenges that they've been having. But they continue to receive allocations. You still cannot point out one thing or another that the local government uses those funds for. Look at the gotters around where you live.  You still cannot point out anything that the local government truly uses these funds for, aside paying salaries at the end of the month. We'll take a short break and when we come back, we'll continue with this conversation and of course we'll be bringing in our guests.


Hello everybody. I am Niharika and I'm Manus and we are from India, which is still a developing country And we have been contributing towards open source for about two to three years now and while we were having fun doing that We definitely faced a lot of Limitations and resistance as I would like to say But it wasn't so this talk is as highly personalized as it is It's also a very general view of what's the Kind of situation that actually prevails in Mostly India, but there are some very Right, so there are some really strong talking points for other nations or other developing nations Which don't currently hold as much truly for India and by that I mean like for Africa It's a whole new situation, but in India it is an Indian Southeast Asia It's a little more Revolent than most and all these things are like limited us in what we could have achieved Right, so just by a show of hands. How many of you right now think that you're from a developing nation? All right, so that's basically like 50 percent. It's cool So we've been claiming all this while that we are from a developing nation We contribute to open source and we face a lot of limitations So though throughout the talk, we would first talk about what kind of groups are basically being affected by This limitation and what these limitations are then we would go on to tell We would talk about how people Definitely thought that yeah, these are limitations that are happening in the world So we should overcome that so they made a lot of student-friendly or People-friendly open source programs right promote more contributions from students Students minorities or people who just that who they thought that should be more involved in open source because everyone wants young developers And then we would analyze the kind of participation that these student-friendly open source programs receive And we would have we would find a really interesting trend here And we would then basically convince you that even though the agenda for these open source programs was a very noble one Somehow it's basically ultimately Lacking in its very sense something is going very wrong somewhere Which I do not know how much the people who are not in a developing country realize Because we feel that this happens behind the behind the It is like a very mentality kind of thing right and these things are Something that I do not know if people in non in developed countries really understand And then we would just have a general brainstorming session on how the situation can be improved What or changes have to be made or can be made Like so like we said there are two different types of groups that are being affected There's these are broadly the ones that we feel are hugely affected by these limitations The first we would talk about our students University core structure values marks. Let's just take India for an example the very essence of success in Indian Education system means a high pointer a high GPA very good marks in your Core structure solely you are basically set for your life. You will get a job. You'll get married You'll get set and that is it you do not Indian families Indian parents Indian mentality does not account for what's happening What change you're making in the world because in India? It's a developing country itself. So they think money equals success How you are a person doesn't really count I mean even during marriage when proposals come they do check your mark sheets. It's that kind of And we're not talking about caste system here. It's a completely different topic right, so that and so over here pointers make a make a lot of Make a lot of decision as to who you are as a person if you're a success or a failure so let's say for example, I'm a person who Takes time out of my college work and contributes to an open source organization Maybe multiple of them. So I have basically I have Worldly skills I can contribute to real code in the real world, which is definitely impacting a lot of people But I am doing this out of my course college core structure So maybe I'm performing Averagely in my college coursework, but at the same time I'm I'm sitting in my place at home locally and making a huge impact People don't get that as opposed to there's someone who wrote learns College courses day and night scores a 10 GPA gets into an IIT, which is basically an MIT for it It's it's like it's like IIT is like the benchmark for success So if so like for me, we'd really you know be personalizing How you are like for example if I'm to get into computer science at one of these schools Then they would only check my marks in maths and chemistry I mean chemistry is not even remotely related to computer science and however good I am But I won't get into those only those with like good marks in chemistry or physics would get into It's that kind of thing right so what was stressing over here is that we study courses which aren't even relevant to us and That counts but when we do things out of the coursework that are relevant to us that Universities do not recognize that at all So I am an outreachy intern and it has this Concept that I could use my outreachy intern as a university project But that would only work if my university accepts my outreachy work as a project the mentality So that that is the fourth point over here either faculty is unaware or they're unsupportive What's happening over here is that so if we have semester projects or which are either two months long or six months long we So they expect either a research intern or they expect an industrial intern or they expect projects under them Which might pertain to machine learning or IOT or anything But if you go and tell them that I am why I will be contributing to an open-source organization Maybe it's Mozilla, which is so highly named a Fedora and I even if I say that I'm getting paid for it And I'll get a proper internship letter in the end They would just do they just wouldn't recognize it they would say no not not something you would do here try it out later It's just that bad. It's very common everywhere So what we are trying to stress over here that all this while we were saying that okay We are contributing to open source Please convert our effort into course credits and they said no So we're like okay fine. We want we don't want course credits But at least give us some compensatory leaves. Let us go to conferences You know if put sugarcoating it we are kind of representing our college So be proud and let us do that and on a good level we're representing India And you know we're being actually productive over here, but they just don't do that And this is exam not not rescheduling exams is one thing then there are Most of the conferences in USA happened during majority of the colleges is N semester exams and You which is basically don't get to go over here. What's so the second group? That's affected are Fetcher, sorry freshers or jobs, which is which are like people in between jobs All right, so this is actually So In India there are a lot of financial companies that Offer jobs to people right after they graduate and find fintech companies translate to a lot of money Just when you start working, but it also Translates to what are they really doing are they really putting an actual impact in the world at the same time these people's Way of selecting Candidates has nothing to do with your resume. They only go for your pointer Exactly, so I have sat in so many interviews Goldman Sachs Morgan Stanley JPMorgan Chase I mean I could go on and on and I have this huge resume where I try to list down all my open source Contributions and they basic and or even let's just not even say open source Extra-curricular activities, which I know count so much in developed nations because you're putting the effort to do something different from your Usual coursework and they basically put aside all of that and then they make you sit and make they expect you to Clear their customized coding test in spite of whatever achievements you have in your open source You're not coding system other your software engineering skills communication skills They don't really matter at the start itself. They will check your grade pointer and then based on that they will shortlist you and With all the other things like attendance and everything if you are going to work on your actual software engineering skills And your gate point is going to go around so there are so I would actually said in 98% of the companies that are in India Even if they're MNC's they they select Not looking on your resume. They might shortlist you. I do not know for on what basis again but it's they just do not care if you have open source contributions and that I mean so That just I mean, what do we do? You're not supporting us anywhere, but here we are still Contributing in spite of all this resistance social and then Remote jobs, which is so popular In the love nations that say but if you say remote jobs, I hear Indian parents saying you don't have a job That doesn't even qualify And you don't need to work. You don't need to get a job. That is what I Mean students don't need that you should focus on your studies Even though one could balance both equally Also, like I know as this is a personal thing if I if I grad like when I'll graduate if I say that I have a remote job Which is paying me more than any other job that I'll you know any under industrial job My mom might even disown me for that matter. I do not know So so I mean that basically boils down to the third point that even we it's all extra time that we're taking out to Contribute to open source. I know all of you are doing that too But we just don't have that support and there's just zero recognition So conferences like these are somewhere where we understand. Yeah, so there's a bigger picture to all of it. So whoo-hoo And then there are programs some of which are local in India like girl's script And there's the Chinese one of Alibaba some more of code and then there are the international ones like Google some of code outracky is Summer of code hacktoberfest. I mean these programs are supposed to promote and they do achieve a lot of things but then When all the other factors come in, I mean, I think some of these programs are very misused and These are the statistics These are gets and this is This is the student statistics if you could see like India has the highest number of students every other And then if you look at the mentor statistics, like it's United States is all the way to the top than Germany and then India and the thing here is people The only reason Students are so much compared to everyone else and they don't continue as mentors I mean, they have to face such limitations that they can't contribute to programs later they can't continue their contributions and You can clearly see that and the mentor statistics and this is the outrack even Point out one thing that I'm not saying. I mean, this is not a generalized Sorry, I mean I'd like to rephrase that It's a generalized notion that Indians basically would get attracted to the handsome stipend that outreachy and Google Summer of code is giving them It's also the one of the pros that these Programs have is that they're very very flexible, which I absolutely love which I think you wouldn't get in any industry Sure industrial jobs have perks, but this is you get paid handsomely. There's just so many there are You get over 10 conferences you get to extend your internships you you have Regular talks with your mentors. It's it's wonderful, but somehow this is being abused What Coming people don't have the approved amount of leave like That would have been amazing material for this Exposing People pay so that they can be coarsen to get into these programs It's that kind of thing that there's like some context 10% or 12% they charge of your total stipend It's that bad and then even if the applicants these are just the applicants but in the end as I said only hardly 10 or 15 people get in because they are in it's ineligible To the terms and conditions that are set by the organizations mostly because of the limitations that are there like Attendance those attendance criteria like outreachy has 40 days compulsory leave 40 days gap should be there, but then that is not possible in many Universities and they won't even give you that I mean I I'll admit so outreachy happens like twice a year and They kind of changed their rule somewhere when I started applying last year And they said that only if you're an Indian or you're from the northern hemisphere You can apply only in the May in the summer round and not in the December round And that just changed when I decided to apply in the December round I was so heartbroken. So I just tried to try to Manipulate my timing so that I could get selected, but it just didn't work out. So I Would also like to say it like so I was talking about abusing these all these good noble Initiatives that people thought would help develop in countries. So what's happening is like? We have so we have so many scholarships for girls that that promote open-source contributions or just normal or you know participation in global coding competitions and what the perks are of these is that you get to attend Conferences and the most the most basic Example of this is a Google conference and Anita Borg or Grace Hopper conference But to get eligible to get to that conference You have to clear the coding round, which is a global is on a global platform and efforts really competitive. So I have personally known so many people girls who have made Boys who are amazing coders do write these tests because these are all Online so no one knows who's writing these tests. They clear they get amazing ranks and they go there for free It's it's just past the point where I do not know how much diversity Outreach are you doing and and people have no idea how to? Distinguish where the diversity part is coming in West and with the abusing part coming in like they have no way of distinguishing or preventing that and I think that's something that needs to be worked upon You get a free tour, that's it There are yeah, so like what's happening over here. There are open source organizations I don't know if I should really name them, but there is a lot of Nepotism They are and these these come from people who are on really high posts and these organizations aren't some small open source Really right and they what they do is that so I feel that if you're a GSOC intern in one organization So here's the here's the basic line in India The users saw those statistics to so many contributors that means there's so much competition in India in spite of all the limitations So we could basically break through all that to get in now. We have these organizations that that Offer previous GSOC interns to work on the same project again Even though they haven't contributed for over a year and then there are these new GSOC developers who are trying to get into The program, but they just do not know what's happening behind the curtain So it just got harder to hardest to just get in so what should be done is that a People employers just start recognizing OSS contributions. I know red hat We were talking and then I know the red hat does Recognize right so they recognize OSS contributions if they're listed on the resume in India It has started to happen, but you would see this culture in startups. So that's really prevalent They look for open source contributions. They value it Basically whatever we talked about Now that needs to be done. We need extra credits for our project The point is we won't choose to name it now, but just no one does anything about it because maybe they're scared Maybe they don't care. I don't know But it's really affecting a huge population of Contribute new contributors. I'd like to say, yeah And even university should begin to support students now because I mean it's hurting students in the end because even though like there are like how much 100k some graduates of engineering every year The that graduate students, but then the problem is hardly one percent of them get employed And that's because they lack skills and the only reason they lack actual skills are they don't get to Experience what actual software engineering is before in college itself. So no one is going to hire them Without telling the college administration No that's also me Yes And then these are the Like just started this one just started to contribute on get a brand only I mean most people do begin Contributions, but once they realize the time limitations and everything that comes in they stop That's okay, but if you are at the start itself a grading students based on their mark sheet and Not even letting them in for the interview However, good the others like actual softening skills may be not just Google like every other company in India then I mean At least Google is now supporting. I mean Google now supports it Google does not check if you directly do When you directly apply they don't ask for your market that much and they look after all your other skills But all other companies I mean they just don't if you want to reach out to us and back 23 on get up and see my Twitter