Tresous, welcome.  No, of course not. So all yours, all yours. Thank you very much, hello everybody. Thank you very much for that very nice introduction and thanks very much to the organization for inviting me to be here with all of you today. Sorry, a little bit about myself very quickly. So I'm Jesús Montes and I'm a data scientist and I've been a data scientist for the last roughly 15 years and I've been working in and out of academia during that time during different projects. Nowadays, I am a professor at UPM at the Technical University of Madrid and I'm also, as Elena has said, I am the principal data scientist at Cabify. So what is Cabify? For those of you who don't know what Cabify is, Cabify was born approximately 10 years ago as a right-hailing company, as a Spanish right-hailing company. Nowadays, it's a multi-mobility company that has more than 33 million users worldwide, more than 400,000 drivers moving people all around the world. 65,000 companies operate with us every day and we have staff of roughly 900 people, which 50% of those are women, which is always nice, of course. So as I said, Cabify operates worldwide. Specifically, we operate in eight countries around the world in Spain and Latin America and in more than 40 cities, which means that we have a very terogeneous marketplace with different regions, different areas, different cities of different sizes, different types of cars, clients, etc. So it's a very complex environment. But let's start talking about a little bit about Cabify and what Cabify is from the point of view of our users, the people who use our application. So the first type of user, the most common one, probably most of you who have used our right-hailing company are part of this group, are the passengers, right? The people who want to find a car that takes them from one point to another, right? So from the point of view of the passenger, Cabify is an app that lets you find a driver to take you to your destination. And your main objective is to get there quickly, so you want the driver to be where you are as soon as possible and to take you to your destination quickly and efficiently. And of course, there's always the bonus thing of having a good price, right? If the fare that is supplied to your trip is a good one, it's not very expensive, then that's a better scenario, right? On the other side, we have the other type of users that we have, which are the drivers, right? These are workers that are driving around cities with their cars and they're looking for passengers who want to use their services, right? And some of their objectives are very similar to the ones the driver has in the sense that they want to locate people who want to move around, right? Passengers and they want to locate people that are closer to them, so they don't have to drive long distances to pick up their passengers, right? But from the point of view of the price, of course the objective is the opposite, right? These are workers that are trying to make a profit, right? So they want good journeys, good drives, which tend to be more expensive the better, right? So these are the two main users that we have and Cabify is like in the middle of those, right? So what do we see? We see a marketplace, we see a complex marketplace with a lot of passengers, a lot of drivers, you know, looking for each other at the same time. And the question that we try to answer here is can we make everybody happy, right? Can we find a way to assign the best driver for each passenger looking for a ride, you know, so they operate? And as I said, there are like two forces here at play and some of the objectives of these two forces are similar, right? They both want to look for someone from the other group that is close to them, but from the other point of view of the price, the objective is different, right? So we have to find an equilibrium and we have to find a way to, you know, get the best situation for all of them, right? This is what we call matching, right? Finding a match between passengers and drivers. And finding a right match is hard, right? It's not something that you can do easily, right? From a mathematical point of view, this is what is called an assignment problem, which is if you have two sets of elements and you have to create what is called a bipartite graph, which is assigning each one of them to the other and finding the optimal assignment for this, right? And this is based on what is called a cost function, which is the number, a way to measure, you know, how good a specific assignment between, in this case, a passenger and a driver is, right? And of course, this is a problem that cannot be solved by brute force, you know, actually there's like factorial and factorial combinations of this problem. So we have to find smarter ways to do this, right? And well, depending on the technology we use and the techniques we use, the problem would be different. But at the core of everything, it's always what I just mentioned, the cost function, right? How do we measure how good a specific assignment is? So we can compare them and decide which one is the best one, right? So we do that based on the data that we have, right? And what kind of data we have? What kind of data a company like Cavify has when we're trying to make this assignment, make this matching, right? So typically we do this like in two stages. The first stage what we try to do is find select valuable candidates. Let's say we have a passenger that is looking for a driver to take him or her somewhere. We have to find which are, you know, feasible candidates for that passenger. And that means looking at the data that we have and trying to make a decision, right? And we can use many different pieces of information to, you know, to use that. One of the most important ones is how far the car is from the passenger. Meaning how long it will take for that car to drive where the passenger is, right? That's what is called the ETA, the estimated time of arrival, right? Once we've done this first filtering of candidates, we have to pick the best one. And again, we have to look at the data that we have. And again, in a scenario like this, we can use many different things. You know, how much time the passenger has been waiting and things like that. But again, one of the most important things, if not the most important one, is how long it would take for a specific car to get to where the passenger is waiting, right? So again, the ETA is the key factor here. So we have to find a way to calculate this ETA. And of course, this is something that we cannot know, you know, in advance because the car hasn't moved, so we cannot time the car and see how long it takes. So we have to predict that. So that's why it's called an estimated time of arrival, right? So how can we do this estimation? Okay, so before we start thinking about ways of doing this estimation, we have to give some, you know, set some boundings to our problem and understand the kind of things that we require from this, right? And the most important thing here is that this is not something that we do, you know, sometimes we're doing this type of calculation a lot of times, right? In a typical day of operation, Cavify makes it more than 150 million of these estimations, you know, worldwide. So we need something that, first of all, gives us an estimation very fast because if we don't have the answer fast, we won't be able to, you know, do the correct matching very fast. Second, that estimation needs to be accurate. We have to have an estimation that it's a good estimation of what is going to happen, right? If the car is going to take two minutes and we say 60 minutes or we say five seconds, that's not acceptable, right? So we have to find a good estimation for that. And thirdly, we have to do it in a reliable way, right? We have to make sure that the service that calculates this estimation is working all the time and, you know, it doesn't crash, you know, because if we lose this service, we lose the capacity to do the matching, right, to do the estimation. So with all that in mind, there are like three alternatives that we can have when we try to do this estimation. The first one, it's apply a simple solution, right? So take something like a linear distance or a haversine distance, something that is really, really easy to calculate. We could use that and see what happens, right? So that would be our first alternative. A second alternative would be to rely on an external provider. There are many companies around the world who provide these kind of services, people like Google Maps are here, and they provide this as a service and you can hire that service and you can, you know, send requests to those services and get the result, right? So that's an option as well. And there's the third option, which is build an ETA estimator, an ETA predictor ourselves, right? If we have the technology and we have the knowledge, we can create our own ETA calculator, right, instead of relying on another one, right? So let's consider the pros and cons of each one of these alternatives. If you think about the first one, you know, you're talking about a simple solution. So the main pro of this is that this is very easy to do and very easy to maintain, right? If we think just from, simply from a technological point of view, it's going to be a very small piece of code, right? So it's something that I can write and it's easy to maintain and it's very, very difficult for it to crash if we, you know, deploy it correctly. So those are all good things, right? Of course, in the other hand, we have the problem of how reliable, sorry, how accurate our estimation is. So of course, if we are relying on a simplified solution, the estimation is not going to be very good. But, you know, we have the pros that, as I said, we have to consider as well. The second alternative, you know, focusing on a server provider, in this case, well, we have all the benefits of all the, you know, the accuracy and the reliability of relying on a service that has been tested and has been backed by a big company like Google or something like that. So we have to, we can't rely on all that, right? So we know it's going to be reasonably accurate. We know it's not going to crash, et cetera, et cetera. But on the other hand, the problem that we're having by, you know, using this alternative is that we are, basically we're relying on an external provider to provide one of our core functionalities, right? At the core of what we do is assigning passengers to drivers, right? So if we rely on an external provider to use that, to do that, we generate a dependency, you know, and that dependency could become a vendor lock. So, and that, you know, limits our options, right? So that's a big problem that we could have, right? The third alternative is, you know, to develop our own solution. And developing our solution is good because we control the technology, and once we have the technology running, you know, the costs are much lower, right? When we have an external provider, we not only have to hire the services to provide it, but we have to pay a monthly bill, you know? Once, if we have our own technology, we don't have to, you know, we don't have that problem, right? On the other hand, the big question here is, can we do it? You know, it's actually possible to, you know, to actually create something like this and, you know, and have it running at a similar level of performance that we would have if we rely on a good external provider, right? So that's the challenge that we faced here, right? Can we make our own service for calculating ATAs? And can we beat the best commercial provider that we have with it, right? And that's, of course, that's not an easy question to answer. And this is what we tried, and we've been trying, you know, for a couple of years now. So with this challenge, you know, in our heads, we developed what is called copy maps, which is our own ETA estimator. Copy maps is an artificial neural network.  And it does all the data processing, it does all the prediction, and it gives me the time in seconds. You know, how long in seconds my driver is going to take to drive from point A to point B. So, and we want to do that with a couple of additional restrictions. First of all, we want to use exclusively our own data, right? We don't want to rely on additional external sources to do this, right? And there are several reasons for that, for this, sorry. The first one is that we want to, you know, we don't want to be dependent on any external source, so we want to do it with what we have, right? And actually we are in a very particular situation where we are one of the few companies in the world that we can do such a thing because we have the data, right? Because we monitor the position of our cars every moment, right? Because we have to know where the drivers are so we can assign journeys to them, we can assign passengers to them. So we have the data, we know, we can see in real time how our drivers are moving around the cities, around more than 40 cities around the world. So we have the data, so the question is, can we use that to create the model, right? And of course, this is something that is going to evolve, it's not a model that is going to do it once deployed and, you know, and just, you know, be happy. You have to retrain, you have to re-adapt, fine-tune, et cetera. So it has to be a piece of technology that can be, you know, adapted and improved over time. So how did we build CabinMap? So we started in, you know, late 2018 as a sort of a hackathon kind of project, you know, in a couple of days, a couple of guys from the Data Science Department, they very quickly, they hack a, you know, a proof concept of this, and it showed, you know, a lot of promise, so we decided to, you know, keep working on it and improve it, right? And from that original, you know, hacked version of it to nowadays, we've been adding lots and lots of features to CabinMap and making it better every month, right? So let's talk about these steps. The first, the first step that we did was a very basic attempt. Basically, we just took the coordinates, the origin and destination and the date, and a couple of really easy, you know, values to calculate, like the, you know, Haverson and Manhattan distances between points, et cetera, and we fed that into our machine learning model to our deep learning network, and we trained the network and we tested it, right? And the results were not good, of course, right? And this is probably not surprised to any of you. Specifically, we got, depending on the city, we got, you know, an error, an absolute error that was between 150 and 250 seconds, which is like two minutes and a half to a little bit more than four minutes, right? Which is way above what are the, you know, the performance that commercial providers give nowadays, which is around two minutes, around 120 seconds of error, right? So, of course, this wasn't the final result. This was just the first step. And it serves as a way to set the baseline of the kind of data sources that we needed, the kind of data processes that we have to build and how to create the framework to create the model. And, you know, using this as a foundation, we start adding layers and adding functionality to it to make it grow. Sorry. So, how did we make it grow? So, it is very obvious if you think about it that, of course, this model is not going to work well because it's so simple, right? And there's the most important thing that we know is that location matters. And what do I mean by location matters? What I mean is that where a car is located right now when moving, it's really important, right? It's not the same thing being on a, you know, on a busy neighborhood and rush hour and it's also raining and things like that, right? Like being in the middle of the countryside, right? So, where the car is, at what time it is and what are the geographical characteristics of the surroundings of that car, it's very important, right? And it has a lot of impact, right? So, if we had only one city or a very small region, we could do this by hand, right? We are made each one of the streets, you know, and optimize our problems so we can adapt to that, right? But, of course, that doesn't scale and we cannot, you know, pretend to do that for 40 plus cities around the world and have it updated and involved over time. So, we have to find ways to make the fact that location matters, you know, present in our model in a scalable way. So, the idea was to try to find a way to provide some geographic context, provide information about the geography, the place where the cars are located so the model can use that information to do a better prediction. And when considering how to provide this geographical context, we have three things that we have to consider, right? The first of all is the kind of information, the geographic information that we want to include in our model. So, of course, there are things like traffic or weather or things like that that are very obvious that could, you know, beneficial for the model, but we have to understand that we are working on a scenario where we have to do hundreds of millions of estimations per day. We have a service that cannot crash at any time because if it crashes, our core business crashes. So, you know, having this kind of information means that we have to add external sources of information to our system. And also, these sources of information have to be, you know, comparable between cities. So, if we have a service for providing traffic information in Madrid has to be, you know, similarly as good as one for Buenos Aires or for Mexico D.F. So, and that's not an easy task, you know, and again, with the weather or with any other source, right? But on the other hand, you know, you could argue that we already have kind of that information already present because we know how our drivers are behaving. So, we can infer the conditions in where they are because of how they drive, right? If they take, if it takes too long for them to, you know, to cover a certain distance, maybe that's because there's something, you know, making them slow, like there's, you know, a lot of traffic and there's a heavy weather or something like that. So, we have that information there. So, the question would be how can we use the knowledge that we already have in the data that we have to infer that information. The second thing that we have to consider is how do we aggregate the information?  But that's not relevant to the car right? So, that's an element that is very, very, very, you know, dependent on where I'm located, right? But if I am in a residential neighborhood, for instance, there are certain aspects of how people drive around a residential neighborhood that are going to be constant around the neighborhood, right? So, there are different levels of scale that could apply here for different sources of information, right? So, we have to find a way to aggregate the information, you know, in an intelligent way depending on the kind of information that went to incorporate in the model. And the third one is how do we detect what's relevant in each case? And what I mean by this is that depending on the scenario, depending on the city, depending on the moment, what is relevant is different, right? So, for a particular moment being close to a sports center could be irrelevant, like for instance, if nothing is happening in the sports center but in a different time, it could be relevant, right? And these kind of things are not easy to do at scale, right? So, knowing what's relevant and what's not and what geographic information is important in each case is not an easy task, right? So, we had these things, these three things in mind and we tried to define ways or to create ways to incorporate all these in our model, right? And we did it by using two things that I'm going to tell you about right now. So, the first of all is we move from a pure, a very basic model based on coordinates to a model based on index, especially index cells. This is, especially indexes, it's a way of organizing the geographical space by dividing the let's say the surface, the entire surface of the earth we can divide that in cells which would be like hexagons or squares depending on the cell, the index that you use. And you could control the size of that cell and depending on that you could aggregate the information inside the cells. So, yeah, well, you could have different levels of resolution, let's say, right? And we used, you know, you see in the slide two examples of using H3 and S2, which are two alternatives for doing these that we've been using over the years. So, once we have cells, the next thing that we want to do is we want to represent the information and to determine what's relevant. And for that, we use what are called cell embeddings. So, embeddings, for those of you who are not familiar with the term, an embedding, it's a tool that is typically used in machine learning models when you have a categorical variable and you want to, you know, incorporate the semantics of that categorical variable in the model, you can use that, you can use embeddings for that. And to explain what the embedding is, you can see the example I'm showing you in the graph, which is the classical example of calculating the embeddings of words. So, in this case, we have the English language, so my variable would be all the words in the English language. And what I want to do is I want to map the meaning of those words, the semantics of the words in a set of coordinates in a multi-dimensional space, so that the positions of those words in that space are somehow representative of the meaning of the word.  So, having this kind of mapping between semantics and special coordinates. So, how can we do this with cells, right, especially in Excel, because we don't have words here, we have cells of a city, like tiles of the world. So, there are different things, different ways of doing this that we have explored over the years. The two main ones you can see here, the first one on the left side of the slide is the first approach we used, which is based on the word-to-back algorithm. So, the word-to-back algorithm is one of the classical algorithms that you use for generating word embeddings, and it works by analyzing sentences in a specific language, analyzing the words in that language, in those sentences, analyzing the position of the word, and trying to learn what the word means by seeing the relationship with that word with the rest of the words in the sentence, right. But we don't have words here, we have cells and it's a completely different scenario, right. Okay, but we have cars that are moving around the city, so we have paths, right. And it follows a certain path, so we can use that path as if it was a sentence, and the sequence of cells that we have as the words in a sentence. So, we turn the paths of our cars into sentences, so to speak, that are sequences of cells, of cell identifiers, and we train word-to-back using that. And the result is that we get embeddings for those cells, and those embeddings, they, of course, they don't contain the meaning but these are not words, they contain information about the structure of the city, how the cars move through the city, what streets are faster, what streets are lower, what kind of routes are preferred by the drivers, the direction of the streets, all those things are implicit in those embeddings, even though we haven't provided the model with information about those, but we just, the model looked at how the drivers move around the city and it returned the embeddings, right. The second alternative is using what is called an embedding layer on a deep learning network. An embedding layer is a specialized layer that you can put on a deep learning network model and what the embedding layer does is it trains an embedding using the output, the final output of the model, so in this case, instead of training based on the sequence of the paths, the sequence of IDs of cells, what we do is we train the embeddings in terms of the final output of our model, which is the ETA. So, in this case, we get a model that learns, sorry, that learns about the relationship between the different cells in the city in regard to the kind of trips that are done from those places, how long those trips take, etc. You can see here two examples of this second alternative I just explained. These are two images of the city of Madrid and what you see, colored in yellow is the target square, the cell that we are analyzing and the rest of the cells are colored depending on how similar they are. If the color is darker, it means it's closer to that. And if you see the image on the left, you can see that just by looking at how people drive around those cells, the model has been able to learn the structure of the city. Specifically, for those of you who know the city of Madrid, there's a very big avenue called Castellana which crosses the city from north to south and it acts like a geographical barrier and divides the city in two different areas with different behavior. So you can see on the image on the left that the model has learned that. So the cells that are similar to the ones that we selected are all in that side of the city and not many of them on the other side. In the second image that you see on the right side of the slide, I am picking the Portal Sol which is like the historical center of the city and what you see here is that the similar cells, the similar area of the city according to how our drivers move is the rest of the old town of Madrid which are streets that are very narrow and most of them are one direction only and it's generally very difficult to drive around that. So without explicitly telling our model anything about the city of Madrid, it has already learned the structure of our neighborhoods and how people drive around them. This is what we use as the input to our model. So once we have that and we have all the other things that I've mentioned, what we do is we train our model, we create we don't have one single model, we have one model per city which makes sense because what happens in Buenos Aires is probably not relevant to what's happening in Madrid and vice versa. So we have one model per city and we select our data intelligence so we can create a good model and we add all the typical improvements that you typically do when you do an advanced deep learning algorithm, sorry, deep learning model and using that we train our model and we have all these models to predict the ETA. So how good are these models, right? So that would be the question. So here you see some results of our analysis. So these numbers are actually taking for the last retrain of the model we did which was only a couple of weeks ago and the image you see on the center is the mean absolute error of the model per city and as you can see more of our basically all but one of our cities are below 150 seconds, around 120 seconds which is the objective that we had and on the right side you see the comparison between our model and the best commercial provider that we are using nowadays and the good news is that 60% in 60% of our cities our results are similar or better to the commercial provider that we were using. So it's actually a very good thing to use this kind of model because in most scenarios we can have results that are equivalent to the commercial provider or even better and the average error that we have as I said is two minutes which is around the same number that we would have if we would be using a commercial provider. After we have the model, of course we have to deploy and everything else so the end is not just doing the model, we have to monitor its performance, retrain it periodically etc. So for these we take advantage of all the state of the art technologies, TensorFlow etc to export the model and deploy it quickly and we have a lot of monitoring tools and performance metrics so we can see how our models are behaving and detect when things are not happening as we hoped so we can retrain, re-adapt etc. So just some final remarks about what I've been telling you about so as you can probably see this is a very complex problem and we have been able to tackle it successfully just both from the point of view of the model itself and the machine learning part but also from the technological part so we can serve this ETA in time and with the reliability that we need we have found a way to incorporate something that was obvious that we needed which is location information but not so obvious how to do this in a way that is also scalable and we can control our model. We have managed to do all this relying only on our data which is great because it breaks a lot of dependencies with external sources and finally we have significantly reduced the costs that we have in using an ETA predictor. I'm talking about several millions of euros a year without compromising the quality of the services that we provide to our users. So, this is it. Thank you very much and if you have any questions. Thank you so much Jesus! Round of applause for Jesus! That was fantastic!  You're such a fast speaker!  Jesus! Well, first of all, I love when you started saying if anybody doesn't know, let me explain what a cabify is. I mean, seriously, do we have to explain this? Have you been living in a cave for the past 10 years or so? Come on, very humble. I'm one of the best clients of cabify. Half of that, every time I go into a cab, I say this car is half mine because I'm paying it from the very beginning. Let me, well, we have some questions and we have some a few minutes. They're asking you, very interesting. This model to me, is it too good to be true? Is it more reliable?  More reliable, faster, cheaper. Are there any flaws? We've been working for almost 4 years on it, so it's not just a few luck. It was just a tissue. They're asking you, if you use DL to create the ETA, how do you get the route to display in the app? Because the DL will not draw a route through the road network. Ok, that's a very good question, yes. So, exactly, we don't a cadmium app doesn't calculate the route so you don't, the thing that you see on the screen is not calculated by that. We have a different model for doing that. So the reason for that is that drawing a route in the screen of your app is something that you do once per journey, once per driver. Once you hire a driver in the app, you get a route. But when you're trying to do the assignment, trying to do the matching, you have to do this calculation for many, many cars. So the problem is different and the restrictions and the needs of the problem are different. So you can use one service for just drawing the route and use another service with a different level of requirements for doing all these very, very fast calculations internally. You have so much fun at work, aren't you? Great, there's another question. How do you apply the relevance of newer data in your model instead of just using historical data to train? Do you use a LTVM and, my god, too complicated? Ok, so yeah, that's a very good question. So the problem is that we have experimented with that but it's actually very complicated because our marketplace changes every day. Events happen in cities around the world every day and we have different players that are appearing in the marketplace from one way to another. So it's actually very, very difficult to anticipate. So our approach so far has been basically monitoring how our model is performing and be very quickly and updating our model when things change. So we can be as close to the current situation as possible. But of course there are scenarios that you cannot anticipate. Drink. Drink some water, please. He's going to die here today. Come on, drink. Take it easy. We still have a couple of minutes. Ok, you were saying? No, that's it. Ok. Well, I'm just checking. We have one more minute. You said this model is obviously improving. It's changing by the day. This has no end or eventually. And what about the competition? Well, we cannot know what the competition is doing. But from what they publish we understand that we are all different players that we work in this industry. We have the similar the same kind of problems. So we have our own solutions for them. So I guess probably some of the most immature players still rely on external providers to do this kind of service. But I'm just guessing here I cannot know what the competition is with. So does this model have other uses maybe eventually to be a provider even for the competition that it could be an alliance? I don't know. I'm just giving ideas business model. Actually, I can't say because of course there's always that possibility. But you don't know because it doesn't happen. Just in case you tell us something and nobody knows. Anyway, so Jesus Montes fascinating. Cavify always. Every time Cavify is in an event there's a lot of interest. So we'll stay tuned and congratulations on the model. Big round of applause for Jesus Montes our virtual we say goodbye to Jesus.


Hello there, it's Sandy Allnach. Welcome to my YouTube channel where today I will be doing some stamping. These are new images from Colorado Craft Company for their new release. And I decided to go tropical. And I made some Tiffany stained glass kind of images. There's three different techniques here. And you can do these in all different mediums. I'm gonna be talking about the design techniques here that I employed and we will get going on that. But first I wanted to let you know about something island related as well. Since I had something tropical in today's video, a fundraiser that is currently going on for Hawaii. It's a color wheel image. You can download in color. And all the proceeds of your $5 purchase go to help people in disastrous situations. Tiffany glass is a kind of, I guess, stained glass. It's made with a different kind of glass, has a little opalescence to it. But what I decided was to call it Tiffany glass because I think it's a pretty name. And this first technique is pre-drawing the lines that you're gonna use for your design. So I made a grid and then I started making lines on that, connecting different corners so that I could create a pattern. And then I did my stamping over it. Because I'm gonna be working in gouache paints on this vintage watercolor paper. Now this is terrible for watercolor. But when I saw it on Amazon, I was like, I gotta try this. I gotta see what this is like. And it stinks for watercolor, but it's beautiful for gouache because the color can sit up on the surface quite nicely. So I'm painting it with the gouache. It's an opaque watercolor. And in my previous Colorado Craft Company video, I actually did some gouache as well. So I'll link you to that in the doobly-do down below if you wanna go see that one as well. But this one, I am just painting the colors so that they get lighter and lighter toward the top. Now the lines are disappearing from the stamp. So I'm guessing at roughly where things are, not really too worried about it, but I'm just gonna paint one petal at a time so I see its detail as I go. But you can keep re-stamping. So if you've got this far and you got confused as to where things are, you could just stamp it again. I just kept going, because I knew I was gonna do one big stamping at the end instead of stamping multiple times. And you can see in the previous video why sometimes I had stamped in between and sometimes I don't. So here's the re-stamping of the ink. This is VersaFine Onyx Black Ink. And all the lines came right back. So you can go in and paint more details on top of that. I'll add a little bit more of a goldy yellow in there later. But for now I'm just gonna get busy painting the background. I used the colors that were in the flower around the border. And then inside I used a complementary color for all of the background shapes. And so each one of those is in various kinds of yellows mixed with various amounts of white. And then I used a pen to go in and make all the lines, just outlined everything. Now I had tried this by doing the outlines first and that was a bad move. So don't do that. Do the lines afterward. Now technique number two is to draw lines for yourself and then ignore them, which is what I did here because I didn't like what I drew. I wanted to have like another plant kind of winding up behind this bird of paradise. And I wanted to kind of have it be all flowy. But the lines that I had drawn I wasn't really thrilled with. So I began by using a lot of warm colors first in the flower. Just kind of mushing around some reds and yellows and stuff in that one petal, making it with a little bit of purples in it. And then I just ignored all the lines that were behind there because I'm working in gouache. So I can paint right over them and I'll just disappear. If you're working in another medium you can easily do the same thing. You can do it with your Copic markers, you can do it with color pencils, whatever kind of markers you're using, just make clear shapes that you're gonna be able to outline. So I'm filling in all the in between shapes with a dark color in between all these. That dark color is what's gonna help to make everything pop. And what I'm using here for the background is all cool colors, blues and greens. And then you see I'm using warm colors around the outside edge. And that also ties back into the flower. So I'm doing kind of the same thing color-wise as I did on the last one. Just using the same color for the flower as the border. And then something contrasting so that the flower is the thing that really pops to the front. You can decide exactly how many lines you want in everything. So I started by outlining mine and then went into some more detail in the border, made some more horizontal and vertical lines and added more lines into each of those stems as well. Because that started pushing all that to the background and just the flower just kept coming forward as there were more and more lines in the background. So the third technique is just to skip the drawing of the graphics entirely. Because once I did the second one, I was like, okay, fine. I'm just gonna do it on the fly and see what happens and see how hard or easy this is. This is a Columbine and Columbines can have purple flowers or bluish flowers or pinkish flowers, any kind of flowers. So I made these pink. Just so I have something different than the other two.  I don't have to worry about going outside the lines because I can fix those with a pen later. And that center portion is gonna have some yellow in it, but I'm just gonna leave the red for now. And after the stamping is done, I can add some yellow centers on all those little bits and pieces in the center of the flower at the very end. But I'm gonna dry it really good because I think I forgot to mention that.  Stamp it and you're ready to go with the background. What I decided I wanted was kind of blue greens, greens, those kinds of colors in the background. I wanted them pale. So I mixed them with white. So they're very pastel kinds of colors. Once I got the blue greens done, I added some more green to my puddle of paint and started painting green leaves. And then I added yellow to that so I could get another color and then I added some dark green to that. Just kept going until I filled in the whole area with nice big, big, big shapes. And then the outside edge, instead of doing a fussy border, the fussiness comes from the pen and ink work and the color was just a solid reddish color. The shapes then I outlined all the leaves and then drew lines in them to just divide it into these little glass panels. And this one was far easier than the other two. Oh my goodness, especially that first one, the first one about like blew my mind. But I think they're all pretty anyway and they're gonna be gorgeous in floaty glass frames, I think, because I love the soft torn edge of the paper. There's just a lot I love about these. So check out the rest of the blog hop. There's lots going on, links in the doobly-doo to mine. And don't forget if you want to get the fundraiser image, you can pop over there and get that on my site right now before you forget. And I will see you guys again in my next video. I put videos out usually on Tuesdays and Saturdays. See you then.


Well, thanks a lot Bob what an entertaining game here today and what an entertaining finish David Let's go to that ninth in I mean that it that reminded me a lot of that Andrew Miller Troy to the witski a bat here a few years ago that seemed like it would never end Miller won that battle Chapman won this battle today. What a show in the ninth Yeah, it really was and you could tell that Vladimir Guerrero junior came off the bench ready to swing the first two pitches We're incredible. He swung as hard as he could Chapman threw it as hard as he could He got up 0-2 and then Guerrero junior really fought back all the way to a 3-2 count almost hit a home run foul And then on the 13 pitch a double-play ball. Well, that was just worth the price of admission and you know for the Yankees The headline is going to be obviously Masahiro Tanaka. What an outing from him and David at a time when he really needed it He did really need he was very sharp today. I think you could see it coming the improvement was there I thought a slider was really sharp excellent command of his fastball I think John Flaherty in the studio noticed that and mentioned it through through one of our cut ends But the splitter's getting there at times to he's still not quite all the way back with it You could see I mean it is dropping more He did get some ground balls with it today But it's not quite all the way back to where you get that swing and miss splitter that he's known for but Definitely a right in the right direction a car real confidence builder not only for him But for the team in the fan base and let's give some love to Brett Gardner He drives in the only run in this game and David He has been so productive this season in a year where he's probably playing a lot more than he or anybody really figured He would yeah, it is very true. I mean it's all the injuries as Yankee fans Obviously know at this point and Brett Gardner probably thought was he going to be more of a fourth outfielder Or you know, maybe get a lot of that bats but play all over the outfield and he's become an Integral part of this offense down the stretch run He's really gotten it going over the last six weeks or so and his numbers are well above average as power numbers are up I don't think anybody really saw this coming, you know from Brett Gardner But he's he's part of the heart and soul of that team and part of the leadership as well Well, the Yankees win it one nothing a lot of drama here in Toronto and hey get to play two games tomorrow Back in the Bronx as we send it back to you guys in the studio


Hello, my name is Sarah Kate Ellis. I am the president and CEO of GLAAD, which is one of the world's leading LGBTQI advocacy organizations. I am pleased to be your moderator today. A warm welcome to all those tuning in globally from around the world. What a wonderful day it is to have you here to talk about LGBTQI people in our community. I am beyond thrilled to share the stage with this esteemed panel who you will meet very shortly. As we sit and discuss LGBTQI issues in the forums job reset summit. So now we're gonna put up our engagement protocol. Most importantly to know that this is a 30 minute session. It's going to be live streamed. And then we will leave the live stream and go into a breakout meeting for forum members. Please, the most important thing on this list is to please make sure that you mute yourself. So that's great. Let's get started. This session will tackle how the LGBTQI community are disproportionately affected health-wise and economically by COVID-19. We wanna consider through this great reset how business leaders, civic society and government are going to be able to help the LGBTQI community reset. Now, we have these wonderful and insightful panelists. First, we have Alfonso Davis, the president of the Human Rights Campaign. We have Shannon Klinger, the chief legal officer of Novartis and Randall Tucker. Chief inclusion officer of MasterCard. So let's get started. Alfonso, we're gonna start with you. Is there an issue? Has the LGBTQI community been disproportionately affected by COVID-19? Is there a specific impact on our community? Can you talk a little bit about that and help us level set and set the stage for this discussion? Yes, indeed. Hello, everyone. Good morning, good afternoon, good evening, depending on where you are. It is my pleasure to be here today. To answer your question, yes, COVID-19 has had a dramatic impact on LGBTQ people, both in the United States and globally. At the onset of the pandemic, not too long ago, earlier this year, the Human Rights Campaign released a brief for policymakers in the media on the elevated risks facing LGBTQ people during the pandemic. We identified clear economic and health vulnerabilities that LGBTQ people face. For example, LGBTQ people are more likely to work in industries impacted by the pandemic, restaurants, hospitals, education, retail, and to be a little bit more specific, 40% or more of the LGBTQ population in the United States work in those industries. In addition, we determined that LGBTQ people and their families are more likely than the general population to live in poverty. What does that mean? One out of five LGBTQ people live in poverty and COVID-19 exacerbated this economic challenge. Also, LGBTQ people disproportionately lack access to adequate medical care, paid leave, and basic necessities. One out of five LGBTQ people have not seen a doctor in the United States because they could not afford one. And the community is also disproportionately suffering from chronic illnesses. So building on that research, the Human Rights Campaign partnered with PSB Insights to track the economic impact of COVID-19 and the LGBTQ community moving forward. And we use polling data from thousands of adults in the United States. And we've released monthly reports that really show that we're losing our jobs at a greater rate than our non-LGBTQ peers. Our work hours are being reduced more frequently. Our compensation is going down. And those who live at the intersection of multiply marginalized identities are even more impacted. So I'll just give you one last statistic. The Human Rights Campaign data analysis shows that a shocking 58%, you heard me, 58%, of transgender people of color have reported losing work hours. And 26% of transgender people of color have reported becoming unemployed during the pandemic. Now, while this data reflects the impact of COVID-19 on the LGBTQ community in the United States, I must note that this is a very limited body of work globally about the experience that COVID-19 is having on LGBTQ people. We have to compile more data and unfortunately advocacy organizations rather than government in most cases are in the driver's seat of really compiling this data for the community. That's fantastic. I mean, the information is fantastic. It's staggering the effect though. Thank you for doing that and for sharing that. I wanna turn, we're gonna come back to actually, how do we collect this information toward the end? Because I wanna have a conversation about that. Shannon, I wanna turn to you because Alfonso did mention there's global ramifications here, right? And Novartis is a multinational company. There are nearly 70 countries where LGBTQI people are still criminalized and countries and regions where cultural acceptance is very low. The private sector plays a huge role in protecting LGBTQI companies sort of taking the space of government, if you will, in these places. How can the private sector continue to support LGBTQI people in these places? And what challenges will you face as you approach, as you build back better? Yeah, so Sarah, thank you for the question and I too, just amazed by the statistics that Alfonso shared with us. You know, for Novartis, we operate in 26 of those countries where it's still criminal to be part of the LGBTQI community and that covers 7,600 of our associates. And so this is a question that we grapple with consistently and it is made more relevant because of what we've seen in the pandemic, the isolation and community that already felt isolated but even further isolated now is as people have been working from home. Because the first thing we've tried to do in those countries in particular where it's not accepted or even illegal to be part of this community is to become an embassy company. That is within the four walls of the Novartis campus and the countries in which we operate, we want everybody to be able to come and be their best and true selves and to promote LGBTQI equity internally in a way that people feel safe. In the pandemic, that's translated internally to things like continuing our work to have gender-neutral policies and policies that make very clear that they don't discriminate on the basis of gender or sexual orientation. We announced last year parental leave, 14 weeks of parental leave for both parents at the birth of a child and we're adapting that in all of the countries to allow parents however that term is defined to take advantage of our leave policies. We've created discussion groups to really reach out to the LGBTQI community internally through our employer resource groups as well as appointed senior sponsors to lean in even more heavily. But as much as we can do internally, that doesn't change the reality that people face when they step foot outside of the campus of Novartis. And so while complicated and while progress is slow going, partnering with NGOs, with governments, riding a new appreciation for human rights and equity around the world broadly defined, we're working to shape those ecosystems where we operate slowly but surely to create an environment we hope in the future where we're not answering this question but we're talking more broadly about what have we done to accomplish even more equity going forward. That's fantastic. Randall, I'd like to turn to you. So given these disparities and given embassy corporations, which I love that term, I think that's really notable, given the disparities of the LGBTQI community is facing economically during COVID-19, how can private sector play a role in building back specific to our community? How do you take into account the disparities that Alfonso laid out in building back your workforce and building back your company and thinking of new ways to do business? So thanks for that. And I'm really happy to be part of such an esteemed panel. Alfonso mentioned that the weight is heavier for those that are in the margins. And the LGBT plus community is a part of that community. And it's just not that community. It's part of my community as well. And what we're looking at many organizations in the absence of society and the chaos that's going on, a lot of people are looking to organizations to show up as that moral compass. And MasterCard, we're one of those organizations that we believe in creating a world beyond cash, but also doing well by doing good, which is our decency quotient. And part of being decent is creating a level playing field for people to work for our organization to be our suppliers as well as to buy our products and services. And so in the combination of all of those things, very similar to what's been said before over at Novartis, we believe that we can do more and that we can do better. At the end of the day, we're trying to make sure that we are the employer of choice, the brand of choice, as well as the community of partner of choice. And quite honestly, part of that is our 100% score on the CEI, so the HRC survey and making sure that we have that corporate index that kind of shows across the board that our commitment to inclusion and diversity isn't just a one siloed event, but it's across the way that we operate as an organization. The piece that I want to bring up outside of the parental leave is that we're really excited about the launch of a true name, which was an opportunity for us to lean in many different areas of the organization from senior leadership to our business resource groups and understanding and meeting the need of our customer by allowing them to put their chosen name on their card. So I know that's really hard for them to figure out a lot of the paperwork that they have to go through and what we saw was a business need because we want to see them as a viable customer of ours as well as we believe in doing the right thing. So that is, there's many different components. It's not just one thing, but in totality, it shows that our organization is trying to move forward in the right way. That's fantastic. And just to follow up on that, is that, was that what you launched with Citi? Did you partner with Citi on that? So Citi is our second. You brought it up. So can you talk a little bit about that? Yeah, yeah. So Citi is our second institution that we are partnering with and we're really excited. So it's fun that, you know, from our place of doing well by doing good, creating a product that makes sense to a certain community within the LGBTQ plus, which probably more transgender and non-binary community. And then having institutions putting that product out and saying, institutions, do you want to join us in our effort to make our lives easier for folks? And Citi has been a partner in which we're really excited about and give kudos to that team for picking up the banner and the baton. That's fantastic. Congratulations. So I want to go to Shannon again and Randall.  Alfonso brought this up earlier. How have you gone about measuring LGBTQ inclusion in your own workplaces around the world? And we'll balloon that into a larger discussion around being counted as the LGBTQI community. But I'd like to start with that narrow view on it if you could. Randall, you want to start? You want me to start? I just spoke a lot. I want you to start. I yield the floor to you for a second. I appreciate that. So, you know, sir, Kate, this is I think one of those topics where it always has a beginning and I'm not sure that it has an end in terms of how do we measure inclusion because there's so many different perspectives from which one can look at that. For us, we start, of course, with employee engagement and cutting the employee engagement surveys that we do every quarter across a wide range of demographics to see what we can know. But that only works if you understand the demographics that are present within your employee engagement survey. We spent a lot of time asking other companies, actually, how have you tackled the problem of self-identification to augment employee resource groups, to augment allies and advocates internally, to really help drive and build a community where people feel safe, physically and psychologically, to come in, come out and be part of what we're doing. And talking a lot with IBM, in fact, and how they ran a self-identification program around the world, that's where we're going to lean in in the second half of this year. Really trying to create a space where people broadly defined in the diversity spectrum, but certainly the LGBTQI community can come in and be counted. Because once we can count more concretely, we can do even more targeted listening and understand a little bit more what our data and our metrics are. But one of the simplest things that we're still doing in the absence of that self-identification being completed is just listening. During the pandemic, one of the unexpected gifts for me and for many of our senior leaders has been to zoom in to countries to have conversations with our LGBTQI ERGs, to ask them, how are you doing and how can we help? I think the isolation and loneliness that we all feel has been amplified in this community, particularly in countries where it's very difficult to be out other than when you're at work. And it's amazing the stories we've heard. It's caused us to bring those stories together and start retelling them inside Novartis to have a mother who's watched the journey of her son coming into his own as a transgender youth and listening to his courage and knowing how much more courage we all need to have that if a 12-year-old can sort that out and can stand up for what he lives in, how can we all do that also internally? We also had Gareth Thomas, who many know as a famous former rugby player who came and spoke at our Pride event where we had more than 3,000 associates all turning in virtually, interacting in our chats, highly engaged to hear him talk about the fact that coming out was not a burden, but as he put it so eloquently, when I came out, just think of how much faster I could run and how much higher I could jump without the burden of hiding on my shoulders. And I think that if I had one sentence or one theme for what COVID and the engagement at Novartis has been over the last six to seven months, that's it for me. And I think that's inspirational for all of us. That's amazing. Randall, do you wanna add to that? Yeah, what's in my head is that there are a number of opportunities where there would be women or the black community or even this community, the LGBTQ plus community is that we kind of take a step back and say the world was not created with that perspective involved. And so what we try to do at MasterCard is think about and how do we become, again, that employer of choice, that brand of choice and community partner of choice. And we kind of look at it in a few different ways from an employer side. That could be, do we have a business case for why someone from the community would actually want to work for our company? To when we're in talent review, are we talking about, do we have the LGBT talent that we need at all levels of the company? And is that next class of leaders, do they include this group? As well as from retention effort that has already been set from our engagement perspective. But we see it broader than just the people HR because it's, I think that when we look at an inclusive strategy, you also have to look at the branding as well as the philanthropic effort. So from a branding perspective, understanding what is your current market share for the community? What are the products that are needed just like true name that we put out there? Is there anything else that we're leaving on the table that we're not doing? And then from a philanthropic area, looking at, here are all the communities in which we operate and does LGBT show up within that? Are the organizations that we're partnering with LGBT friendly? And do they have a specific focus on helping that community advance just like everyone else? So it's the only thing I'll add to that, there's not just one component, but there are many things you have to think about because the LGBT person was not considered in many of the systems that currently exist. So we're trying to make sure that we're embedding it. That is a great segue for Alfonso to bring this one home, which is, throughout this pandemic, we have seen that data quantifying LGBTQ people who are infected just doesn't exist and is actually not prioritized. This is very common for our community. I always like to say, if you are not counted, you do not count. This has some pretty big implications globally. Alfonso, what can be done to push governments around the world who do better at counting up? The very big question, you're right. It is. I think I'll start with business. And both of my colleagues reference this, Human Rights Campaign has an assessment tool called the Corporate Equality Index. And we have more than 1,000 businesses to participate in that index. Why is that important? It's important because it creates inclusive workspaces, which then, by default, allows LGBTQ people to feel more comfortable coming out at work because they feel that they have an environment that would be supportive. And if we have businesses, which are incredibly important in terms of advancing public policy, supporting inclusive workspaces, participating in the Corporate Equality Index, we could potentially also inform our government functions. We need government to collect more data on LGBTQ people, to help create smart solutions for the obstacles that are faced by our community. So I'll talk about it both nationally, here in the United States and also globally. We need government to collect data, to understand the health disparities, especially on how this COVID-19 is affecting the LGBTQ community. The Human Rights Campaign, along with a few other public health advocates, sent a letter to the Trump administration asking them to provide us with data they have failed to do so. We also need data to track the fatal violence that we're seeing against members of the transgender community so that we can try to craft meaningful government solutions to that problem. With respect to the global issues, we also need to make sure that we have inclusive data collecting tools. I know that in the UK, they're starting to collect sexual orientation and gender identity data in their census, but we also need health and economic data to really support international development, philanthropic efforts. We need that data to really inform how we develop policies and how those policies directly affect LGBTQ people. So to make this more granular, I know in many countries there, they have the equivalent of what a census taking tool is. You need to know how many people are living in a specific jurisdiction. You need to know how many people are living in your country. Why? Because then you allocate resources based on where people are, but it also informs whether or not the specific populations have adequate representation in government. If we can advocate not only through business, but through advocacy organizations to have government really be inclusive in how they track us, because they're tracking all of us. Government is tracking us, but they're not tracking LGBTQ people. They're not tracking how illnesses, how certain disproportionate negative events impact LGBTQ people, and that's what we need. We need to make sure that we have the data that informs the policy, because the policy then directly affects our lives. I was gonna add something to that from the corporate perspective is, we have the systems in place. We've done the legal work of trying to figure out, what can we track, and what can we track around the world? But our employees in many different organizations that I've worked in, not just MasterCard, our employees are afraid. They're afraid to actually count themselves. And that's why it's so important to have leaders at the top that are of the community to voice themselves that they are part of the community.  And I think that's why it's so important that the work that we do at HRC, and Novartis, and at MasterCard, it's so critically important, especially at the top of the house, for their buy-in, is because lives are at stake. And as many opportunities that we have to count folks, the more resources and tools that we can give them in order to have a better life. And on that point, I speak with a lot of CEOs of Fortune 500 companies all the time. And one of their biggest challenges and their biggest question is how do I ask people to identify in countries where it is against the law, where they are endangered for their lives? And I'd love to turn to you, Shannon. I've made you the global guru. So I'll turn to you on that because I know it's a real issue on Novartis. I'm gonna talk to Bob's about it and tell me, how are you handling that? How do you approach that? Yeah, I would say with care and with consultation. What do I mean by that? With care, because it's one thing to build the relationship and I fully agree with Randall that it has to come from senior leaders who are part of the community, who are out and who make it psychologically safe for people to say, I see someone who's like me and I'm gonna take a step further into my own transparency. And it's another thing for them to believe that their government will respect any privacy rights that a company may have and collecting its own data. And so we do balance very carefully and talk to the community that's directly impacted about how they feel about collecting data and about self-identification. And we're considering other categories that are a little more nuanced that allow people to step into diversity without necessarily having to step into the particular slice that they would claim if they had complete freedom of choice and freedom of speech. So carefully, and that I think this is a stepwise process cautiously because we are not naive to believe that anything we collect, we can fully keep to ourselves and we owe our associates a duty of care, but doing so with a commitment. And I think this is really important. For us, when we signed up to the UN Standards of Business Conduct to combat discrimination in the LGBTQI community, it was a moment for Novartis as the first pharmaceutical company to step in, not just externally, but internally to put a big sign up there that says, we are turning the page and this is becoming incredibly important to us. As you know, Sarah Kate, it hasn't been a long journey for us. Our journey is two and a half years old. We need to do more. We need to continue to move forward. But those sorts of commitments that your associates can see you're holding yourself accountable internally and externally can make a tremendous difference in starting to create that trust and allowing people to self-identify and come in to the community. Sarah, Sarah Kate, if you wouldn't mind, I just want to piggyback on what Shannon just said. I grew up in Liberia, West Africa and where it is unfortunately against the law to be LGBTQI recently went to Ghana, which is a very, very different. Well, they still have challenges in Ghana, of course, but it's a little more accepting in certain circles to be LGBTQ where people are LGBTQ. They certainly face harassment and discrimination, but they're not necessarily being thrown into prison and they're not in most cases subject to death. And how businesses are operating in Ghana versus Liberia is very real as you think about nuance and how do you create policies that are very inclusive of LGBTQ people where they feel comfortable to come out and they're not exposed when they walk outside of the building. At some point after COVID-19, we will go back to offices, I suspect, but right now while people are working at home, in some cases they're even more exposed. If they're communicating in their home with family members who may not know that they're LGBTQ and they're on the Zoom call and it's disclosed, that could compromise their livelihood and also compromises their ability to actually remain safe. That's fascinating. So I am asking everybody, please put questions in the chat room. We have a few more minutes here, just a couple of more minutes. I'm gonna start with the commercial for governments and corporate leaders that you can't do this alone. Call myself an Alfonso. We partner with both of the people that you're talking to right now, Shannon and Randall. We partner with corporates, we partner with governments. We are here to help you through this. You don't do this alone. With that, I'm going to ask for a lightning rod round here of what is one action that people can take that you would recommend one or two, but we're tight on time and we're gonna move into the private session in about two minutes. And we'll start with you, Randall. Okay, so I think that many different people around the globe are at different points in the journey. And so I'm kind of a country boy from Virginia and gay, so all of that. So if you're crawling at this, the advice that I would give you is if you don't know how to treat someone, ask them because they will always tell you how they want to be treated.  And then if you're actually running, I would say that you should share your best practices with others. Beautiful, Shannon. I would say practice radical curiosity. And it builds on everything Randall just said, ask, listen and act and it will make a difference. Wonderful, and I'll find so. If you're a corporate head, please create an inclusive workspace. If you're an LGBTQ person, please look to see how you can get resources from the Human Rights Campaign from GLAAD, from other organizations to assist you. If you're anyone, please vote, vote for pro-quality candidates, because that is really important. We need government that actually will represent the interests of all of our communities. And that starts in many countries at the ballot box. So I would ask folks to really vote and exercise if they do have that constitutional right to vote, to vote. We have a small election going on here. Just a small one. I'd have heard about it. Thank you. This was fantastic.


We are the opening, I can't sit back because of my, I'm just gonna sit here, it's hopefully less awkward. We're the opening act for the former prime minister. Are you ready for that? Yeah, warm up, I'm the hype man, I get it, it's good. Well, I have to say any man that wears shorts and how sinky in November or December has the confidence required for this opening act. So I'm very happy to be here with you. I remember when I first joined Slack's board and Cal's co-founder, Stuart, and I were having a discussion and I said, you know the thing, I'm on Slack all day engaging with my colleagues all over the world and I really, what Slack gives me is a sense of belonging. And he says, that's nice, Sarah, that's not why we built it. He said, when Cal and I really built Slack, the idea behind it was transparency. You used to be working in an organization and you didn't have a sense of what decisions managers were making, why? If you weren't in management, you weren't in the room. You didn't really understand what decisions were being made and if you use Slack as an organization, you really get insight into the decisions that are being made, who's making them and why. And in that way, I think you've changed organizations more fundamentally than I think many other software products have. So today, your business communications software used by tens of millions of people around the world every day, but that was not the origin story. No. So today we're gonna talk about your pivot and I would love to start by talking about when you realized that maybe this game wasn't quite gonna scale. Yeah. So we started out doing something very different trying to make video games. We had had this vision for quite a few years of this game that we wanted to create. It was an online massively multiplayer game. You played it in the browser. It was gonna be collaborative and non-competitive and we'd had this vision. We're very excited about it. That's what we formed the company around and we spent four years and nearly all of our investors' money making a game that hundreds of people loved and that was a couple of orders of magnitude smaller than it needed to be to be a viable business. And the, I think when people think and hear about pivots that they assume that it's some kind of quick decision or obvious decision and I think there's definitely not the case with us. It was a period of probably a year, the fourth year of working on the game where we were in that zone where should we continue doing this? Should we stop? And I think there's kind of three states. A startup can be and there is obvious success where it's like, yes, we should definitely double down on this. It's working. What can we do to control the growth? What can we do to make it go faster? There is obvious failure. This isn't working. We need to try something new.  And you really believe that the next thing you do is gonna bend the curve and change the trajectory. And so we should keep trying new things and we just need to try for another week and just another month. And the next thing we do is really gonna make it work. We're in that situation for a long time, unfortunately. And we're having that conversation very regularly about is this really gonna work? Are we gonna be able to do something that will bend it upwards, bend that trajectory, change that conversion, make it fun because we're building games? And it took us a long time to come to that conclusion. And I think the real reason that it came, that we ended up deciding to stop working on the game was that we knew we were gonna run out of money and have to raise more money. And we're gonna have to raise more money without the proof of where we wanted to be and without that growth. And so we ultimately made the decision that we need to stop working on this, spin the company down, we weren't gonna realize our vision and our dream. And so when we did that, we knew that we wanted to keep working together. We were three co-founders and we loved working together. And we knew we wanted to work on something. But we had this period of knowing that the game wasn't gonna happen but that we wanted to keep working together. And so we had a period where we're thinking about ideas. And for a long time, in the years afterwards, we thought it was a period of many months where we went back and forth and brainstormed ideas and came up with tons of different ideas before finally deciding on which direction we were going. But because we have all of the slack logs from that time, we know exactly how long it was and it was about eight days. But it seemed like a really long time. We had all these grand visions. The number one idea for a really long time was we should start a bank because everybody hates their bank. We don't have enough of them, I don't think. Well, I still kind of believe this.  So it's a great business. Why doesn't everyone start a bank? But I think we decided that was a bit too difficult and instead looked at what we'd already built internally. So Slack really came from the tools that we had built to build the company, to be able to build the game, to collaborate across our offices while building the game. And how did you get alignment around? It sounds like there was some urgency in eight days around this being the idea. It's obviously, I think it's the first product I have to say that I actually loved and it really, I think raised the bar and expectations for what people, but how did you align? Okay, business communication, this is it. And then you're the CTO in addition to being a co-founder. What change was required? How did you get alignment on the investor side? You kept some of your investors through the journey. We kept all of our investors and I think actually when we pitched the change in direction to investors, we talked to our existing investors who had invested in the game and said, they knew the game, wasn't working out. We were sharing the metrics with them too. We said, would you like the money back? We have a little bit left. They're like, no, no, no, go try something new. Like we'll keep backing you, try something different. We went with this pitch for Slack, put together a pitch deck, which we still look back at now. And their reaction was, oh, this is great. It's enterprise software, we understand this.  You know, most investors back founders, right? They're back founders and teams and hopefully not if you have a terrible idea, but you back a team because at the beginning, you don't have anything else. You don't have any proof that this is really gonna work, that there's a real market there, that there's a real product there. You back the team. And so I think our investors were very happy when we made that change. But I think the reason that we thought it would make sense as a product and the direction to go in was that it's how we had been working. Now, our vision was that other small technical teams like us were a small company at the peak when building the game with 50 people. And we thought other small technical teams like us would benefit from working in this way. So it's less about the product and more about the process of how we work together. And you touched on the transparency piece. I think any kind of software that you use around productivity or really any kind of software that you use in the workplace tends to have some kind of encodes the philosophy of the people who built it. I think this is especially true of like task tracking software. So if you think about the different ways in which you work if you use JIRA versus Trello versus linear, right? They each encode a way of thinking about how you break down work and organize work. And I think to the extent that we encode some of our philosophy in Slack, it's transparency, right? Public channels by default and the kind of like opt in the pull versus push nature is that we think organizations work better when more information is shared with more people, right? So that kind of philosophy was at the heart of what we're building. And Slack did work out, I would say, now or for a period of time a public company and now again with tens of millions of users around the world. And how did you, and one of the things I like most about you as a person, Cal is your humility. And you always say like it's a combination of luck and the timing was right and there were a lot of great tailwinds, the rise of mobile, the consumerization of IT that helps Slack be for a period of time while when I was chasing it was the fastest growing software company ever at that time. And what worked? How did you capitalize on those tailwinds? You know, and how did you really get this product market fit that the game had not delivered? Yeah, I think it often seems in retrospect like we must have launched the product and had immediate product market fit. But it was actually the first kind of year in which we were developing it and doing our kind of private alpha and our first external beta was really painful. We built a product that worked, the initial product that we built worked really well for a team of exactly eight people who were us. And when we got it into the hands of our very first testers, we learned so much. And it really shaped the product very rapidly, especially over the first six months, really completely changed how we thought about how people would interact with it, how we would introduce people to it. You know, we came from this big disadvantage, if you like, which is that it's hard enough to get people to try things. You know, to convince somebody to try a product. To convince somebody to try a product when you've just failed at your previous product and we want that commitment from somebody like, oh, put your important data on here. We promise it's not gonna go away in six months and this is gonna be a real business. But also we have to convince you to convince everybody you work with to give it a try. So that was a real uphill struggle. And our first, you know, finding our very first test customers was convincing, going to our friends at their startups in the Bay Area and like day after day for weeks and trying to convince them to give it a go. And that there is a different way of working and somehow this will, you know, this will make you more productive or make you happier using software, which sounds kind of ludicrous. And so it was like a real slog to get people to give it a try. And then we learned so much from those people. And I think so much of what Slack became, even like right at the initial launch was shaped so heavily by people using it and giving us their honest feedback. When you go forward a little bit in time, I think a very kind of underappreciated kind of source of really important information in product development was maybe still is Twitter. I don't know if we've talked about this before. I don't think we have. So in the kind of before Twitter era, and it's Twitter very specifically, not really any other social media, in the before Twitter era, if you tried some product, software product, consumer product, whatever it is, and you thought it was good or you thought it was bad or there was something wrong with it, you might tell your friends if you have that kind of relationship with your friends and the topic came up. If it had to be so incredibly good or bad or life-changing that you would write to that company and give them your feelings, right? Like how often have you contacted a company to tell them how you feel about their products? Maybe never. But then Twitter came along and suddenly if you had a slight feeling about anything, you broadcast it to the world. And enough people were doing that when we launched, or even during our kind of beta period, we got so much kind of passive feedback coming back to us about things people liked, things they didn't like, things they were confused with, ways in which they were using the product. It's such a rich channel of real-time feedback at scale that it's like not necessarily a replacement for but definitely an amazing augment to kind of any kind of user experience research and we learnt so much from that and shaped the product around it. The best argument I've heard for X, I have to say. And another tailwind that I think is really, well it's actually, I'm processing what you said because I always think of community as one of Slack's great strengths. And I think of that as like getting actual people together around the world and I hadn't thought about the online part of the feedback that was so instrumental in the early days. In addition to I'm sure the great product feedback that you got from some of your investors. But I will abstain from doing any of that here. But one of the big tailwinds, I can't get off the stage without asking you about AI. And I do wonder one how you're using AI in the product development process today at Slack. And then I'm also curious how you might have built differently had chat GPT been available at the time when you founded the company? That's a great question. I don't know if everybody saw Benedict's talk earlier which was fantastic. I think there is still just we're in this great kind of like step change technology time where it's not exactly clear what most of the applications are gonna be. And it's pretty clear that a lot of things that kind of a being built right now have been built in the past year and not necessarily what the applications of that technology are gonna be over the next five. And the incumbents which we are now, look at the how do we use this to augment features that we have. So how can we augment Slack capabilities using large language models? And then the future version of that looks like some kind of, I don't know, maybe there's gonna be a substantial change in how we interact with technology. I think the, it has, you know, if you think about the really big changes in how we interact with technology in the workplace over the last 20 years, it was really, I mean, I think one that goes less talked about but was really impactful is probably switching from desktops to laptops and really changed people's relationship with when they had access to technology. And then of course the introduction of the smartphone into, and using smartphone in business is relatively recent, even more so recent than just the smartphone in general. At the time when we started Slack, which was only 11 years ago, people weren't really using smartphones for work outside of just sending emails. You know, applications, workplace applications didn't really exist on mobile. Which sounds crazy now, because it wasn't that long ago. But I think, you know, like does AI bring a new kind of level of interaction with technology? Maybe. I don't think conversational interface is probably the kind of future of that any more than it was when, you know, that we had the bot craze, you know, whatever it was five or six years ago and the future of technology was going to be talking to Pepsi on Facebook Messenger, right? But it does enable a whole bunch of new really interesting, you know, modes of interaction. And so maybe it's going to be talking to AIs and maybe it's going to be wearable things in some way. Who knows, right? But there's probably going to be some change to, you know, broadly how we interact with technology as a maybe as a result of this AI. But I think, you know, what's interesting to me as a like product builder at Slack is how we can make Slack more useful for our customers, you know, with AI. And that is really one of the dreams that we've had, dreams is strong, we make software. One of the things that we've wanted to do is if you use Slack for a long time, the longer you use it, the more and more messages you have. You know, we have billions of messages from some of our customers who have been using Slack for a decade is how can we make those more valuable? How can, you know, the, as Bendix said, the kind of infinite interns. What could we do to like give you the superpower of knowing everything that's happened in your company over the last decade? And what would that mean? If you could answer questions about every project that's happened, but also who's the expert in this area of the business? If we do a project on this, you know, who are the people to contact? What is, when we've had this kind of issue with a customer before, what were the actions that we took that were successful and what were the actions that we took that didn't work out so we can avoid that in the future? You know, how can we make anybody who joins the company today be an expert who seemed like they've worked there for a decade and been involved in every possible project? And I think that's a, being able to unlock the power that's in that archive of information that we have for each of our customers, I think is really valuable. And we thought that's valuable for a long time, but just no idea how we'd actually do it. And I think using large language models is probably the way in which we do that. I'm so glad we're having this conversation because actually internally at Co2, where I opened our office in Europe a year and a half ago, we've actually built this. So we're huge users of Slack because we have a team in Menlo Park in New York, London now as a year and a half ago and in China. And everybody in the firm is on Slack and we're constantly debating what's our point of view on electric vehicles, on NVIDIA, both on the public and the private side. And every day we've actually aggregating insights of like, here's what the firm is thinking on these various topics. There's obviously pieces on macro. So we've built that internally and I'm sure you would do an excellent, better job at it. And I'm also curious to ask you, we've talked a little bit about, you talked about the desktop to laptop and people have really changed how they've worked in the last few years, right? Where they're working, when they're working, the amount, the different ways teams are organized. You and I've had many walks around San Francisco discussing this. And so I'm just curious what you think today in 2023, what are some of the alignment challenges within collaboration, productivity challenges within companies today? I think this is kind of related to the AI topic in the sense that perhaps we'll see some kind of massive paradigm change to how we interact with technology, but maybe not in this era. But either way, the capabilities we get with generative AI is in some ways just a continuation on the spectrum of different kinds of things we can automate. And it allows us to automate a whole bunch of work that we weren't able to before. And in every instance, kind of starting with a spreadsheet up till generative AI, what it has done is stopped human workers from doing certain kinds of tasks and allows people to spend their time focusing on things which are more uniquely high-leveraged human activities, which tend to be more collaborative and more creative. And I think if you look back even just at the last decade of kind of knowledge work, it has tended to become a lot more complex and a lot more collaborative and involve more people. And I think fewer kinds of activities for information workers are solo. Like fewer and fewer kinds of work are done alone, more and more things are done on teams. And the biggest challenge with working with groups of people is communication, but really around alignment, how you get people pointed in the same direction and measure of agility for any kind of organization is how quickly you can get everybody realigned when you change direction. And I think that is still just like a fundamentally difficult problem with groups of people. If you've ever tried to go out to dinner with a group of five friends that is almost intractable to decide where to go and it gets geometrically harder as groups grow and that's when you're aligned as friends. Like when you're trying to do something in the workplace, it's obviously much more complicated than that. So I think the challenge still remains this kind of fundamental challenge of how do you get people on the same page, how do you get people pointed in the same direction is gonna be one that is enduring forever, however much technology advances and takes over more and more rote work and I think our definition of rote work will continue to expand, to basically match whatever we can automate with computers then whatever's left becomes the more complex, more collaborative pieces and I think that's the focus of how we can improve productivity for anybody. Sounds like a fun way to work. You know I wanted to ask you this question because one of the things I loved most about working with Cal, Stewart and the team was your very first principles. So it's like the culture was never if I came and said oh but this other company did this or we kind of always did it that way, it was just not on and one of the kind of relatively unique things you did was the direct listing and I'm hoping that the markets will see more of the great companies in the audience going public all around the world in the years to come and I'm just curious reflecting on that, it was a great moment in New York in 2019. You know the decision, what I remember the discussions, you know what made that appealing, do you have any different views or advice to people looking back at that a few years later? Yeah it was, I mean like, it was very much pioneered by Spotify, right? So following in their footsteps and I think it is not a choice for many people because you need to have like some consumer brand recognition but also the reason that it made sense for us was because at that time we just didn't need to raise any money as part of the process of going public. I think it's obviously a very different environment today. We had raised a lot of money and still had a lot of capital and we were also not burning any capital at the time and so for us it was much more of a process of we want to become a public company from like a governance and maturity point of view not we needed to raise money in public markets for the way we would do that. Now I think especially with the position that a lot of organizations are in today that's probably not what they're thinking. It's not that we wanna go, wanna become a public company but we don't wanna raise any money and so that necessarily doesn't make any sense. I think there was that brief period where a few companies were doing direct listings but economically times have changed. Whether we'll get back to that, I think then we also had the SPAC era which is largely over but I think these things come in cycles and it's interesting to have options to not necessarily just go the kind of traditional route and we did what worked for us and it worked out great.  To me personally, it's true. He hates compliments but I get to give them and I know to many people in this audience and the Slack story is one that I think we'll be talking about for decades to come. Thank you and now we have to give the stage over to the main act. All right, thank you so much. Thank you. Thank you, Slash.


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We had a night mission where we headed out and started at the beginning of the night. Went all the way until the early morning, took a different objective called objective Jackson. The enemy was actually gone before we even got there, so we took it without any resistance. We stayed there for about a day, made sure they didn't take it back, and now we're here getting ready to assault this. So this is our sand table of the objective Margos where you're looking at. We got roads, buildings, the whole thing. We are involved with the fires planning, so we're going to hopefully get some assets to call in artillery or mortars, and hopefully as well get some close air support from rotary ring patches. So I think the training is a big enough payoff to make this worth it, because a lot of the guard guys don't get that time, don't get that type of training every drill, so going through things like this prepares them for years and years to come because it's such a big event, simulates real life combat.


With another big hard-core 500 on the way in Acapulco on the road to Indian Wells in Miami the Sun's run double Which is coming up in a couple weeks. We have some big names playing in this event I'm coming back for the first time in a while. We also have some withdrawals So let's go to the list of withdrawals first. So a couple players are pulled out Demetrov He's pulled out Jera has also pulled out and McDonald also not playing in this one So it's a bit of a shame Demetro has been in good form over the last few months not to see him there But we do still have some big names that are playing all right having a look at the top of the draw here We've got eight seeds in this one and nobody gets a buy in the first round Alexander Zverev number one seed He'll take on Altmayer in the first round winner of that will take on either Kichmenovic or Schwartzman in the second round then we got Borges taken on a qualifier winner of that will take on either Draper Or number 70 Tommy Paul and they have played a few times already this year and the matches are being very very fun So watch out for that one Then we've got number three seed Demetro taken on Daniel winner of that will take on either Escobedo or Ovner in the second round Then we've got Felix Ozelia seem taken on a qualifier winner That will take on either Sefulan or the number five seed City pass in the second round is a really interesting potential second rounds Of course plays to watch out for you gotta watch out for Zverev and City pass both sort of bit of a letdown last week And lost carboss as the top two seeds didn't make it to the finals So they'll be looking to rebound same with Diminor bit disappointing last week But did make the Rotterdam final Tommy Paul of course has made back-to-back finals in his last two events So gotta watch out for him and Ozelia seem unseeded always gonna be dangerous if you can get into some form Especially if you play City pass in the second round that'll be really fun So top of the draw really dangerous players there and the seeds are gonna have to do some work bottom of the draw now We've got the number six seed rude taken on you banks winner That will take on either Mendez or live itch in the second round Then we've got Evans taken on Shelton winner that will take on our either on Audi Well the number four seed fritz in the second round And we've got the eight seed Tiafo taken on Purcell winner that will take on either Norrie or Coppa in the second round Then we've got a qualifier taken on Thompson winner that match will take on either another qualifier or the number two seed Runa in the second round and of course Runa is coming back with Patrick Moratoglu So let's see how that impacts his play but again some really interesting matches First second round potentially if we get the winners to play each other you got to watch out for Runa Of course with Moratoglu back in the camp Let's see how he can rebound after a bit of a disappointing start of the year Casper rude has made it the final lost carboss and by the time this is out You probably know if he's one or not They've definitely got to keep an eye on him Taylor fritz also winning in Delray Beach You're gonna keep an eye out for him as well Jordan Thompson just speeds very of this week in lost carboss So definitely a player it could be really dangerous for Runa in that second round So not an easy draw for the top seeds But but it could be really really fun to see some of these second round quarter final clashes as this tournament progresses There it is There's the Acapulco draw and again just kind of like what we have in Dubai the top seeds have got some tough work to do It's a really dangerous opponent It's not only in the first round but second round as well and beyond this tournament is gonna be worth 500 points So it's got a lot on the line especially for those players playing for those top 10 spots guys like Diminor fritz rude Even city pass who's still trying to get back into that top 10, but let me know down the comments below Who do you have winning this one? It's so open of course Diminor is the defending champion He kind of snuck through the draw last year and got the win with beating guys like Runa and Paula on the way Maybe it's gonna be Vera kind of rebounding from last week. Maybe it's gonna be somebody else Let me know down the comments below. Who do you think is gonna win this one? But Acapulco bit of a stacked draw for this one