Hello, everyone. Thank you for being here. I'm Maria Medina. I work as a data scientist at Microsoft on the consulting part here in Madrid and I've been working in data science consultancy for little more than six years now in in different places and So it's amazing how things have evolved lately and Data scientists all over the world. I'm making great models that are solving really complex problems But I'm sorry someone has to say this we suck at putting models into production And this is of course a problem because life out there is not like kegel It doesn't matter if we have a model with 99% accuracy if nobody is using it then we're not providing any value But it's it's fine. There's some things that we can do to to help fix this So I'm going to walk through some of our pain points and some things we can do to improve this and being able to Put models into production quicker or even at all So one of the main things here is that in in machine learning systems the There's the part of the code that is actually related to to machine learning is very very very tiny it's the part of the code dedicated to training models and scoring predictions is Very small compared to these other parts we have about handling data creating features Configuring things deploying things and so on. So if we are Using notebooks for example to handle of see all of this It's very likely that we're going to fail to be able to build a strong robust system But we also need notebooks at some points because data science is a science right and Therefore we spend a lot of time Experimenting and trying out new things trying different ways of handling data train different models different configurations And we keep this in like in a tier in a natural TV process Starting from the business understanding part moving on to the data acquisition and understanding plan doing a bit of analysis and then starting with building the models and we keep this and as an iterative process But eventually we stop Experimenting and we should start building something more robust that we can eventually operationalize So from that perspective data science processes data science projects looks like something like this, right? We have a big experimental phase where we try out many new things and Then we start developing something that is going to eventually come into into production or in a deployment environment and You might see now where I'm going because this is looks similar to something they talk about in traditional software development They have only the development and the operation parts But recently they have come with this new philosophy which these two areas aren't disjoint anymore But they work together development and operations to make processes more efficient and that practice is called DevOps, right? So The definition we like to use at Microsoft is this one So DevOps is the union of people process and products to enable continuous delivery of value to our end users And I'd like this definition a lot because it it puts people on the top of it So it's it's stretching out the importance of communication and collaboration between Like inside the team and between teams to enable this kind of efficiency And we also have processes there because there's probably a lot of things that are going to Need that are going to to change for us to be able to implement these practices And of course, there's also a lot of products and tools that can help us through this process So in traditional software development delivering something can mean building a product or a service that are end users are going to use but in machine learning Delivering something it actually means Having a model ready to like for example in our service in an API that given input data It's going to issue predictions And that's what we want to deliver with these DevOps approach So there is not like an official list of what it's DevOps and DevOps and what it isn't But all the sources agree on some common points that and I'm going to try to explain those points and relate them to from the traditional software development world to the machine learning world So the main difference between these two worlds is in software development. They have like the code It's Every behavior of the system is ultimately coded into into the code if you look at the at the code You can see how the system is going to behave given a specific scenario at every time But imagine learning is not only the code that determines the behavior of the system, right? It's also models and ultimately the data used to train those models that are going to define the behavior of our system So we need to take into consideration these three things and put them together into these Practices here. So we have to take into consideration data models and code three things So the first practice is of course using version control this is Having a way of tracking your code changes So if everything in something goes wrong, you can go back and fix it as quickly as possible And in our case, we also of course need to track The changes in our models and in the data we are using to trains those models So we will need a version control system for modeling for models and data as well the second practice is called continuous integration, which is the being able to automate the process of from code Building testing and building Your code so you have something ready to use And in maxi learning having something ready to use also means having a trained model that is able to issue predictions, right? So our continuous integration practice is also going to include having having a model train. So our build Our building process is also going to include model training Next we have continuous delivery Which is a practice in which from from the cold be privileged previously built. We are able to Deploy it in different environments different staging environments and ultimately going into a production environment with that code that code or a system is going to be used by the end users and In our case, this is very convenient because we can have multiple stages with different models. For example, we want to try out and finally automatically move the best the most successful modeling to production and So if we're going to build many different environments to try out our system We need those environments to be the same, right? We cannot Riff's to have some errors related to different packages, for example being installed in different systems So we need a way to replicate our environments in a safe way and we do that with infrastructure infrastructure as code That is a practice in which you use code and configuration files to Automatically build your systems and build all your environment just from deterministic code So every time you build it again, you are sure that you are making exact copies of your environment and that is related to the use of microservices because If you are going to build your system Very often I'm from scratch. It's much easier if you use Tiny pieces that relate to each other instead of one big environment and those are microservices so microservices are those tiny pieces that have a very specific function and Relate to each other and communicate with each of the other Systems of microservices to create a whole environment and that way it's much easier to to deploy and automate things And finally, it's very important to monitor all of these things because you since you are building all of these Automatically you need to have a way of quickly finding out if something is going wrong. You get a notification and you go Fix it as soon as possible So in software development The only thing that can that you have to monitor and that can fail is your code, right? But imagine learning systems we can have our system running properly from a development point of view like we have our model giving out Numbers that is expected but those numbers might be wrong right because the model might drop its performance for example, so We don't only need to monitor our code Of course, we also need to monitor our model performance and also the data We're giving because that data might change as Casey was saying this model that the world We're living in might change and our model is going to probably suffer from that and we will need to adapt to that So now we know which practices are the best ones for Operationalization things in in data science. Let's have a look at six Steps we can we can give to to get into to an mlops adoption And this is how we do it in in Microsoft so Let's imagine you have already a trained your model after the Experimentation you already have come up with with a good model that is performing well And I don't know if this has happened to you as well But it happened to me a lot of times you have your model and you either forget to store it For example in a file or you have you rerun all your code and suddenly your model has changed And it's not as good as it was before anymore and you have overrated it And you need to start all over again and trying to see which things when were wrong So you can replicate your model again and that problem we had it because we didn't have as Version control system for our model. So the first thing once you have a good model is saving it So you don't lose it again And for that we use a tool called a share machine learning which is a resource available in in Azure to use for free and it's It's not totally meant for for saving our models It's meant for much more things and it's not replacing any of our machine learning code For example, if we're using Python with cycling all that code is going to stay and as your machine learning In going is going to assist us through all the the lifecycle of our data science project so and This saving the model thing we're doing is like the second step in in that diagram But it's going to help us through all the steps and we're going to see it so the way we we create this with Asia machine learning resource is by going to a short portal the cloud and created what is called a workspace Which is kind of a folder a project we have for for our System that is going to store all the assets and all the Yeah, processes that are related to our machine learning system And we have two ways of interacting with that environment with that workspace We can either do it by by hand clicking on the portal They have what it's called a designer, which is a web-based tool with Dragging and dropping you can build a machine learning system for from scratch without the needing to have Programming abilities, but since we want to automate this We need to use code of course and that we have three ways of interactive Interacting with this workplace through code We can use the a short command line interface, which is just a programming use on on your console And you also have Python and our packages with a lot of functions and functionalities to interact with the workspace So if we want to save our model we say we have our model file We just give it a name send it to the our workspace through a function call and then in our environment We're in our worst case We are going to have a list of all the models we have registered with the not with the name the version number the date The day we uploaded it and so on so all our models are in the store in there. We are not going to lose them anymore But now So you might have your model So simply having your model file and giving it to the software Developing in your team if you're lucky to have one It's not going to work right because you need to know you need to have some machine learning knowledge to to be able to extract the the predictions out from that model so It's your machine learning also helps you building a really simple web service And that way you just have an API with an endpoint you can give to the software development team So they integrate that endpoint with their Like bigger system that is kind of an microservices approach, right? So the way of doing this is you just take the model ID You got before for the model we just we have just registered in the previous step We write a very simple scoring script saying like a few instructions saying from the You load your model you do some data transformations and this is how you predict which is normally calling the predicts function And that's it and you may have some dependence. This is your good You can in your code you can also specify those and then you choose what they call a compute target Which is where in in the world is this web service going to be deployed? It can be locally in your machine. So in that case a short machine learning gives you the Docker image so you can put so you can deploy it anywhere you like or you can use hs your Container services, which is hr container instance is for like for the simple lightweight Containers, but you can also use Kubernetes service if you need something more robust and That is see you just make the the function call and you get your running your web service running with an endpoint You can call and give the data and the model will return you the predictions And then you have a list if you go to the your workspace you can also Make function calls and get these data like programmatically, but if you go to the Isher portal you can see the list of all the deployments you have Active and running or even stopped and if you click there you can go back To the model is actually being used in this service that you have just deployed So now we have our model up and running and we might want to monitor it right to make sure that everything's going fine And it's still working properly so the way we have for doing this is an integration with another microservice called application insights inside the Isher portal and this is the service that is in charge of monitoring every app inside a sure So it's very integration. You just have to click like some configurations and you can activate Web service log so you have a log of everything that is happening inside your work service But you also have two very cool things related to model and data you have you can track all the data that is Coming through your to your web service and the predictions you are issuing so you can check if your performance if the performance of your model is dropping and you also can have a Notifications if the data you're receiving the first two mat two mats From the data you use to train your model back when you train it So and you will get an alert and you will be able to go see what is happening And maybe retrain your model if you need to so in the case you have you want to retrain your model you might want to try out new Holland load of of new algorithms parameters, maybe even create new features and So see with you since we're doing this with an MLOps approach and we have already Experimented back when we built the first model we know now More or less the things that are going to probably work out and the things that are not so we can start building something more robot So we can easily automate this and the way we have for doing this with hr machine learning is You take so you can have the data set download Directly from your code, but it's it's a big data set is more likely that you're going to upload it to the cloud And use it from there. So you just specify the connection. Hey, I'm going to use this data set located over here in any storage option you want You build a training script a training file with all the steps You are going to give for for the training you do So everything you were doing before such as I don't know train test splitting the data or proving Trying out many different options models and so on you're still going to do that with the framework you prefer You're going to specify the dependencies and you're going to specify the compute targets Which in this case can also be your local machine or also maybe a virtual machine with CPUs or GPUs if you're using if you're doing leap learning or Databricks if you're using a spark or many other options and Asia machine learning is going to package everything together Send it to for example the virtual machine is going to start the virtual machine send everything they build like an image for to be executed there and store all the results that you're getting in your training process and then when the training is finished It's going to store everything shut down the machine and And happily So you are you're only using the compute you need and on the only times you are training It's automatically being starting and turned off when you're not you using it again and then in the workspace what you get is a summary of all the train all the all the experiments you get Every Interaction everything you try with with a new model is called an experiment and then there you have all the metrics you You store like I don't know if you're accuracy and the same the time it took to train the model and everything You have it there and you can take the best performance model from all the Log you have here of all the experiments you have made So you take the best experiment that's the model you might want to put into production of actually and you Register it as we were doing before so before we took our model file We had in our computer and register it and that's like Having a list of favorite models that are likely to be end up in in a productive environment And Here what we are doing is okay I'm not giving you a file because I've already trained everything in the system So go take that experiment we put which was the best one and take the model you had there and Save it as a favorite to be used further on And in here what we have is this is the same list that we have from the beginning But now we have That parameter there which is called a run ID that tracks back to the experiment We had and the experiment tracks back to the code We used to train the model and the data we used to train the model So from that's this model here We can go back to the even the data we used to train it We can go further to every place this model is deployed into so now we have the end-to-end Traceability of our whole machine learning system from the data We used to train the model to the end point with that model is working in our system And now we have that end-to-end process now It's time to automate everything using continuous integration and continuous delivery pipelines and for that we are using Azure DevOps that's the tool we use inside Microsoft, but you can use any other end-to-end software building tool you want and In here we have so in here we have the repositories, but you can integrate that with github for example if you want to We have the pipelines we have a task force, but you could also use I don't know JIRA if you like or Jenkins or any other tool for automated things you you want You might want to use and integrate all the pieces you want if you like different tools for different things and What we're doing here is So we're having our our cold repository. We normally are Trying to do a trunk-based philosophy world everything tries to be on on the master Brands and if we do some changes we try to keep them as short and as small as possible to to prevent having like a mess when we try to merge brands together so We set up a trigger in the month in the master branch and every time something changed in that master branch We can trigger a really build pipeline story That is going to perform every step we configure here So this is totally customizable and we have selected with steps We want to take each time our code changes in our repository So the steps we are taking here is first whenever something changes in our code, we're going to Launch a training with special machine learning. We're going to take that code which also has All the variables we're going to use the configuration for the models We want to try and everything else and we're going to launch a training and Those experiments are going to begin to run and where all of them are finished The pipeline is going to move on to the next step and the next step for us is going to be having some command line script Configure there to get the best experiment we got and that Experiment that was successful. We're going to register it as our favorite model and as your machine learning is going to package it and we're going to Publish it into this pipeline as an artifact. We're going to use later So that is the result of our build pipeline is the model We are going to use eventually in in a productive environment And this is how it looks like when you execute this so you get You can have many jobs if you want to do things in parallel, but this is like a simple thing We are going to go doing one Like one line and we have every step We had configured in our pipeline and we get the green check marks is if everything goes right if something goes wrong We get a cross a red cross and the pipeline stops there and don't continue to the next Step so for example if the model training fails, then we don't register any model because the previous step has failed And now we have our release pipeline, which is the continuous deployment Practice and we can set a trigger when and when an artifact change So in this case when our model article fact change, which means when we have a new model train We're going to release. We're going to trigger the release pipeline, which is going to Deploy that model into different environments we We can configure this is we for example may want to create first deploy the model first in a development environment and then move on very Following stages until we'll get to production environment and we can even put a manual approval Process in between so we make sure that somebody is taking these before changing the model into the into the Productive environment if we want to have some business decisions involved there or anything And this is how it looks like so if we have Everything has gone right then we have all our Environments one after the other and if we click on one of them We see all the steps that we're performing and everything green in there if everything has gone, right? So now we have both of our Continuous integration and deployment pipelines in place. So we have automated everything But there's one thing missing very important thing here So I didn't mention it yet to like to simplify the process But of course if you're doing all of these automatically you need to have tests everywhere to make sure nothing is going wrong And you're not propagating something wrong until the very last stage of your deployment The problem with testing in machine learning systems is this that is very complex and difficult So in here you can see the difference between a traditional system and software development system in which we have only unit testing integration tests monitoring but in machine learning systems again We have data to test models to test and codes to test a code to test So we have many different tests we we need to we need to take into account This is a very good paper that has a battery of 50 tests or so so you can have a checklist of everything you need to test But I'm going to sum up with some examples of things you might want to to try So for example before launching the training So imagine you yeah, you make a change in your code and that triggers training pipeline and you Start machine with GPUs and you have it running for eight hours And when you finish you realize that the data was wrong and that's of course a waste of time and resources, right? So you want to make sure all your data is fine before you actually start doing anything and consuming resources So in this step you might want to take things as Features how are they built if their distributions are fine if everything looks more or less correct before you move on to the modeling phase and then Before registering the model you might want so want to take the model you have created To see if it's actually Doing better than the model you currently have into in production For example, or to take that is not biased or skewed in any way You might want to have different tests here's here to check that your model is actually good and then Every time you deploy a new environment you need to have a lot of tests their integration states to check that everything is working fine and maybe have a different Difficult examples to make sure that everything is behaving properly before and you move on to the next stage And if any of these tests fails Then the pipeline is going to stop and you are not going to or this the tool is not going to move forward to the next step And this issue DevOps has integration with many of the testing frameworks out there So you can have a test tab here inside the tool where you have like all the tests you have run and The processes that has have gone right and the ones that haven't and you can see the log of everything that has happened inside your system and with that we already have an Yeah, an MLOps system that is automatically running and Yeah, efficiency Automating most of the machine the machine learning and data science projects. This is something that looks like this and Since I work in in consultants We try to do this in in every project we have but of course that depends on the maturity level of the customer and the project So we try to do this step by step Try not to rush anything because it's easier to start deploying a model and then start with the experimentation phase and then start and then follow with the CICD pipeline so it's better to to have everything done a step by step until you read this whole big architecture up and running But I think we have it so in in our area I think it might be easier than than DevOps in in software development systems because this is a quite new area Meaning that we don't have that many systems that have been running for 10 on 20 or 20 years So we don't have that much legacy. It's easier to change and also there are some teams that are even being built now So we're like a very young area where it's easier to change the culture and implement something like this easily and in an efficient way With that I conclude it's only if you want to know more about the business perspective of this you can take the talk by my colleagues Pablo and Carlos tomorrow and also if you want to know more on the data side, you can take the talk about Automating data quality, which is also going to be tomorrow where my colleague. I turn Thank you. You have any question? I Set here some resources you might want to go check great. Thank you then


The recent success of mRNA vaccines in SARS-CoV-2 clinical trials is in part due to the development of lipid nanoparticle delivery systems that not only efficiently express the mRNA-encoded immunogen after intramuscular injection, but also play roles as adjuvants and in vaccine reactogenicity. We present an overview of mRNA delivery systems and then focus on the lipid nanoparticles used in the current SARS-CoV-2 vaccine clinical trials. The review concludes with an analysis of the determinants of the performance of lipid nanoparticles in mRNA vaccines. This article was authored by Michael D. Bushman, Manuel J. Carrasco, Suman Alashetti, and others.


Open source has the potential to drive transformation and innovation in an organization, but many companies still have questions on just how to intentionally and strategically maximize their open source investments to achieve those objectives. It isn't just about the technical side and the decision to use open source software to support and grow your business. It's about also embedding value into your business. Really the why about why you're doing this in the first place, as well as the cultural considerations for your teams and the value that you put back into the community. We're going to talk about how you can use these levers to help drive innovation in your organization through open source. But first I'm Tracy Robinson I'm with GitLab, and I'm a technology go to market strategist, meaning I bring people ideas and resources together to create develop and take to market innovative technology solutions. I'm always looking at how businesses can transform to meet the needs of tomorrow through technology people and processes. So let's take a look at the obvious question. Why adopt open source software in the first place. According to a recent industry analyst study, nearly 70% of enterprises report open source is very important, or is a mission critical enabler of digital transformation initiatives. There are myriad benefits for adopting open source. It's helping organizations with achieving faster time to market and it's providing more efficient, more secure innovation. It's also helping to deliver a better developer and contributor experience that translates into happier employees and retention, which in turn reduces cost. It also provides a better customer experience and offers a greater flexibility and interoperability and many organizations state that open source software is also a significant part of their cloud migration initiatives. As a recent McKinsey McKinsey report described the biggest differentiator for leading companies in an industry vertical was open source adoption, where they shipped it from being just users to be active contributors. The reports data shows that for companies in the top 25% of their developer velocity index, the DVI, who adopted open source, that adoption had three times the impact on innovation than for those companies in the bottom 75%. According to McKinsey, building an open source culture is about more than using open source software within the code. It extends to encouraging contribution and participation and the open source community, as well as adopting a similar approach to how code is shared internally. That is strong inner source adoption for organizations that already have a high DVI score. Open source adoption acts as a major accelerator. In short, open source clearly has proven potential to drive transformation and innovation in an organization and to be the market differentiator that you need. However, in order to maximize your investment of time and people and money and resources, you must strategically and intentionally create a program that makes open source a priority and not an afterthought. The extent of the program should be threefold. It should, one, prove and demonstrate the value of your investments internally, especially to leadership. Two, it should embed the ethos of open source culturally, not just technically throughout the organization. And three, it should champion the virtuous cycle of contributing and reaping in the community. It is critical to demonstrate to leadership and to other internal stakeholders how open source adds value to the business, that your efforts and investments make business sense. You should start with defining why you're building this program and where you want it to go. Set goals and metrics, lay out your ROI objectives. These goals and metrics will certainly help keep your projects on track, but for the purpose of delivering and communicating value to leadership, this action is absolutely critical to reporting regularly on your successes. It will also help with relationship building with potential partners, users and developers, and the transparency that you will provide with this will help establish much needed credibility. You will find that metrics are only one measure of success. You'll want to also include non qualitative measures that are equally important as the numbers and articulating that value story. Some dimensions you might want to report on include your participation and the level of influence in the community that you're having your ability to attract and retain developers, your organization's reputation in the open source the health of the company and their open source projects and those that you contribute to externally and how well your open source licensing compliance is being managed. Perhaps you'll want to set up an open source program office to help you achieve your strategy and your goals. And if you do go this route some successful examples you'll want to consider include capital one red hat drop box to name just a few. So please see the resources at the end of this talk for links to their published case studies with greater insights that you can apply from the programs. If your communication plans to leadership you should make sure to explain and to demonstrate your risk management approach to ensure their sponsorship and the resources that you need. A couple of examples of organizations who are proving the value internally include Salesforce and the US Department of Defense. Salesforce has this open source strategy that keeps everyone aligned. They have a dedicated open source program team that circulates internal documents to the company's engineering team that provides strategic guidance and encourages the creation and the use of open source. The documents provide the foundation for an open source culture culture and it lets the team know clearly that the company's leaders are fully on board and behind what they're doing. One goal of the company's open source program is to build its reputation among developers. The open source program also focuses on expanding the communities that align behind the company's own open source projects. So the community doesn't just use their software they also are contributing to it. Salesforce also measures its own success against the industry-wide success of open source. The more progress there is in open source in all of its many dimensions, the better off everyone is because open source software and what constitutes shared components that everybody can depend on and use. That is what makes their entire ecosystem and the whole industry better and everyone benefits. With the Department of Defense, historically, software development has taken anywhere from three to 10 years for big weapon systems. Now releases, which usually took as long as three to eight months, can now be achieved in one week. In order to break the cycle of slow moving development, Platform One, which is the US Department of Defense's DevSecOps Enterprise Services team, it adopted a new approach to software development services that enables the DOD to support continuous upgrades and new features with integrated cybersecurity testing on a much faster timeline and at a lower cost than the traditional waterfall models and cycles had allowed them to do. And the core of this new approach is leveraging open source software, which has now allowed Platform One to adopt new technologies rapidly. And it takes advantage of the cutting edge solutions and innovations that were previously out of reach. The next step that you can take to invest in embedding open source culture into your organization is weaving it into the DNA of the business. According to McKinsey, culture that fosters open source includes factors like psychological safety, collaboration and knowledge sharing, bias towards continuous improvement, servant leadership, customer obsession and talent management, which also includes employee value proposition and career growth pathing. This all looks like creating and communicating policies for open source software usage and contribution on the individual level as well as making sure that your teams have the right level of support that they need for their success. Autodesk has done a really good job of this. They've made a company-wide shift to open source and to inner source. Inner source simply means applying open source development practices and methodologies to the internal projects, even if the projects are proprietary. The culture change required to be successful can be very, very difficult. It's a hard shift from a traditional corporate hierarchy to an open approach. But Autodesk's role in open source has become critically significant in the media and entertainment industry because of all the things that they're doing. And they're now looked to as the leaders for open source innovation. At Autodesk, the goal is to let engineers get loose from their business silos and to create a fully open source cloud-centric company. They have an intense focus on innovation, as well as making all the products work seamlessly together. So what they're trying to do is enable those engineers the freedom to find a way to make things work out in ways that they wouldn't previously have even uncovered. And part of that cultural shift really is getting all those internal stakeholders to say, you know, instead of why are we doing all of this, why are we doing this for community when we only care about our stuff, turning them past that and getting them to be champions for understanding, yes, we're doing this work, but that's going to be used elsewhere. But in the end, we're going to get benefits from pulling in work from other people who've done things that they know are going to be used in the community itself. So it's really changing their approach to that. GitLab has also invested heavily in the open source ethos. GitLab knows a lot about how hard this is, but also what the culture and results can be like when you build your organization around open source. So let me just take a moment to give you a bit of background on GitLab, as far as contacts for the rest of this discussion. We were founded in 2014. We have over 1300 employees in 65 countries around the world. We have over 30 million paid and free users who use GitLab from startups to enterprise level companies all over the world. And we also have a very strong community. We are open source core and that means that we balance the need to improve the open source code of GitLab with the need to add source available features in order to generate income. We have an open core business model and that generates almost all of our revenue with subscriptions from our paid tiers. We recognize that we need to do that to generate the income and also to balance the needs of our open source project. And as part of that corporate dual flywheel strategy that we have, one of our flywheels is the open core flywheel. Essentially it means that the more features that we have, they drive more users, which in turn drives more revenue and more contributions, which leads to more users. So that keeps going. Every single release that we have includes approximately 50 new contributions from the wider community of more than 2400 active global contributors. We strongly and actively engage with our community in stimulating and thought provoking conversations about how to best and safely leverage open source software to solve industry challenges. Our approach to open source culture is this. From the very beginning, our founders have believed that actions be louder than words. They've instilled a set of values and behaviors that are inspired by DevOps practices that drive how we function every single day. These traits are the key to why we're so successful and how we help our customers to be successful as well. It's all centered around the fact that everyone can contribute. That's the core to all of GitLab. We believe that everyone has a valuable part to play in contributing. That can be employees across departments, external contributors, customers, competitors, even random strangers on the internet, everyone. GitLab believes in a world where everyone can contribute and when they do, consumers become contributors and then we all greatly increase the rate of progress. For example, just one of the ways that we're contributing back to the community is through supporting the Finno's legend project led by Goldman Sachs. Legend is the project that provides a logical modeling language as well as the platform and visual model tool for banks and regulators. We also provide open source organizations access to our top tier features plus 50,000 CI pipeline minutes for free. Collaboration is also key to who we are. It's all about eliminating the silos. Silos create islands of information and barriers to trust and create friction everywhere. The first key to accelerating delivery is to reduce the impact of the silos and the handoffs. That doesn't necessarily mean a hundred percent flat organization, but it does mean that you'll find ways to encourage and enable collaboration everywhere with all of your stakeholders. Collaboration enables a DevOps environment that makes it possible for multiple work streams of product and development, QA, security operations, everyone to perform their task at the same time instead of waiting for handoffs. Teams that work concurrently and review changes together before pushing code to production. When employees make quicker decisions, when they're sufficiently informed, employees can communicate and make impact in real time or near real time. This keeps the execution process moving along and helps firms to change direction quickly. We also have a results driven approach. We do what we've promised to each other to our customers or users and to our investors. We care about what team members achieve, the code that they shipped, the users that they made happy, and the team members that they've helped. Someone who took the day off shouldn't feel like they did something wrong. We don't have to defend how we spend our time or our days. But we do trust team members to do the right thing instead of having rigid rules. Our focus is to improve the results that customers achieve and that their customers experience. And that is way more important than a long list of other things that can get in the way of delivering results. We value efficiency. We care about working on the right things, not about doing more than needed, not about duplicating work. This enables us to achieve more progress and that makes our work more fulfilling. We write things down. We create simple, boring solutions to problems instead of complicated ones. And we optimize solutions globally for the broader GitLab community. We're respectful of others' times. We spend company money like it's our own. We're frugal and we accept mistakes. We also honor diversity, inclusion, and belonging. The values of diversity, of inclusion, of belonging, those are very fundamental to the success of GitLab. And it complements our other values, specifically collaboration, efficiency, and results. We strive to create a transparent environment where all team members around the globe feel that their voices are welcome and heard. We aim to be a place where people can show up as their full selves each and every day and they can contribute their best. There is empirical evidence that diversity and leadership supports innovation. It promotes better decision making and it improves financial results. If that believes that many perspectives coming together creates a more innovative environment to work in, to be satisfied in, and have satisfied teammates, which all lead to better product and increased profitability. Part of our inclusion is realizing that you can't make great software without UX designers, without technical writers, and all the other people that all bring it together. The people who are promoting that work and they're helping to make the community inclusive are a huge part of the overall success of GitLab. We also work in iterative chunks. I'm sure that you've heard of minimal viable product, which was made famous in the lean startup. But we think that a minimal viable product is too big. We even consider minimal viable features too big. Think about how much you invest in designing and developing and testing and shipping in MVP or in MVF. Lots, right? We push ourselves to ship minimal viable changes. So we ask ourselves, what's the smallest change that is an improvement? Not something that's incomplete, just minimum. Not untested, just minimum. Here's an example of how we create a new webpage. We start with the title and a few key points, and then we publish. It's live, it's public, and from there we make frequent improvements while watching the feedback from our stakeholders. The point is to learn as we iterate. Here's why. Four good reasons. One is we get feedback and we learn faster. Two, we limit our risk from any one change. It's much, much smaller. Three, we can back out a small change much, much easier. And number four is we learn and improve faster because smaller equals agile, nimble, more responsive. And at GitLab, we've embraced the concept of smaller changes and a low level of shame to unlock velocity. We're also transparent and we believe in direct communication being fundamental and foundational. We strive to be open about as many things as possible. By making information public, we can then reduce the threshold to contribution and make collaboration easier. Everything we do is public by default, including issue trackers, but also our marketing and infrastructure and the public repository of our website, but also contains the company handbook. Transparency creates awareness for GitLab. It allows us to recruit people that care about our values and it gets us more and faster feedback from people outside the company and makes it easier to collaborate with them. It's also about sharing great software documentation, examples, lessons and processes with the whole community and the world in the spirit of open source. We believe that creates more value than it actually captures. Most things are public unless there's a reason not to be. And we take above average measures to make sure that those things are protected and secure and that things are confidential as appropriate. The cultural values that I've outlined here enable us to rapidly respond to markets and to define, develop and deliver new capabilities for our customers. But the good news is that these aren't proprietary. They are the foundation that anyone can adopt to transform their culture and accelerate their open source innovation. Step three is to champion the virtuous open source cycle. This involves not just using open source software in your business, but also being active contributors upstream, meaning sending that thing code back to the source repository and the project where contributions happen and releases are made. This has been official for the original source code and the forks of that code. It also involves starting and maintaining projects and ensuring that the right infrastructure and technology are in place to support your program. SAP and Uber have really great examples of this. SAP started its open source journey with Linux in the late 90s and its involvement grew over time. They formally defined and internally documented their processes for open source consumption and the company committed to being to using inbound open source projects to build SAP products. Eventually though, their developers began actively contributing to several Eclipse Foundation projects. But it wasn't until 2008 that SAP started to actively promote open source contributions from their employees on a company-wide basis. That was also when they decided to roll out their outbound open source process. They also integrated open source tools further into the development processes. In 2014, SAP shared with their open source community a tool they had developed for managing open source contributor license agreements. Their central open source program office was then established and that was under the guidance of their CTO. Since then, they've become even more visible in the open source community and they see that as a sign of their continued commitment to excellence in open source. Their goal is to form more partnerships and to spur accelerated innovation. Uber also has been an open source software user since their very beginning. It was natural and organic for them to create an open source program office because it allowed them to build their platform and to scale it at unprecedented speed. They love open source at Uber and they view it as essential to their success. In fact, their long history of contributing to open source projects has many active maintainers in the community. Their successes have solidified their program as being mature and it's prioritized the investments that they made in it. So what they want to do is they want to encourage their ongoing regular involvement in open source. So they instituted internal standards for governing and incentivizing contributing upstream and back to the community, which is one of the best ways to help ensure that you have sustainable projects. Those greater opportunities and innovations tend to spread outward to create greater good. Now, in 2019, Uber helped launch a program to support community development, which helped to improve mobility, transportation, safety and infrastructure for their communities. They have projects that can help like urban planners, policymakers, local governments all gain critical insights and better understanding about the data of their cities that they they operate in. They say that the collective knowledge that their engineers actually grows by integrating with the open source community and they note that one of the hidden benefits is that it's not just the technology that's benefiting. The engineers are learning about collaboration and best practices, and they're helping to build their leadership skills for the process of interacting with the open source community. So, what is it that you can do to realize the value and innovation that is the promise of open source. How can you make open source work for your company. And this three pronged approach to drive the innovation that you want to achieve. Start with proving and demonstrating the value internally. You've got to define your strategy and your goals and your measures of success. You want to make sure you know what you're looking for and, and your ROI is laid out so that you know exactly what you can expect. You should look at can at implementing an open source program office and designating a person, at least one person to help to manage the open source for the company and what that direction should be. Also invest culturally, not just technically technology is very important with all of this, but you need to look at driving home and embedding the culture into your company culture that that open source ethos is really important. So that people understand it is about contributing and reaping, but both of those things are part of this cycle. And then you also want to make sure that you're communicating the right policies for usage, and what does contribution look like, and what does it mean for you to accept contributions. You should also look at what that internal support layer should be a training and education should be, and to make sure that you're you're providing the right level of support for for your employees and for the community that you're you're working with. And then you should also make sure to participate in the virtuous cycle of contributing and reaping. So make sure that you're contributing upstream. That's very valuable what you can get back to the community, but also start projects, hire from the community support growth of people in your organization as well. Look at simplifying your IT infrastructure so that you can optimize your collaboration. There's also a lot of great projects out there that you can participate in, and whether it's the organizations it's projects and special interest groups but there's a ton of them here at Finno's and at the Linux foundation that could use your contributions and your help, but also groups like the clips foundation the triple foundation and many others. So, obviously, there's still so much that I couldn't cover today. I want to invite you to dive into the resources here on this page to help you scale your open source within your organization. There's a lot of really great things here and I've just touched the surface of it but I think that you'll find some really, really strong stories that you can learn from. So please visit our website about dot get lab dot com and you can check out our handbook and learn more about how we do the things that that I've shared all about our culture and how that's been very helpful for not only our organization but because we've shared that many other organizations have used that to work on building their culture in their DevOps culture in their their companies. So, all of this is out in the open, and I do invite you to take a look at it. And speaking of transparency, please also see our product roadmap and learn where we're headed in the spirit of collaboration and open source we like to hear your feedback. And we'd like you to join us as partners in development so please feel free to send me a connection on LinkedIn. I'd love to hear what you're thinking and your feedback as well. And remember, everyone can contribute. Thank you.


For more videos on people's struggles, please subscribe to our YouTube channel. Venezuela's new National Assembly was officially sworn in on January 5th. The investiture of the new legislative body marks the end of submission to US imperialism and the return of the legacy of revolutionary leaders Liberator Simon Bolivar and Commander Hugo Chavez. The great patriotic poll led by the ruling United Socialist Party of Venezuela Pesuv and other Chaviza forces holds a majority in the new National Assembly elected for the period 2021 to 2026. The Pesuv Alliance won a resounding victory in the parliamentary elections held on December 6th, 2020 and secured 257 of the 277 seats in the country's unicameral parliament. The swearing-in ceremony began at 11 a.m. and was held at the headquarters of the National Assembly in the capital Caracas with all of the sanitary measures in place against COVID-19. Presidents and heads of state from across the world joined the ceremony. Representatives of various social organizations from across Latin America were also present at the event. Meanwhile, the European Union has withdrawn the support that they had been extending for the self-proclaimed opposition leader Juan Guadó. Ahead of the ceremony, paintings of Bolivar and Chavez were returned to the parliament's hemicycle. They were removed following the victory of the right-wing forces in the 2015 elections. With the inauguration of the new congress, the people of Venezuela will recover its parliament and legislative independence. For the past five years, under the dominance of the right-wing parties, the legislative body was used as a tool in attempts to overthrow the democratically elected government of Socialist President Nicolás Maduro. The new National Assembly faces a number of challenges such as the restoration of economic, institutional, political, and social stability in the country. It has a great responsibility of drafting and enacting laws that can confront the great economic crisis generated by the numeral unilateral coercive measures imposed by the United States on Venezuela. President Nicolás Maduro in a meeting on January 4th asked the newly elected deputies of Pesuv Coalition to focus on promoting the political dialogue, reconciliation, coexistence, and inclusion in the country's economy.


We are talking about national cultures and in this topic, we are going to talk about the particular aspect of ambiguity and attitude towards risk. You know that people, they vary on their ability to tolerate and accept risk and according to that, they take decisions which are either risk-taking or that are risk-avoiding. That also relates to how a person is ready to accept responsibility because when you accept responsibility then you are still taking risks. So how much they are ready to accept responsibility. A person who is ready to accept responsibility is a person who is tolerant towards ambiguity and risk and a person who is not ready to accept responsibility is somebody who is not very much tolerant towards ambiguity, towards risk and uncertainty. So these characteristics, they can directly influence the degree to which people are willing or able to make decisions on their own and accept responsibility for the outcome. So it directly affects on how much you are ready to make your own decisions and accept responsibility according to that. That actually depends on what kind of upbringing or what kind of cultural values have been inculcated in you. You see that in the developed countries, particularly in UK and USA, children, they are given this opportunity as well as they also have this responsibility to take care of them earlier in the life. So from 16 and particularly 18 onwards, they are supposed to be taking care of themselves. So most of the children, they move out of the parent's house and live on their own. Even if they are living with the parents, they are taking care of major aspects of the cost of living and the responsibilities of the house. Even before that, when they are children, they are trained to take care of themselves and for example, they are trained to make food for themselves or they are trained to put clothes in the washing machine or they are trained to do dishwashing. Whereas in cultures such as in Pakistani culture, children, they are not given the responsibility of looking after themselves and taking care of themselves, particularly the money matters until they are holding some kind of job or until they get married or when they get married, they are the responsibility of the husband and they are not supposed to be taking care of their financial matters themselves. And even the male child, they are also not required to take care of themselves unless they get employed and if they don't get employed, they stay the responsibility of their parents and keep on being a burden on them. So that is the amount of, you know, then that affects how much you are, how much willing you are to take risk or how much willing you are to take decisions on your own and responsibility of your own decisions. That actually then reflects in your organizational cultures as well. So in Pakistan, the employees particularly they have, because they have less tolerance, they are more likely to avoid making decisions on their own and they at least need direction from above in the form of procedures, regulations or direct supervision or they at least need to share the responsibility with others. So people take a lot of advice from each other and they really can't, they are not independent to make decisions on their own that we will do it ourselves. They need some kind of, you know, somebody to share the responsibility. And that is why senior managers, they take all the major decisions with or without consultation with the subordinates and that is something which actually complements the high inequality culture because people are not ready to take the responsibility. They are not, they are fearful of that. Then people in the upper level positions, they have to take charge and that leads to the high inequality culture. So it's kind of a vicious cycle. All right. So on the other hand, when employees they have higher tolerance, they tend to be entrepreneurial and what does entrepreneurial means? They have ideas, they have projects in their minds and they are ready to, you know, take care of themselves and they are ready to take the decisions in their own hand and then be responsible for the outcome of the decisions that they take or for any project that they launch or the product that they develop, whether it's going to be a failure or a success, they are ready to take that challenge. So they are entrepreneurial and they are able to handle uncertain situations. So when there is an uncertain situation, for example, change is coming up or there is high volatility in the environment, they are, you know, they can handle that uncertain situation. They are prepared to take risky decisions on their own. They don't need the help of a parent or a friend or a co-worker or a colleague. They can take risky decisions on their own and that is why because of these things, their managers, they tend to be comfortable to let them do it because they are responsible enough. They can take the responsibility, they can take the blame, they can manage the outcome and they are ready to do it on their own. That is why the managers, they also let them do it but the role of the manager is not to actually direct or supervise but they are ready to come to their aid if it is needed, if they are doing something wrong or if things are going wrong in some direction so they can come to the aid but they are being helpful, not being directive. Then in such kind of organizations, there is generally a decentralized structure and the man or woman on the spot means the person who is doing the job who is actually responsible for conducting a particular task is empowered to do the job as he or she seems fit. So, as it seems, as he should do the job, he feels that it is a threat, it is a margin, it is a freedom that he wants to do his own job as he wants to do. So that is an aspect of how employees behave in higher tolerance cultures and how the organizations that treat such kind of people in these kind of cultures.


With a mouth sounds really necessary at the end there. Hey, welcome back to your stupid rakes and the poor man I'm Rick and he falls in Instagram Twitter for more Content thanks a patron following true to count scribe you haven't like but there's some patreon phones Which is to conscribe you haven't I did I just say those words you did you got confused and distracted because before we started recording I whacked SRK. Yeah, you did whacked off SRK right behind him Hey when that happens today, we're doing a movie review Kind of Yeah, well, you'll I'll explain I'll explain don't worry We're reviewing the new film that just came out a Hindi film and action hero Starring a Ushman and our Dost Jai deep Directed by Anarude ire Who directed I? Thought he directed something we'd seen anyways and no this is actually if I'm not mistaken This is Anarude's debut as a director helming a film. Yeah What did he do with his other stuff has done like assistant AD? Like assistant director not assistant AD AD Okay, yeah Okay, but he also wrote it as well. Yes Along with somebody else Sorry, I'm not on the right page here and Composed by say that one. There's two names for that credit. Farid Kut is the one name and Rajesh Rajarishi Sanyal Okay, and so normally with new films we do a non-spoiler review because we want to make sure people can go see it without being spoiled, right?  Yes The reason I'm saying this is because I don't know if we can non-spoiler review this We were talking about this in the car We don't normally talk in the car because we want to keep our thoughts fresh We're pretty silent in the car usually we want to keep our thoughts fresh dialogue for here So you guys can see it And I can't really tell you why because if I tell you why Because I I my thoughts aren't even completely Hashtag about this right I can't tell if I it didn't like it or if I think it was brilliant Well, see I I'm okay on that regard, but there's just the main thing Corbin brought up in the car was how do we do this as a non-spoiler because Here's what I can say in the non-spoil section And you'll for those of you who've seen it when we begin to spoil it you'll understand and you probably understand already understand why we can't but if You're one of the people wanting original storytelling in Hindi cinema. Yeah, go see it. Yeah go support it very especially very original especially if you speak Hindi Yeah, because I think along with certain things about it in terms of references One of the reasons I think that I don't know if we're fully qualified even review, right? That's film, right? There's I think somebody because I've heard this from many people people I trust as well But does somebody just put because I post about it that we were going to see it on Twitter Yeah, and he said subtitles do not do it justice to Jadip's comedic timing sure and straight-face humor I don't remember the last time I laughed so hard throughout an entire film. It's just frickin hilarious And that's not the first time I've heard that of course I laughed some I Would not even consider this a comedy So that's that's because it's lost on us and also the either the subtitles and so that's that's that's something I'm trying to get across Is that not only are there references that I don't know if we're qualified and also subtitles? There's also so much going on in this. Yeah that I Don't even fully understand What I saw and we want to hash it out and we'll talk about it in the exact way and we can't tell you that So it is we can tell you it's very very original. Yes It's very intelligent. Yeah, and it probably is hysterically funny Yes, if you're a native Hindi speaking person and you particularly understand the Indian culture of Media movies Politics and things like that. Yes anything more we say. Yeah, we'll give spoiler story needs to tell you So so I would just if you haven't seen it go watch it and then you can come back and talk to us or go see somebody Who's maybe a Hindi speaker and they have a non-spoiler review right can watch? I think they'll probably a little more qualified right this is going to be more I think of a discussion about The film then an actual full-on like our normal style of breaking down everything because I think the most interesting part to me is The entire concept of the film. It's the concept of the film I want to talk about the most so like I said Our non-spoilers just go watch it and come back form your opinion and then come see Let's talk about the film and yeah spoilers. So this is a little different than our normal stuff I'm sorry, but if you've seen it you might understand why So Not we're in spoilers spoiler go away go away if you have not seen the movie Go see the movie. I don't want to spoil.  Yes the very first sentence we would have for the film is that It's a movie that is Representative oh so many not To be taken as a real action movie. Yes.   What's going on? Why where's the where's the where in the spoilers? Yeah, I want to say that again. You should have already seen it.  Why is jideep not shooting him in the goddamn phase every time he gets shot to police officers right right in broad daylight It's because so I was like everybody's representative. Yes. It's all representative in that part. I was like, oh my god Why is everybody saying this film's amazing right? Why? I don't get it and then like obviously as the film goes on you You kind of more understood even though I was already aware I was like something is going on Is this entire thing a movie like is this like is this gonna be like I'm met in like he's gonna be in a trance He comes out and it's right. It was a big movie Like all this kind of really didn't take place. Yes, and I don't even know if anybody can actually answer It because in the second half when we he was being interrogated And they basically were the audience right and they said that's That's the dumbest thing I've ever heard right. It's not believable in the slightest. What are you talking about right? Like why should I believe you right and then obviously he comes in with you know, if you put a Yeah, maybe I did it. Maybe I didn't do it I'm a I'm an actor. Yeah, so what do you care? What do they care? Obviously this had a shit time to say about the media It had a lot to say about the media about clickbait about uh Relativity and becoming truth and everybody's just saying something because they want to say it to gain eyeballs about the power that a movie star has to Cover up stuff whereas the commoner doesn't where one day they're being vilified the next day. They're being deified Yeah, it's smart. Yeah, it's so there's a whole and like I said, there's a whole bunch in this that even us Four years into this that are more than probably most Americans Do not get all the I get a lot of the references. There's a bunch of stuff I get I'm not saying that But there's so much like I know certain things about the media like the the thing that they were playing off of in the media I like I know the the big meme of the the nation wants to know right that guy I know I know of him and hashtag. Yeah said hashtag boycott. Yeah I know of those things But I don't watch the media that media regularly and we know there were very specific references wink wink nudge nudge To very specific celebrities and things. Yes and certain things that happen I know all that but there's probably a billion other things in this film. Yeah that Completely went over made that probably made this film even better for some people. Right. Yeah. Um, and so that's why I'm like I feel like like I don't fully understand a lot of the things that happen And I just like need to kind of talk it out like what's j.i.d. actually just a reference of the audience Like or the common man And I think why was he chasing? Why didn't he ever kill him? I and I I well Yeah, yeah, why would you think like I don't I don't know because we don't know what actually happened But I think it's because the primary goal on the writing wasn't to have a thoroughly believable through line and plot as much as it was to show the representative characters Who could kind of morph in and out sometimes of who they were representing But as they represented what they represented It it also kind of tongue-in-cheek for itself showed you how The way the media and celebrity can change their narrative It did it itself. Yeah on its own And made you have to forgive it for it. Yep the way That happens in media the media. Yeah, and if I ever you see it started I thought the cops in the beginning were very funny. That was probably some of the funniest parts Like when they were first introduced he was like and the dead body on the side Yeah, that that moment was funny when he said, I love the cool breeze I love feeling this way and the dead body on the side of the road Yeah, it was funny and then like they made them get so obviously this had stuff to say about the police as well of The journalist informing the police. Yes about what they what what's going on not them Figuring out and telling the it's it was like the opposite direction So obviously this This film has a lot to say about the goings on in bollywood the media the police The audience in general and how they treat stars. You know what they believe, you know what it reminds me of I think this is the best way I could give somebody an analogy of it So you've probably seen these on tiktok on twitter on youtube They're the the the reporters. Yeah who go to trump rallies. Yeah And ask the questions And the people there say things that are definitively contradictory But they don't see it and the reporters agreeing with them nodding their head and all the time You know that there with us the audience going Oh my goodness. That's what this film is to the entertainment and celebrity and politics In india specifically in india in the same like I wrote down some films that we have an american cinema that that fall Far more into the dramatic realm. I don't know of a film that's like this, but say like American psycho. Did you see american psycho with christian bail? No, that's actually one i've never seen one of the big classics. I've never seen obviously fight club.  Yeah, it's representative. It's not supposed to be a real story a little yeah It's a it's supposed to be representative of the cutthroat capitalism of american society at the time it was made But it's far more straightforward and linear in its representativeness than then this is well, this was also Since india and it Its action films are incredibly unique Yes, especially like with the introduction of the big south mass films right that it's also playing off of with the hero worshiping So that india and their action films are very specific Yes, there's that's why west is going crazy for rr. They've never seen anything like that right ever um And so that it's a very unique circumstance Which was funny because I felt As it pertains to the action sequences I thought they were kind of underwhelming. Yeah, I think that may have been on purpose And I think it was on purpose because one of the underwhelming points and I think this was on purpose every slap punch kick and elbow Used the exact same sound At the exact same sound level Except for the opening scene where he's filming it and there's no sound Yeah, and it was so funny to watch because you're not getting the sound and I thought at one point that's It's gotta be intentional and we're not really seeing any mind boggling usual stuff Which I think was another kind of like the other thing Did you notice that this was a predominantly Male film like the only female character is one of the news people And the item girls in the song numbers. Yeah, there's there's no heroine. There's no mother There's no daughter. There's no sister. There's no love interest. There's no women. There's no female cop chasing him There's no women. I think there was one female British police officer For a split hot second. Yeah. Yeah, which is another thing of okay. You want patriarchy. Here you go Here's all the guys here's all the guys and all the mental all the roles I think that's another thing with the indian action films are the very very very male heavy Yeah, and and male focused. Yeah, which was another reason why in the item number It was like Welcome to bosom city.  Exactly because this is not real I know this is not real um Or supposedly we're right. It's a story that somebody's telling a story representative. Yeah.  Yeah, I didn't know one if we were supposed to know who that person was right So if that was a real reference once again went over my head me too I just I was like, okay. It's a terrorist and I know that Actors and Bollywood's throughout Its history have performed for rich people and gone to parties to dance and perform for people who are supposed terrorists Yes, and but also the the tie-in of the mafia. Yes was in there I know all that and so I cut all right, but like the fact that like It was it was like incredibly almost meta like that whole sequence happened You don't know what's going on then he goes into a party And then he knows the dance already and he's like this is incredibly meta That got really meta at that point Of the film and then he started talking to jai deep asking jai deep if he's the audience I'm like, is this just going on in his mind? Yeah Well, that's the thing with jai deep if jai deep is representative of the audience, right? Who is trolling and saying we hate this and has every opportunity to kill it He doesn't but never does yeah constantly even when he's shooting it at point blank range at the party I think that's one of the funny moments I bet the audience is when he shoots and then the next shot you see them sitting side by side I bet audience is roar. Yeah over the fact that No matter what happens. He's going to complain.  Yeah, and This review is very strange to me because very very I'm not going through like the different because I don't even know if I Deep and uh, uh, good job. Obviously jai deep. He has such a screen presence. I love jade break Um, but like it feels like a film that I can't even Break down like that because I'm just having to break down What the film is and it took it took a while to unveil itself to me in terms of I really at at the front half of it, which I think was intentional Felt like we're watching an action film and not a good one like because it's like a bunch of unbelievable It was unbelievable stuff and it also pacing wise was boring me I was just sitting there waiting to find out Why should I care about this? Why didn't I need to shoot? Yeah, why didn't he kill you? Is this just poorly written? And then when I realized like when you leaned over to me and said, what the hell is going on? Is this meta? I was like I was in the same place thinking Something's going on. There's gonna be something representative. Yeah And and because of that and because I think so much was lost Yeah, I was bored in a lot of in a lot of the film I get that. Yeah for sure. Uh, it was more like I almost felt like you at times because you always harp on Films for doing something that strains credulity Right, and it was like so much right. I knew it was like god. I feel like rick I knew this was absolutely intentional. Yeah, uh, like that's how I felt and I was like, I don't want to feel like rick. God damn it But obviously like it's I'd be so interested to actually talk to In indian who got to who is from india and it lives with all this media and lives with All these references. Yeah real stories that is probably being portrayed on screen that that they're seeing every day in their life And so like to hear their take on it and what all these things meant and I think it's a very smart film. It's very smart Really really intelligent and very subversive. Yeah, so I I I do enjoy that It's better. I don't know if it's meant for Non-indians. I agree. I don't think you can We can't get yeah, we've been in it for four years. Yeah, we can't fully get it Not to say that like it's a bad film at all because I think it it did what it's supposed to right But in terms to I think to really get this film you've need to have been An indian immersed in the media immersed in the culture immersed in all these references Yes for your entire life agree to really fully appreciate this film that I think I think That's why I was bored through a huge portion of the first half was because I don't get that stuff. Yeah, I will say Because I think we understand where he was coming from to make this and his intention um A film that I did not like that is also supposed to be a subversive Parity representative thing was don't look up. Okay. Yeah, this is way smarter than don't look up Don't look up was I just didn't understand why it had to be so dumb And if which is saying a lot considering it's seen that film. I still never saw it It just there's a lot of people who liked it and there's a lot of people. I've talked to who felt like I did which was You had a great opportunity to say something and you made it just a big This was intelligently Doing the proverbial reporter at the trump rally Where as I felt like don't look up was just mocking everyone who doesn't believe the way the creators of don't look up believe Okay, even though they're right Because it's about climate change. Yeah, even though they're right it was very Uh, just If you don't see it the way we do then you are worthy of being mocked and made fun of in a way that really wasn't Intelligent whereas this was really really smart and I bet if we knew even more of it culturally We would think was even smarter and dialogue wise. Yeah, exactly if we spoke hindi Um, I think we would have gotten a lot more references and also I heard somebody say like there were song references Right when something funny was going on that the song made even more funny. I'm sure I didn't catch that certain stuff Yep, and so that's why it's why I said what I said in the beginning I was like, I don't know if we're qualified to fully review this film even though I thought the the background score was Good and also I think it did what it was it did what it was playing off of the big action Like every time jay deep entered it was almost it was actually almost adam's family Ask the it was his theme his theme and anytime somebody dropped a truth bomb There was that Very kgf very kgf and there were moments that were very mission impossible It really was kind of an homage in many respects Probably far more than we know. Yeah to action film soundtracks. Yeah Um, and it's even though it's not the smartest movie that ever bored me Without question It's the smartest movie that ever bored me That's funny. It's my fault that it bores me because I think I'm just ignorant.  And I didn't care and and later in the film the guy was like You can't even punch me. You can't even break through a window your your action film my foot Action here on my foot So obviously there was a bunch of this stuff and then he could do it again and he couldn't do it again It's like there's so much meta Going on meta references that I think if if I got it more more of the humor in this because I this film if if you're laughing throughout I think that makes it a lot better. Of course And like I said, I did laugh some But I would never call this a laugh out loud film It's probably what people thought didn't get I I bet it would be comparable to somebody not from america Watching blazing saddles Oh, yeah, and thinking it's a lot more offensive that one. Yeah and thinking I don't get it. This is kind of offensive Whereas we would be dying laughing getting all of the references. Yeah, and it makes fun of hollywood as well in that That film is meta. Yeah, I just rewatched that. Yeah I think I also saw gene wilder say it's probably the most important film Yes done because it was talking about racism. It was and putting it on itself. Yes But even like the sequence the sequence of them sitting around the campfire eating beans and farting So everybody can laugh about that because they know that you know that but not Not everybody would understand that the quintessential western trope among many is Everybody sitting around the campfire having their food and he took it to the extreme of well, if they're all eating beans Why aren't they gonna have farting like crazy? Yeah, so right. Yeah, I think I think it's comparable whereas you you can fully appreciate blazing saddles as genius when you understand american culture at the time And no same thing with this after seeing this film now and seeing the full film and what it's about It's very eushman. Oh my stars. Yes. It's a hundred percent the kind of film that the fact that it has starting in it Should tell you a lot. Yeah, it has a big message It not as it says a lot that actually drops in there too. I love the fact that I love the line I did love that line when he says wait, what do you think I should do? Don't say you ever met me That was a great moment. I love that he was uh, I love that he was animated as a cameo and all that So you guys can let us know what you thought about this film and if there's more that You can explain explain it to us. Please. Yeah, show us What we miss and all that kind of stuff and I hope you I hope you did enjoy it because that's actually The predominance that I've heard of this film people loved this film that actually went and watched it It's not great at the box office But I know that people that actually watched it and that I trust Loved this film and so obviously I think if you're a hindi speaker or you can speak hindi and you've been in the culture I think you'll be able to get this film Probably quite easily and get all the references probably if you if you're big into bollywood and and you've been too bad and drawn He didn't see it with us. She might have got a probably would have picked up on a lot more Yeah, absolutely. Yeah, um, so you guys can let us know what you thought about the film Uh, and what should be the next film we watch down below


You can go ahead and just go go at the top go back to add accounts and you can just add another one right here. So, and what I would do is I would, I would know hit cancel. It's not this one. What I would do is I would, I would, here's what you, here's what you want to do for sure is is you have one. You have this specific business manager at instance real estate investing. This is the business manager, right. And then you have this one at account. We have four more that we can create. Or, or I don't know if that hide close one click on that hide close that might, that might be one of your five. So, I think it is one of my five. Actually know the height so there's nothing there there's nothing to hide. I thought you had like an old ad account that you were hiding. They just trying to delete, but that's fine so now now we should have four more that we can work with the other four should be still in the same niche. Yeah, no, yeah, I thought what can I just name it like just Edison real estate investors one, two, three, four, five. Yeah, yeah, perfect. So go ahead and click on add and you're going to simply create new ad account. No, not that one. I don't know. Okay, wait. Yeah, hit the ad create new. So it's the third one down. Oh, okay. Yeah, I got you. Yeah, so go to name this one right here name it to or whatever. Yeah, I was going to name it to perfect. Okay, cool. So go ahead and change that time zone as well change the time zone. Go back change that time zone. See if you can find yours. There's a shitload in there. But see if you can. What do you central time. Mm hmm. Okay. For me. So, perfect. Okay, so hit next. And then all we're going to do is my business. Yeah, for my business. And then and then create and it should ask for payment info. Or actually go ahead and click on your name. And then admin access to the right. And then assign. Okay, cool. And then add payment info. And it's not even just for seeing that is that in the files of the. No, watch out. Go to go to go to the course real quick. Do you have it saved. Yeah. I think it's here in the business settings like this is where again in the course that that's what I learned as well as do everything through here. So click on the drop down the top left go to that new account. Actually it's in this one so just click on add accounts. Just click on add accounts. So now you should have this new one so click on number two. And then your name, your name is right there. Now we could, we could tie a pixel to it the old pixel, but just create a whole new pixel. So let's create let's create a whole new pixel. So let's just go to, let's go. I need to do it right now I don't think I just wanted to make sure it was set up just in case. Perfect. This went down, and then I can still make more accounts. Yeah, you'll make more accounts. And then and then again each account will have will have its own pixel and not and to do that you'll just simply click on the dots at the top. The events manager. And then hit the drop down find number two. And then you would create the pixel from from there so actually maybe the top right hit the top right and go to the number two. Yep. And then over here. It'd be a isn't it a data source. Yeah, go to and click on the blue and connect the data source. And then you'll go, you'll go. Perfect. So you click on web and then and then Facebook pixel right here, and then connect, and then you would, you would add the pixel you can name it right there. Yeah. And then you would just hit continue. And then, and then you would get the pixel code and add it to a page. And then once you add it to a page, you open up that page that Facebook read the pixel for the first time. Then you come over here and create a custom audience or a custom conversion. Okay. Okay, so. So that's what you want to do the what is yeah and then the custom conversion is just to the custom conversion is a thank you page. It's letting Facebook know the ultimate destination when you run that custom conversion is the ultimate destination. Yeah, the custom conversions a thank you page. Yeah. So you would create some problems with affiliate marketing is because there is no, I guess custom conversion on your end your custom conversion is the landing page. No, no, we've got some conversion. No, no you do you do but what we're having what a lot of people are having trouble with is is the is the so when you click on the dots it's that new aggregated freaking thing where when you click on it. So go to go to just click on the dots go to events manager. So what a lot of people are having problems with and what I've been having a problem with for the last literally literally two weeks, hit the drop down the top right go to the other one. Yeah. So over here.  So I'm guessing is that where you would also add your new domain, if you were going to be doing. No, that's in the business settings. Okay, but yeah, you got to do that part first and then you can come over here and do this part but what what I've been having a hard problem with is that when I hit that drop down. Everything shaded just like that and I couldn't choose anything. Yeah, so that that's the hiccup that a lot of people are going through is that when we hit that drop down, we can't click on anything. And nobody knows how to fix it.  So, oh, so I probably got to wait like two weeks or something probably. I don't know every every account is different man I don't yet you have to wait yeah you definitely have to wait. And that's what what we're all experiencing. So, but in your case you're able to set up but see here's the deal. In your case. Yes, the custom conversion is your thank you page. Yeah, so that's why you're able to set it up and that's why you're able to choose it here. But if we're going off of the pixel, which which in on the Commission hero side where we're sending them the pixel. So we're sending the vendor our pixel. So yeah, so they're how they take that pixel and put it on their thank you page. And once they do that, then they do that and then we should be able to come over here and hit the drop down and choose our pixel. That's been the hiccup a lot of a lot of us haven't been able to choose that pixel so even you hit the drop down you still can't choose. Yeah, yeah, which which I don't know why it's it why I mean. And so I guess is there an unlimited amount of pixels they can put onto a page. No you can put as many pixels on a page. Yeah. Yeah that that doesn't matter but um. Yeah, just again this whole iOS man it's just Facebook is just going through a funk right now. But but at least you have your other ad accounts and you go in and that's why in the future what you'll want to do is is yeah try to get multiple credit cards. And even if you even if you just open up a few different bank accounts even if you go to your bank and just open up a few different checking accounts. And that's one way to do it. So it doesn't necessarily have to be a credit card it can just be a debit card that you have maybe have three different accounts at your bank three different checking accounts. You know, with the same card. Or what the no a different would be it be different cards, different cards, or or you can look at that. I haven't fully fully looked into that that one that you know Robbie's recommending. I think it's like a good bet as well this is going through some type of service like that. Yeah, that sounds good too. No yeah that definitely be something I'd still want to like, completely understand how to do everything like the basics first. Yeah oh yeah for sure bro take your time with it take your time. But setting up multiple ad accounts is definitely what what you want to do so that's that's how you do that for sure.


This is stock number 9554. We are here at Summit Automotive in Fond du Lac, Wisconsin. Your new and used sports car headquarters. Today we are checking out this super clean 2012 Dodge Challenger SRT8. This vehicle has the 6.4 liter V8 Hemi motor which pumps out 470 horsepower. From this HD video you will be able to tell that this car is in really nice shape considering the age and miles on it. It has the HID headlights, the LED, I'm sorry no LED run lights, but the HID headlights, the factory fog lights, front valances in excellent shape. This one comes with the 20 inch painted and polished aluminum rims. Also has Cooper 245-45R20 tires on them. It has probably about I'd say 70 to 80% of the tread left. These are RS3 G1s. Brembo brakes. Red line pearl coat is the color. You shoot all of our videos in 1080p so if you have HD capabilities on your computer tablet or smartphone device turn them on right now because it's like you're right here looking at the car with me. You can see that metal flake in that paint. Really good looking color. Passengers side rim is in really nice shape as well. No major scuffs on that one and as you go down this side of the car you can see just how clean the body is, how reflective and nearer like that paint is. We take these HD videos so if you are far away or even if you're close by and just debating on whether to make the trip down or not. You can see the car, hear the car and have confidence in the vehicle that you're looking at before you even get here. Has a power sunroof so you can make a smart decision on making that trip down. Really clean down this side. Back rim is in excellent shape as well. You got the Brembo brakes in the back and you can see just how much tread is left on these tires. They actually look like they are brand new tires. Yeah you can still see the little wear knob so those are new so that's how much tread comes on these new tires. Sports guys are a little different than truck tires but rear bumpers in really nice shape as well. No major cracks on that. Has the deck lid spoiler. The deck lid is in really nice shape. The trunk area is very clean as well and this one does have the Harman Kardon sound system with the factory subwoofers. You have the entire inflator kit and of course the batteries back here as well. But very clean. Those seats do fall down for extra storage. I like that it has a little button right here to get into the trunk. That's a pretty nice feature. Don't have to have the key or jump inside the car to open a press button or pull a latch. As you go down this side of the car just as clean as that passenger side. For full disclosure this back room is in pretty nice condition as well and that tire is just as much tread as the rest is brand new as well. But very very clean down this side of the car. I didn't see any dents or dings down this side. Inside the SRT8 package gives you the black leather and suede seats. There are no rips or tears on the seat. It's a leather. Got the red leather right there. Back seats are in really nice condition as well. No rips or tears back here. It has the latch child safety system. Factory floor mats down there. And we'll hop inside here. You can see that this car has 18,430 miles. You get a compass and outside temperature display as well as a radio display. The SRT8 white faced instrument cluster is in really nice condition. Has the leather wrapped steering wheel. Is the SRT sports steering wheel. Got cruise controls on that side. Bluetooth audio and information center controls on that side. This one does have the factory navigation system in it. You have your heated seats and heated steering wheel button as well. This one does have a push button start. Six speed pistol grip shifter. Pastiner seat is in really nice condition as well. And we have a factory format on that side too. Up here you have your homelink buttons for your garage door security systems and lighting systems. And of course we have the power sunroof up here as well. We will take a quick look under the hood. Has auto headlamps as well. Power windows and power locks. Very nice car inside and out. Has the cowl induction hood. Under the hood we have the 6.4 liter 392 hemi. Engine bay is very clean runs very smooth. This car has been fully safe and inspected by our service shop. Has a fresh oil and filter change. All the fluids have been checked and tapped off. The car has been gone through mechanically 100%. Has four brand new tires on it. And is 100% ready to go. To see more pictures of this Challenger or one of our other 400 new and used cars, trucks, SUVs, minivans, Wranglers, sports cars, do you name it? We got to go to our website www.SummitAuto.com. Full pictures and descriptions of every single vehicle on our lot. Videos of every single used vehicle that we have all at SummitAuto.com. Thank you so much for checking out the video.  Ask for one of our sales associates to make this car yours today. Once again that number is<PHONE_NUMBER>. Thanks again for checking out the video. If you want to check out more go to youtube.com slash Summit Auto. Remember to like, subscribe, and share on this video and all the videos you see there. In fact in a second you'll see a link to subscribe to our YouTube channel on your left. A link to more sports car videos like this one on your right. And if you have not been to our website on the bottom link to this car on our website click those check us out. And we really look forward to helping you with this super clean 2012 Dodge Challenger SRT-8. Thanks again.