Well good morning everyone and thank you for attending the 2021 CNCF KubeCon North America Cloud Native Wasm Day, which is quite the mouthful. On behalf of both the program committee and all of our generous sponsors, it's my sincere pleasure to welcome you to those of you that are here in Los Angeles, and certainly all of you that are watching on home or later online. My name is Liam Randall. I'm the founder of Cosmonic and a long time cloud native participant and enthusiast. I've worked with a number of large organizations to put different technologies into the CNCF. I'm a big believer in the power of open source and the power of community to make a difference. And to echo Ralph's theme, today's story is really about us all working together to make the future better together. So let us begin with where cloud native development is today. This is by some measure the third great epic of computing in the last 20 years. In the first, organizations raced to digitize their cores and business processes. In this age, dominated by vendors such as Sun, Dell, and Microsoft, applications were tightly coupled with a specific piece of hardware. I'm sure many of us are old enough to remember specking out a specific Perk 6 RAID controller to run our applications. Innovations in both CPU architecture, virtualization, and a desire for more efficient utilization led us to the second epic. The virtual machine rose to prominence to bring elasticity and optimization to the utilization of the underlying hardware. And it's first dominated by VMware and later by the public cloud, the second epic of computing began. And the security boundary was moved from the physical boundary of the machine up to the shared CPU. And while different public clouds emerged with their own unique approaches to the segmentation and boundary, the elasticity of the cloud accelerated this transformation. And in building and managing these applications, the literal pain of managing unique individual hosts drove the creation of the third major epic of computing. The container emerged out of the pain and suffering, and it too raised the security boundary from the CPU to the Linux kernel. In conjunction with Kubernetes, this innovation unleashed the entire universe of innovation that we find today in today's CNCF. Both the changes in application architecture and the Kubernetes abstraction from a specific cloud to a portable one were gigantic, and the opportunity in the next wave of computing is just as big if not larger. So let us consider for a moment what this modern cloud native computing environment looks like. If we find this ever-expanding elastic edge or this new cloud, as Ralph just mentioned, redefining both how we need to build our applications and where they will need to run, the crisp boundaries between clients and servers are fading to peer to peer. And probably the largest question is, is where will the execution need to live? Can all the things in the far left of this image be dumb terminals and all the things on the right hold our logic and execution? The client server approach is somewhat dominant way of thinking about computing, and this entire week is full of approaches to this problem to take what we do in a cloud native way and to simply pull it towards some lower bound. And this works and will work, but only to some lower bound. As our desire to move execution close to the user transcends the traditional boundaries that define cloud native itself, we find environments and assumptions where the dependence on both Linux and Kubernetes just no longer hold. Certainly in the web browser itself, or in the venerable iPhone, will not run either Kubernetes or containers. And now, just as I've said that, I'm sure some stunt hackers are going to figure out a way to run Kubernetes inside of your browser. But do we really want to? I think you understand my point. Now, as we consider where execution will run, Ralph mentioned that article that I wrote recently around the WebAssembly being the future of distributed computing. There are a large number of reasons, many compelling reasons, that we think that the execution and logic will need to live on the edge. Certainly when we think about latency and determinism, some decisions should be made locally for performance reasons. When we think about where the data lives, large data processing means that we want to put the computing as close to the users in the edge and the data as possible. For privacy and security reasons, in cases where we want limited or deliberate autonomy, drones, manufacturing, my refrigerator, if it's disconnected from the core, it better still work as intended. And finally, I think the sleeper reason will be regulatory, governance, compliance. Those reasons, when you think about CCPA and GDPR, those are the growing obligations that we're going to have to execute under. So if we accept that in the future, that the future is distributed, that we are faced with a plethora of distributed computing challenges. The diverse CPU system architecture rising not only across the edge, but in the core of the public cloud itself. And in a world where everything is capabilities and we face new application architecture such as peer-to-peer and locality-based computing, we need to consider how we're going to solve that. And this is, of course, a security nightmare that is hardly satisfied with an abstraction that sits at either the CPU or the Linux kernel. These concepts and capabilities don't even apply or are available in many contexts where we need to run. And of course, this all needs to work when it's disconnected. So if we itemize these three great challenges, let's call the first one portability at the bottom of the stack here, that we need a format and a solution that addresses that. The second great problem is going to be the security model that we use and need in the new cloud. And the third great model then becomes somewhat a little more nuanced. And it arises from this tight coupling of our applications today to specific libraries bound to a specific capability and context. Now, tightly coupling your business logic to a specific capability will seem in the very near future as crazy as coupling your application to a specific piece of hardware or to a specific Linux distribution. And just as the cloud evolved from the traditional data center, the near future will echo the past but will be fundamentally different. This entire stack needs to evolve. So let's walk through these three great challenges that are facing the modern distributed computing landscape. So the first is pretty straightforward. This is portability with, as Ralph mentioned, with an estimated 30 billion internet connected devices. This is part of the hyper theme that will define both the challenges and the opportunities we face over the next 20 years. With dozens of manufacturers and hundreds of CPU, OS, and microcode options, we see clear opportunities for WebAssembly here. What may not be as self-evident, but it's probably more important, is that the diversity of CPU architecture is rising not just on the elastic edge but in the core of the public cloud itself. Individual companies like, excuse me, AWS and Apple rolling their own silicon is the harbinger of things to come. As nation states and other companies continue to recognize the power and security risks associated with the digitization of our lives, I strongly expect us to continue to see the balkanization of the computing landscape. Expect more companies and more countries to follow suit. So for the first problem, we have this clear fit that WebAssembly gives us a path forward. Now let's talk about the second problem. With security, the concepts of embracing a boundary like the CPU or the Linux process don't make sense on the edge. And we've seen this rise of this capability-driven security approach. On your phones, when you install an application, you're prompted for access to microphones and other local concepts to a phone. This is not a new concept. My fellow Linux security nerds will no doubt remember pseudo set cap for granting privileges at the command line. But WebAssembly's strong capability-driven security is certainly a good approach for us moving forward. So for the second grade challenge, we have another great fit with WebAssembly. So let's carry this forward. WebAssembly raises the bar, but does it raise it far enough? In French, there's this incredible word, ilan, which if we translated it, I guess it means something like momentum. But it's more than that. It's that moment right before liftoff. So when the bird's wings are out and it's about to take flight or the ballerina's leg swings spinning faster and faster, picking up momentum. And just like that moment of lift, all of the momentum and explosive ilan of WebAssembly will be lost unless we also confront this third grade challenge. The tight coupling to a physical machine or the tight coupling to a specific Linux distribution, the tight coupling of an application to a library will seem just as quaint. So what do we mean when we talk about cloud-native capabilities and tight coupling to libraries? What are these? Well, in the context of the application, these are the specific choices you make to satisfy your nonfunctional requirements. These are examples like a specific database that might bind you to a specific cloud provider at a specific time or a specific device on the edge or a message queue or a key value store. Now, the real dirty secret of modern cloud-native development, it's that enterprise apps are primarily composed of nonfunctional requirements. The private analysis that I've seen at data at large scale of applications is that, in many cases, 95% of an application's code satisfies the nonfunctional requirements. And this is a pernicious risk to operating applications, never mind operating applications across a distributed edge, because the consequences painfully emerge throughout the life cycle of creating, building, operating and maintaining these applications. From design to deployment to management to scale and the most painful to maintain all of those nonfunctional requirements across a distributed landscape requires a considerable amount of developer time and effort. And it doesn't grant you the ability to make location-specific choices. That's why I strongly believe that actor models are the near future of distributed computing. So if we're to take flight and to dance across this distributed landscape, then we need to solve this final opportunity. Now, those of you that know me know that I've been working on a project with Kevin Hoffman called WasmCloud for the last few years. This is both compatible with the current cloud-native stack, but not dependent upon. As WasmCloud combines an actor model with Wasm to run everywhere securely. But there are other approaches to this model as well. Microsoft's Dapper project, which they've put into the CNCF, also embraces this sort of abstraction. So the difference is primarily that we're solving similar problems with different opinions, but the idea remains true. That we need to break the tight coupling of applications to libraries. Now in the WasmCloud context, we really want to flip the model. We give you this actor construct that gives you the ability to build applications that are 95% application logic and only 5% non-functional requirements, letting you defer the binding of those capabilities to runtime. And Dapper offers a similar approach in computing. There are so many other considerations that come into play, why this is a good idea. As we move into the edge, these tiny constrained devices, you would sort of like the Aladdin genie moment. You want these phenomenal cosmic powers, but in living space. That's the world that we live in now. So by working together to solve the great challenges facing cloud-native computing today around portability, security, and capabilities, I'm confident that we can make progress together. Thank you very much for your time. And for more information about enterprise offerings in this space, please visit us at Cosmonic.com. Thank you.


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Hello and welcome to Dataversity Talks, a podcast where we discuss with industry leaders and experts how they have built their careers around data. I'm your host Shannon Kemp and today we're talking to Manish Suh, the CEO, founder and chairman of RELTU. With a robust catalog of courses offered on demand and industry-leading live online sessions throughout the year, the Dataversity Training Center is your launchpad for career success. Browse the complete catalog at training.datavercity.net and use code DVTALKS for 20% off your purchase. Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Officer at Dataversity and this is my career in data at Dataversity Talks podcast dedicated to learning from those who have careers in data management to understand how they got there and to be talking with people who help make those careers a little bit easier to help keep up to date in the latest in data management education, go to dataversity.net forward slash subscribe. Now today we are joined by Manish Suh, the CEO, founder and chairman of RELTU and normally this is where a podcast host would read a short bio of the guest but your bio is what we are here to talk about. Manish, hello and welcome. Hi Shannon, it's great to be on the show. Thank you for having me here. Thanks so much for joining us. So tell me, okay, so you're the CEO, the founder and the chairman of RELTU. What is RELTU and what is it, what's this company that you built and what is it that you do? So Shannon, RELTU is focused on unifying core data in real time across the enterprise and you know with this core data when you think about data domains like customer information, product information, or supplier type of information, most of the businesses run on this kind of information. This is core and central to their operations, where they want to drive growth, where they want to optimize costs and most of the companies today have a highly complicated ecosystem of different applications that fragment and silo the data and hence it becomes one of the biggest friction points in the enterprise for companies to be able to execute well and that's where RELTU comes in where we are able to provide the product capabilities that would allow customers to manage data as a product and leverage it across every touch point, across every business process, across every application or insight they're trying to get to. I love it. So very cool stuff. So you have many titles as the CEO, founder, and chairman, but let's talk about the as CEO, CEO of the company and we'll get to the you know the foundation of it here in a bit but what is it that you do? What is your day-to-day in the company? So as a CEO, my day-to-day in the company is essentially driving the strategy, making sure that we're driving and defining the right culture that we want to grow and scale with and focusing on operations so that across all parts of the business, we are able to move in lockstep and get to the targets and the results that we all desire to achieve through the course of the year and as well as as a part of our long-term strategy. Very nice. So tell me, Manish, when you were very young before you even started school, did you dream I'm going to start a company that manages data or what did you want to be when you grew up? I think I never I never as as I was growing up, I never thought of being an entrepreneur or leading a company and you know through different parts of my career you know or let's say when I was in school I was interested in tinkering with stuff you know creating things, building things and what I found interesting was that through those early formative years as well as you know in college my first job out of college data became more and more interesting and you know sort of tangible part of what you can touch and feel and work with as a part of your day-to-day business because if you have to think about driving go-to-market strategy you have to think about what is the size of the market you know you have to look at data that tells you some of that information so there is a lot of information gathering you know just using that as an example and then thinking about what are the signals that you can read out of it so it wasn't something that I dreamt of early on when I was growing up but it certainly became a part of everything that we did and tried to learn along the way where it became very clear that data was going to play a major part or a role in in my career. What did you study in school? What was the passion you're following there? Yeah. So I studied as an engineer and as a mechanical engineering student and in fact when I graduated I went and worked in an area that was all about managing power generation type of units and machinery and automation type of capabilities for large power plants and you know that experience again a lot of the automation work was first of all gave me access to you know our the ability to work with software because automation as you can imagine was tied to software engineering types of work that was associated with it but at the same time you know the critical components that had to be monitored or automated a lot of it brought us back to data and that that sort of started to form this thread of data in that entire journey. Sure, sure. So where did you go from there? So from there you know I got more interested in understanding some of the systems behind behind the business and got exposure to working on a multi-country type of an integration for business. We you know were a part of a larger conglomerate and I was working in their India operations trying to integrate it back to the the mothership in Japan where the larger entity was and you know those types of cross-country type of projects gave me more exposure to why some of the systems needed to be integrated because you know if you're a large global business you are always trying to figure out what is the total global footprint, how much business do we do across geographies, which markets are performing well, which markets are not, are based on the market opportunity, what is the type of investment we should be making in those new markets and that again sort of thematically brought me back to understanding of the systems that were in place and why they needed to be integrated and by the way data was sort of again a common part of that fabric in this and then you know as I was working on that I got an opportunity to move to the US as a part of my work and there you know being in the Silicon Valley I got exposed to some of the newer things that other companies were doing. There was a lot of innovation going on, there was a lot of interest in creating new types of products and capabilities across a wide variety of sectors and just seeing that innovation you know I sort of gravitated towards an early stage startup where all the work that I had done in the past when I heard their pitch I was you know thinking out loud and saying to them this is exactly the type of solution that would have been useful for all the places I have been in my past and you know guess what the theme was data again bringing that data together and you know because I had worked by that point in time I had worked in large industrial organizations, I had worked in telecom industry if you think about telecom industry all the way from the front office to the back office the systems need to be integrated so that you can have automated order flow and provisioning and then billing all of those types of things happening seamlessly but all of this required that there was seamless connectivity between the systems as well as the data flowing across easily so that you could have that end to end flow and you know after living through those experiences looking for are there better ways of streamlining these processes because the number of applications or the number of different systems that number you know there was there was some thinking going on in the market in the early 2000s where some companies large enterprise software companies whether it is SAP, Oracle, IBM they all sort of came you know and said that if you had end to end our capabilities from us then you would not need to integrate all these systems and it would it would all be solved because it all sits inside one single system but those visions then pan out and most of the businesses went after a best of breed type of application landscape and you know that sort of multiplied the complexity of the enterprise landscape where you have more systems more applications more third party data sources than ever before so you know since the early 2000s I've seen this complexity grow and you know by the time we were around the 2011 type of a time frame I decided that this complexity was not shrinking the current systems were not necessarily able to handle where the future was headed so it needed a complete rethink of what types of solutions would really solve the problem for the customers and bring the data together in a useful manner for these organizations to accelerate their ability to do business so that's been you know a little bit of the journey that I've been on through my career and how at every corner at every step of the way it has somehow led me back to data as the core central team in in all of these conversations. That's amazing and so before we get to kind of the next phase which is which is thrill to you know you talk a lot about things that you started working with the different software and different applications and so you know outside of school were you self-taught where did you go how did you you know were you just following a passion where were we going to learn and and grow yourself. I think the two ways to think about it is one is yes you know areas that you're passionate about or find more interesting those often you know tend to gravitate or you tend to gravitate more towards and for me data was certainly one of those areas. The second thing you know I go back to the tinkering where you know being able to shape things and this is where rather than just abstract software type of constructs I found data as a more tangible thing to work with because you could really see the output of you know whatever you were working on in the shape and form of the resulting data that that would come out of it. So that gave me something more tangible to work with and then the third thing that I've always thought of in terms of the guiding spirit for my career is where are the secular trends headed you know are you are you picking something up and getting passionate about something that is also informed by a set of secular trends or is it is it something you know you're doing just because you're passionate about it but there's necessarily no market or opportunity associated with it. So the secular trends in this case every time reinforce that belief that data was going to be a strategic asset that everybody would have to think about and think about in a different way than where we were before and that's the type of transition that I have seen throughout my career and I'll just give you you know one one simple way of thinking about it early in my career you know late 1990s early 2000s the data professionals in a company or the any kind of conversation that was taking place about data was at the lowest levels of the organization you know to the extent that in fact even the offices given to the data professionals were literally in the basement of the building and you're laughing about it but I think yeah no I know it's not the first time I've heard that yeah yeah and and now data is being talked about in the boardroom at the C-suite level as the strategic endeavor that people need to focus on and undertake and I think this is you know at this point in time this is one of the best points in time for anybody to be in a data career right or be a data professional because this is the time and age of data that we are in. So it's really exciting for me to have seen that transition and to be in a place where you know we are helping shape the thinking of what the future can be with data. That's very cool so so then let's talk then you know you found this customer need or this company need and so you decide to start your own company I mean that's not an easy decision that's a big that's a big deal so so tell me a bit about that in the starting of of Reltio. Yeah so I started Reltio in 2011 and you know again I go back to the secular trends the number or the you know think about the silos or fragmentation of data that was growing and it was on an irreversible path because we have only seen the number of systems every company uses go up not down. The second thing was the whole focus on how businesses would need to be more digital in nature and when you think about you know digital experience that companies need to build or deliver or digital processes that they need to support there is no one single out-of-the-box application that is going to magically solve the digital experience problem. In fact you have to build it on top of data that is you know going to help drive that. So the third thing that came to the front with that was where would you build such systems because in a lot of cases you would have digital experiences that would need to be exposed to partners to internal employees to contractors that you're working with or customers that you're selling to and digital experiences cannot sit just within your on-prem four walls because you won't be then able to expose it to the outside world. So cloud was the natural vehicle that it would have to ride and be built on and 2011 was also the time when all of these things were becoming you know focal themes for every organization out there and I thought that at that point in time if this is the direction that everybody's thinking about or focused on then we will need a different set of capabilities to handle, manage, curate the information that is needed for these outcomes and that led me to the starting of relative you know once you once you get an idea stuck in your head and you can't stop thinking about it even when you're sleeping you're still thinking about solving the problem you have to go do something about it. I love it that is that is fantastic that's such a great story. More and more companies are considering investing in data literacy education but still have questions about its value purpose and how to get the ball rolling. Introducing the newest monthly webinar series from Dataversity, Elevating Enterprise Data Literacy where we discuss the landscape of data literacy and answer your burning questions. Learn more about this new series and register for free at dataversity.net. So I'm going to jump around a little bit here so with that forward thing this is we're talking about that so much you know do you see the importance of data management and the number of jobs working with data increasing or decreasing over the next 10 years and why? Shannon the number of jobs working with data are going to increase but I think at the same time what you're going to do with the data right or what type of job it is going to be it's going to improve and go higher up in terms of the sophistication you know it's almost like data is a new language we all have to know and understand it and as we learn more about the language you go to the next level of the work that you can do with it so I think a lot of the lower level work will get automated so if it is just shared data entry type of job then automation capabilities can be put to use there right if somebody is reading things off of a printed form and then typing them in those things can be automated now and technology is getting better at that process automation or if you're just you know manually massaging some of the data those types of things will get improved but what that will also do is give the same people the ability to learn something more and go to the next level of that hierarchy or you know the next step function where they will be able to apply their subject matter expertise in a more meaningful manner to the types of jobs that need to be done so you know I see jobs increasing but also the quality or the tight nature of the work that needs to be done you know moving or graduating to some more subject matter expertise that has to be gained and applied to solving these problems. It makes a lot of sense so what is your definition then of data? How do you define it? We haven't worked with it for so long now and sorting a company around it. How do you define it? That's a great question we all talk about data what is data? I think it's you know at the end of the day facts and numbers that help you make a decision and you know in a lot of cases when you start dissecting that and start thinking about what are those details facts or you know numbers then even a simple question that will get asked in a business environment is let's say it's a global organization and somebody in the C-suite level asks the question how much business do we do with American Express across the globe, across all countries, across all lines of business, across all subsidiaries that they have? People scramble they don't have a ready answer for it right? Some facts are missing because you don't have the precise understanding of how many different types of customers do we have in the American Express global family of companies and what does that family look like? Which accounts do we have there? Which ones we don't and by the way if we have different lines of products or lines of business then which lines of business have sold to those different subsidiaries and which ones have not? It just becomes a massive project to assemble all that information together and come up with that answer and both numbers are lacking as well as you know some of the core facts are lacking both have to be put together and that's the type of stuff that I think gets in the way of getting to increasing the speed of your business. Everybody today you know if you go ask any CIO or CEO they all want to increase the clock speed of their business they all want to move faster but unless they get this right or the data right you cannot and that becomes the biggest friction point. So how do you work with data at relative? I mean not in terms of the software but I mean I'm assuming that you know on a day to day I know at data diversity the irony of what we do right educating people and data and then we have to manage it ourselves for our own company right you know how do you work with it and and what are some best practices there? So you know again our core hypothesis is that you have to unify core data because number of silos or number of you know systems that you have that's not going to shrink in fact even our business we use 100 plus different applications to run our business every day but we have to unify information out of it. We have to have a unique identifier for every customer that we work with we have to know everything about that customer how we are engaging across multiple touch points what is our services team doing what is our support team doing what is going on in training you know what is our sales team doing because if it's an existing customer or a prospect understanding all those dimensions round out our perspective. So we assemble those details together in fact we use Reltio as the capability inside Reltio to run our business right so we drink our own champagne and we look at that data as the starting point to inform our decisions and you know that's something that we take very seriously because you know what we go tell our customers is that unless you have high quality trusted information you will not be able to make the right decisions and that is the same principle that we go by where we are able to think about you know what do we need to do to grow faster or be more efficient or to meet compliance type of requirements in all three of those cases it comes back to the high quality trusted information for our customers for our products for our suppliers for our employees all of those details have to be improved on and continuously curated inside our own business. Nice and I love that I love that phrase we drink our own champagne I've heard we eat our own dog food but I much prefer drink our own champagne that's very nice so you know with this what advice would you give to people looking to get into a career in data in any aspect? Yeah that's a great question Shannon because I think first of all as I mentioned the number of jobs in data are going to increase right this is this is a new language that everybody has to understand and learn whether you're in finance or sales or you know IT it doesn't really matter everybody everybody needs to have a good understanding of how to work with data and data systems and that's something that I would recommend you know regardless of whether you go become a data professional or not you should add it as a part of the tools in your toolkit that you will take to you know your career opportunity because even if you're in medicine you know think about the implications of software and you know the type of work that can be done with AIML in the field of medicine but guess what the starting point for that would be data data so this is this is something that everybody has to understand and especially you know what are the different ways in which you can have access to it what are the the tool sets that are available in the market you don't have to be experts at every part of that toolkit you just have to be aware of those tools because if you're working with a team then you would be able to guide them direct them or ask them questions in a manner that will get you get you to the right outcome sooner so you know once again this this has to be a key part of the the set of tools that that everybody has to be aware of and trained well. I appreciate that that's great advice so Manish tell me if if somebody wanted to learn more about RELTO and inquire about your product how do they find you? So go to www.relto.com that's the best source of information you will also see that we have a community at RELTO where you can join that community you can learn more about how other customers are solving you know these tough data problems especially for unification of data across multiple systems and data silos and how they're benefiting from it because Shannon one of the other things that I found lacking you know think about this we always talk about if we have better data we will have better outcomes sounds sounds like a no-brainer to all of us but when you go ask all of the data professionals how much value can you quantify how did it increase or how much did it increase your revenue or how many dollars did you save they don't have a good answer and and I think that's the part where we have to do a better job as data professionals to guide customers or guide your business our business stakeholders on what the quantification of that value is and I think that's that's going to be one of the the big things to solve for and also you know help everybody with that's going to be front and center especially in the given economic conditions. I tend to agree more and that's so great that you have a community and and have made that a focal point in priority it's so important especially I think in the data community I've never seen such a greater need and and and even desire to for for community that's something that we work on and believe here at data diversity as well so and you can you can also find me on LinkedIn happy to you know talk to anybody who has questions about what we do at help here and help them. That's amazing thank you well Maneesh thank you so much for taking the time to chat with us today and for all of our listeners out there if you'd like to keep up to date on the latest podcasts and the latest in data management education you may go to dataversity.net forward slash subscribe until next time. Thank you for listening to Dataversity Talks brought to you by Dataversity. Subscribe to our newsletter for podcast updates and information about our free educational articles blogs and webinars at dataversity.net forward slash subscribe


othe you guys welcome to episode 1 in the nivrathri series today I am going to teach you nivrathri bushchetta which is very interesting and I am very proud of it it is actually a recipe made with summer ke chawal or varni Admila which I made like cubes by pressing it and then I made it crispy and then quickly made a topping in the pan with some cherry tomatoes, salt, pepper and a little bit of tulsi or veer kadi सु��면 अऴ Phi Thay Toyota. मल्छा नहीं गद गुईशा लेसल चीर। और मेश्लूड़नर मेश्लूड़नर.


I have the community because they're passionate people. The people really care, they're just a day job and people really want to make an impact and to improve the project and to improve our lives, essentially, but that's the passion. When you see the people who share your passion, in my case, technology, the open source and sharing, basically sharing, if you can't come and join us at KubeCon Europe or North America and I know that many can't do that, remember that there are also localized events. I'm one of the organizers of the CNCF Local Chapter in Israel, in Tel Aviv and you have many of those all around the world. So you can start by joining one of these local communities and get involved and get to know the people and ask questions. This is a very good way for the in-person getting involved beyond the online one. Think about it as a small-scale KubeCon that you can meet the people, you can meet the communities and until you can join us here also on the CNCF KubeCon.


The question is, how can we translate Gita, the philosophy of Krishna, into augmenting business in India? I haven't… I have not read the Gita, so I really have not. I'm sorry because for me my own vision has never failed me. So I always kept away from all scriptures because I didn't want to clutter myself with something or the other. The only thing that I learnt and I continue to do is, if I look at something, if I look at a person, I know their past, present and future. That is the level of attention I'm paying to them. I don't look at like this. When I look, I look at it completely with all that I have. If you pay enough attention, there is nothing that will not yield. So I never had any reason or need to go looking for scriptures or something. With all respect and regard for them, it is not that I'm a worse to it, it's just that I've not had the time. How this whole process started is when I was very young, I realized that I just don't know anything. See, if you realize that you do not know, if it's just a full-scale realization that you do not know anything, paying attention will be natural because you know everything. Ah, you know this guy, you know this, this, this, this. There are assumptions and assumptions and assumptions. Well, I have used it in a different way, but even if you're doing business, even if it's for business purposes, the only reason why one human being seems to be all the opportunity seems to be going in his direction and not other people is simply because he's able to see and other people are not able to see. It's not that it's not there for others. One is able to see and others are not able to see. So, essentially a leader means that you're able to see something that others are not able to see. So, attention without intention, simply being attentive, that's what we were trying to do in the afternoon, not paying attention to something, just practicing attention, a very heightened level of attention, where an unfocused attention, but when you focus it on something just about anything has to yield, there is no other way to that attention. So, I always focused on enhancing and sharpening my attention, never under attention because what you gather is not you. Essentially, you know that you exist only because you have some sense of attention right now, isn't it?  So the basis of your existence itself is attention and this attention need not be martyged to anything. You just have to sharpen the attention. See, if you have a knife in your hands, there is no such thing that you have to cut only apples with this. If you have a sharp enough knife, you could cut anything that you want. But the important thing is the knife is sharp enough so that your attention is keen enough, not for something or the other. If your attention has become very keen, if you wish to do a certain type of activity, you can successfully do it. I think I must say something about my own enterprises at one time. My enterprise started when I was eight years of age because of a strange kind of pride in me, I wouldn't... I never ever took a single rupee as a pocket money from my parents. I started making money when I was six, seven years of age. By eight, I was quite an accomplished entrepreneur. Simple things. Even today, I'm known in Mysore because I caught snakes in everybody's houses. If I caught a snake, they gave me twenty-five rupees. In 1960s, twenty-five rupees is a million dollars for a seven-year-old, eight-year-old kid. I was just on top of the world. So there is a Food Research Institute, CFTRI. Every Saturday afternoon I went there. I caught four, five snakes. They measured it. I have to show it to like this. If it's over three feet, I get fifty rupees. Less than three feet, I get twenty-five rupees. On an afternoon, I would make hundred to hundred-fifty rupees. I... I don't know if you can imagine. Hundred-fifty rupees for a eight, ten-year-old kid in 1960s or 70s was like a million dollars today. It was big. And I... it went to so many things, just about anything that came my way.  After my university, where I learned nothing, where I passed. The only thing was by then I had crisscrossed India on my motorcycle and I suddenly realized when I went to the borders of India, they asked me for something other than my driving license. I did not even know that there was a passport. See, it's not like today. Today every child, maybe three-year-old, four-year-old kid knows he has to have a passport. I'm in my twenties, but I don't know that I need a passport. I went to the border and then they said, where is your passport? I said, what? I have a driving license. They said, no, you need a passport. Then I turned back from Nepal border and came back on my motorcycle. So my only dream was I'm going to just ride across the world. So I wanted to do something. I started kind of farming, commercial farming. I was making money. I thought I will do for two years and leave. But in the meantime, my entrepreneurship took off in so many different ways. Like this I went about and then there was some... there was an industry coming up close by. They wanted to build a water treatment plant. I happened to know the person who was running the industry. I just went there and I wanted to meet him. I was sitting in his office, they were discussing something about a water treatment plant. I just heard this and they were looking at the drawings and stuff. Then I asked him, can I apply? Can I do this? Because it is close to my farm. Can I do this? He said, no, no, this is very complex. There are some experts from Mumbai and Bangalore who will come and do it. You cannot do it. I said, give me a chance. Let me see. I took the file from him. I went and read through everything. Then I went back to him after three days and said, I can do this. He said, don't you do this, you're my friend. I don't want to do this. No, just let me give a... you know, a tender. So I... you know, those days there's something called as Navakarnattaka bookstalls. They were selling only the Russian books. You used to get big literature books. You can get Lev Stahl's toys, Warren's piece for two rupees and engineering books for one rupee, two rupees, all hard-born books. I bought ten books about water treatment plant, sat down in my farm and poured through this day and night. I made my own new design. I went there and I gave a tender for 1.76 lakhs. Mind you, this is seventies. And the closest tender was 32 lakhs. The highest tender was over sixty lakhs. He looked at this and said, you are funny. You are not going to do this. I said, no, I can do it. He said, no way you can do it. Look at this. The nearest tender is 32 lakhs and you're saying 1.76 lakhs. How will you do it? I said, give me one more day. I went back again and poured through the books. I said, I'll do it. Then he said, it's ninety days. You must finish this. And it must work. If it doesn't work, at your own cost, you must remove it, clean the site and go. And I will not give you a rupee of advance. I again went and poured through the books. I said, I can do it. I completed this in a little over seventy days. I did this in ninety plus thousand rupees. In one shot I made eighty thousand rupees, just like that. People thought this is a hit. There my construction industry started. Raja Ramana was coming for RMP plant. He's just coming in a helicopter to see where the site is. There is no anybody there. I just went and met this engineer. He said, he was breaking his head. I don't know what to do. I have to mark the site. I said, I've got pegs and white cloth. I will just go and mark the site for you. I marked it. They paid me thirty-eight thousand rupees for a fifteen minute job. I went on my motorcycle and just fixed it myself. Like this my enterprise started and we grew into a major construction company. We were going very big, but then I got enlightened. So I gave up. Thank you. Thank you, Sadhguru, for sharing that story. Thank you, Professor. We kind of absolutely run out of time. Otherwise we could have continued for another at least. Thank you for being an attentive audience.


Okay, the second talk of the session is dismantling the Outs64 Automotive Cypher, and the talk is given by Christopher Higgs. Thanks, Emmanuel. So yeah, I'm presenting dismantling the Outs64 Automotive Cypher, which is work that I did with Flavio Garcia and David Oswald. And the structure of the presentation is that first I'm going to introduce the automotive context in which Outs64 is used, then I'm going to describe the Cypher, present the crypt analysis work that we did, and then conclude with the practical implications. So Outs64 is an immobiliser solution, and immobiliser is an authentication system, which is designed to prevent vehicle hot-wiring. And the way in which it does this is a transponder, a passive device is embedded in your car key, and a coil is placed around the ignition barrel in your vehicle. And then when the transponder is brought sufficiently close to the ignition, the transponder is powered up, and an authentication protocol proceeds between the transponder and the immobiliser box. Specifically, we looked at an Atmel transponder chip and an immobiliser box from a Mazda. And Outs64 is also used as a remote keyless entry system. So these are the press button to unlock vehicle door systems. And that was where Outs64 was first discovered in 2016. So Outs64 is a proprietary Cypher, and so the first thing that we had to do was to recover the implementation. And so to do this, we recovered the firmware from the immobiliser box, loaded it up into IDA, and then by cross-referencing the patents and data sheets, we were able to recover all of the sub-routines. And what we found is a 64-bit block cypher with a 120-bit key. And so to begin with, we were quite surprised and we thought we might have found quite a secure design. I'm sure many of you have seen recently that the Tesla Model S has been using a cypher with a 40-bit key for some of its keyless entry systems. And what also makes Outs64 quite unusual and quite interesting is that it has an unbalanced Feistel network structure, and so classically Feistel networks are balanced, iterative, round-based designs, where in each round, the half of the state is changed by the output of the round function, whereas in an unbalanced design, some proportion other than half is changed. And also, quite unusually, rather than the security of Outs64 resting in the secrecy of the key, it actually also rests in the structure of the key and the security is dependent on the specific structure of the key. It operates for either eight or 24 rounds, dependent on a bit which is flipped in the transponder. And until now, there's been no in-depth crypt analysis or study of an immobiliser implementation, and that's what I'm presenting today. So this is the Outs64 block cypher. It takes as input eight bytes, and then a byte permutation is applied. The permuted bytes are then input to a round function f, which outputs one byte in each round. The round function comprises a compression function g, which takes as input eight bytes, and outputs a single byte. And then there's a small substitution permutation network, which has one s-box at both the input and output, and then a bitwise permutation in between the two. The compression function looks like this. There are two main properties, which are that it operates nibble-wise, and there are three lookup tables. And so, first of all, each byte in the input is divided into its upper and lower four bits. And then the first two lookup tables prescribe permutations of the compression function key part. That's t-u and t-l in this diagram. And for each nibble in the input, a nibble from the key is appended and uses input to the third lookup table, t-offset. t-offset outputs nibbles, and an x or sum of values from t-offset is computed for the output of the function. An Outs64 key has three components. It has a bit string, the compression function key, which is 32 bits. It has a permutation key part, which describes an eight-element permutation, and it has a substitution key part, which describes a four-by-four s-box. And this gives us a nominal 120-bit key size. And in terms of the dependence of Outs64 on the structure of the key, both the byte permutation in the Feistel network and the bit permutation in the substitution permutation network is defined by the permutation key, and the s-boxes in the round function are defined by the substitution key part. And of course, because substitutions and permutations have structure, they're not just random bit strings. The actual entropy in a key is reduced from the 120 bits to only around 91.5, based on the possible combinations of permutations and substitutions. And if we think about this a little bit more, the first thing that you realize is that the byte permutation has to have the property of being cyclic, and this is because the permutation mixes the output from the round function in the subsequent round with the other bytes, and so if the permutation weren't cyclic, then there would actually be bytes of the plain text present in the ciphertext, even after an arbitrary number of rounds. And we might also want our s-boxes to be resistant to linear and differential cryptanalysis, and a result by Sarenan indicates that there are around 2 to the 40 of these, from the 2 to the 44 total s-box space. And for a reason we'll see in just a few slides, the compression function key should not contain any nibbles with the value 0, and so perhaps the total entropy of an A64 key is just 83 bits, but this is still too much for us to brute force, it's still quite secure. And so we proceeded to do some cryptanalysis to see if we could weaken the cipher, and we focused on a chosen plain text cryptanalysis where what we were trying to do was just distinguish the output of 8 round A64 from that of a random permutation. And the first thing that we realised is that in the first round, if all of the bytes in our chosen plain text have the same value, then we can nullify the byte permutation, and so we can have quite tight control over the input to the round function in the first round. And so if there were any cryptographic weakness in the round function, we'd be able to learn the output from the first round and distinguish it from the other bytes in the cipher text. And in terms of the round function, what we would hope is that the output was uniformly random, and we would also hope that the output from the compression function was uniformly random, but because the s-box is a 4x4 component, it's necessary that the s-boxes operate nibble-wise on the upper and lower 4 bits of the byte output from the compression function, and so it's likely that this function's only going to operate uniformly and randomly if the compression function outputs bytes uniformly and at random. Unfortunately, what we found, unfortunately, is that it doesn't do this, and the main reason for this is the nibble-wise operation and the property of the x or sum that computes the output, and so each nibble in the input has a nibble from the key appended to it, and this is used to select values from the T offset lookup table, and the key nibble selects a row, and the input nibble selects a column, and so in the case where all of the input nibbles have the same value, then we fix one column in this table and the key nibble selects rows. Now, because the key schedule just prescribes a permutation of the key part and what's going to happen in the scenario is that we will compute a sum of the same set of values for both the upper and lower nibble and the order in which the sum is computed will differ, but because x or is commutative, the output byte will be symmetric and the upper nibble will be equal to the lower nibble, and so this forms the basis of a divide-and-conquer attack where what we can do is force the output of the compression function to always be symmetric, so we build the set of chosen plaintexts where each plaintext has the property that all of the nibbles have the same value, so all zeros or ones all the way through to 15, and what will happen is the compression function will output a symmetric byte, the S-box will operate nibble-wise and we will still have a symmetric byte. The bit-wise permutation will typically remove the symmetry, but not in the case where the input to the bit-wise permutation, all of the bits are one or all of the bits are zero, because no matter how these are mixed it will have the same value, and so for at least two out of the 16 plaintexts we will have a symmetric byte in the output from the first round, and this forms the basis of a probabilistic attack that allows us to distinguish the output of the first round from the other bytes, and so this is what 16 ciphertexts might look like corresponding to these chosen plaintexts and you can see that in this case the fifth column features eight symmetric bytes and so we know that this is the output from the first round, and once we do this we learn immediately one element from the permutation which corresponds to the position from the first round after eight rounds, and in the average case where there are two symmetric bytes we also learn nearly two elements from the S-box, so if there are only two then the inputs which cause them were all zeros or one bit, and in the paper we show how we can make this attack non-probabilistic. Of course, so this gives us a remaining entropy or uncertainty and the key value is still around 77 bits, but fortunately learning this element from the permutation that corresponds to the first round of encryption forms the basis of a much more significant attack in which we actually reduce the security to just two to the 51 encryptions in which we can recover the 420 bit key, and the way in which this works is we set all of the input nibbles to zero except for one target nibble which we assign each of the possible values, from 0 to 15, and the T offset table will output a zero for each of the input nibbles which have the value zero, and only four bits of the compression function key will be used to compute the output under these attack conditions, and so essentially we just brute force the remaining key space which is the uncertainty that we have in the S-box, the bitwise permutation, and just 15 possible values for one nibble of the compression function key, and so that concludes the cryptanalysis part of this talk. We found an attack which can recover the 420 bit key of 0.64 using just two to the 51 encryptions, and so in terms of the implementation, what we found is that although the default in the Atmel transponder is 8 rounds of 0.64, they had in fact implemented 24 rounds, and so they were clearly conscious of security when they designed the system, and they've combined it with the bespoke challenge response protocol in which an ID code is first transmitted from the transponder to the base station of the immobiliser box. A nonce is generated, encoded using a proprietary stream cipher, which is deterministic, and then both the transponder and the immobiliser box compute the 0.64 encryption of the nonce, and then the result is compared and authentication succeeds if it matches, and unfortunately we also found some very weak key management. In fact, the compression function key is derived to deterministically from the ID code of the transponder, and the patent prescribes that the permutation key part is actually, there are just 16 values assigned to each automotive manufacturer, and finally we found some evidence that the substitution key part can be fixed across different vehicles. So to conclude, we've shown that 8 round ought 64 is certainly not a secure block cipher, and that we can recover the 420 bit key in less than 2 to the 51 encryptions. 8 round ought 64 with a known compression function key, for instance because it's derived from the ID code of the transponder, can be broken within milliseconds, and this is because once we know the output of the compression function and we know the output of the encryption, the final ciphertext value, you can very quickly attack the small substitution permutation network, and for most key values there's very little entropy in that. And finally 24 round ought 64 is more secure, we'd expect that with a block cipher that the more rounds we apply, the more security we tend to get, but it's broken in practice owing to weak key management. Thank you. Thank you Christopher. Question, comment, no? Maybe one short question. Are you aware of some rationales behind the design of this block cipher? It's the rationale behind it. I think that it's designed to be bijective, which is not apparent from the design of the protocol, so it clearly was designed for more uses than just this. And that's evident from the T offset lookup table, which is actually symmetric about the descending diagonal, and it's why the key nibbles for the compression function key part can never be zero because if they are then all input nibbles are encoded to the value zero and you lose the information that allows you to invert the function when you run it backwards. But beyond that, it seems to be just a sort of relatively classical combination of confusion and diffusion, but it's ineffective when it's used in eight rounds. But it seems that there are weak keys in this kind of design. Not all the possible keys are of the same behaviour relative to. Yeah, certainly the permutation key part is quite constrained. It certainly needs to be cyclic. I think using S-boxes at random tends to produce relatively reasonable S-boxes. You can design AES with key dependent S-boxes and it can be secure. And did you see a document explaining the classical attacks against the black cipher, for instance, the differential linear analysis or things like that? There are no rationales about the security of this black cipher against classical... Well, we decided not to use linear or differential cryptanalysis actually because of the dependence on the key. So if we did that, we'd be exploiting, say, the linearity in the S-box, but that would be key defined and so it wouldn't generalise well to all of the keys. Thank you. No other question? Okay, we thank the speaker again.


Did you know that the largest contributor to water pollution today is storm water runoff? When rain lands on our rooftops, roads, fields, and forests, the water picks up pollutants including trash, cigarette butts, yard debris, sediment, pesticides, and animal waste. The rain and pollutants then run off into storm drains and waterways. It can take just 15 to 30 minutes for the water from your driveway to end up in the storm water that flows into our rivers and streams. But it doesn't have to be this way. How we manage our soil in urban and rural environments can make a big difference in how much water runoff we have. When water is allowed to infiltrate the ground instead of running off hard surfaces into storm drains, the soil and vegetation can filter out some of the pollutants before they reach rivers and lakes. The goal is to reduce the runoff and have more water infiltrate the soil. This rainfall simulator can give us a visual depiction of what is happening to our soils as a result of the management techniques we use. Some of the techniques we use are great while others are not so good. These examples show a forestry site, an urban area with an impervious surface, an active construction site, and cropland and pasture. The rainfall simulator demonstrates how land use affects rainfall runoff. The jugs with more water have more runoff and are poor management systems. The jugs that are dirtier represent soil erosion that is taken place because of the runoff and lack of cover. With erosion, we lose nutrients and productivity of soil that can carry pesticides and nutrients off site. When there is no soil and only a hard surface like concrete, the effect is even worse. It's important to capture as much water as we can. Through this demonstration, we can see the benefits of good soil health in our urban and rural landscapes. Good management promotes actively growing roots and keeps high organic materials in our soils, reducing erosion, runoff, and pollution. Healthy water sheds and clean water provide a better quality of life for everyone. So what can you do? Keep grass clippings and yard debris out of storm drains, don't litter, don't dump toxic chemicals into storm drains, and research your soil management techniques.


Welcome to the last and final lesson for the Shopify course. I really hope that you guys enjoyed your journey up until now. And just because this course is reaching its end, that doesn't mean that the same thing is happening to your drop shipping adventure. In fact, quite the opposite. And I'm sure that you guys already know that, but you're just in the beginning. But I really do hope that this course really helped you understand how to create that store that you always wanted, how to conduct product research and find the right products that you can drop ship and how to market your store successfully so that you can get your product out to the right audience and start hitting those cells. Whether you're sold up until now is really not relevant. What matters is that you learned and that you implemented. And that is worth much more than that ad or two or three that you ran, whether it made a sale or not, you're investing in your future and you are definitely headed the right way. Now in this bonus lesson, I want to give you guys some more pointers so you can fine tune your stores even more and start off with a bigger bang right after this course. So let's head back to our Shopify dashboards and let's see a few more cool things on the left menu, click on analytics. And here is where you are going to see analytics for your store, which means just about every bit of information that you can get on those people that are entering your store, how many people came to my store today, how many of those people that came to my store are returning customers, how many added items to their cards, but they didn't check out or maybe they did. Where are those people from the ones that are coming to my site? Which countries are they coming from? All of your customers, demographics and behavior, you will be able to find on the analytics side of your Shopify dashboard. And this is really, really important because once you learn your audience, you'll definitely know how to reach out to them with better promotions and better products for what they're looking for. And as you can see on the left side, you have your dashboard, which is what I'm seeing here. You have your reports, which is a detailed report of everything that happened in the last week or the last month or so forth in your store and a live view of what's happening right now in your store, how many visitors are in your store right now and what exactly are they doing? You can simply learn a lot from your customer's behavior and use that to your advantage. So check out the analytics for your customer behavior. Next on the left side, click on discounts. Now this is where you can create a promotion code for the viewers in your website. If you create a discount code, just click over here on create discount code. What you can do here is give a name to your discount code and then your customers can use that promo code on your website when checking out to get some kind of a discount. For example, let's make an end of the year discount code, for example. So let's say that we're at the end of the year right now and we want to make a Christmas discount for our customers. So let's make a code called Christmas dogs 10, for example, because I want to give them 10%. Then you can choose the type of discount that you want to give to them in a percentage, a fixed amount by X get Y, which means by two get one free, for example, or by 10 get two free or whatever you want to configure there. Then the value, how much of a discount you want to give using the type above. So in my example, I want to give a 10% discount to anyone who uses this discount code. So I'm going to give it a 10% discount value, then applies to all products or a specific product or a specific collection. I'm going to leave it as is minimum requirements. If you want some requirement from your customer before getting the discount, I'm going to leave that as it is customer eligibility. I want to give this discount to everyone usage limits. If you want a limit to one per customer or make any other limit that you want, I'm going to leave it as it is and set a start and end date for this promotion. I want to keep it ongoing. So I'm going to leave it as it is, keep these settings and click on save discount code. Now I can use this discount code and write it on my Facebook ads. For example, for anyone who uses this promotion, you'll get 10% off anything on our website. You can also write the same thing on the announcement bar on the top of your site. If you remember from a few lessons ago, let's go back to the site to show you for example. So I'm talking about this announcement bar up here. So you can write end of the year promo 10% off anyone who uses this special promo code X mix dogs 10. And that will do the job. Your customers will see that there's an extra discount. Everybody loves a good discount and using these discount codes is one of those ways to do it. So that's another bonus tip that I have for you guys. Let's move on to the next click on online store, then click on preferences. And here you have the title and meta description. Now, what is a title and meta description? Because you already have a title for your homepage and you already have a description of, you know, who you are, you have your about us page. So where is this going to show? And who is going to see this? A meta title and description is kind of like a hidden title and a hidden description that you will not be seeing inside your website. But if you share your website, for example, on WhatsApp or on Facebook messenger or other chat platforms, notice that when you paste a link, you're also going to get a link preview of the link that you are sending that person. So your recipient will see the link's address, but they'll also see a title and a description on that message that you sent them. But once they click on that website, they won't see that title and description again inside the website. So it's something that you're seeing before you enter the website. You can also see it on Google search when you search for anything and you see the websites results. So when you're seeing those search results on Google search page, you're actually seeing the meta titles and meta descriptions. Once you enter that site, you could see a whole bunch of different information that was different from what you saw before. So you do need a homepage title and a description for the meta description for people who are searching for your site before they enter it. You want to show them some good information about your site. So on the homepage title, let's write doggie dog club because, well, that's the title of my store. And then the homepage meta description. Your one stop shop for all dog lovers. Spoil your dogs with our special high quality collection of pet products made specifically for your dog. So you can write anything, get creative, work on this for a couple of minutes. I'm just showing you guys a quick example. Next, you can also add your Google analytics account. If you don't have one, it's easy to create one. Just go to Google, write down Google analytics on the search, create your analytics account. It doesn't cost any money. And then you'll get a code from analytics to paste inside this text box over here. Once you paste that code and click on save, you'll be able to track your audience's behavior just as I've showed you on the analytics button over here on the left, just in a much more sophisticated way with much more audience behaviors and more information on your audiences. This is something that you can go ahead and do right now. It is not only at an advanced stage. Once again, Shopify's analytics is great, but Google's analytics is going to get you much more in depth information. So once again, really quick, there's no need to demonstrate it because it's as simple as it sounds. Go to Google analytics, create your free account. At the end of the account creation, you'll get a unique code to paste over here, click on save, and that is it. Go back to your analytics and start analyzing what's going on in your store. And that is another pointer regarding Google analytics. So that is everything that I want you guys to do on the preferences page of your online store. So on our website, we are writing blogs all of the time on everything that's going on on Shopify and on the dropshipping world in general. Let me show you a quick example of some blogs that we have on Shopify. Top five low budget marketing tricks to market your Shopify stores. We are talking here about exactly what the title says. We also have Facebook ads over here and a few more low budget marketing tricks to get customers to your store. Another blog is why you should use American suppliers on your Shopify dropshipping business. We already talked about it in this course, but you have a full depth blog on this subject. Another one is 10 ways to increase your product page conversions for your Shopify stores by using all kinds of special methods that are used today by the top marketers worldwide. Next, you have another blog called e-commerce upselling, how to boost Shopify sales and increase your profits using upselling. Upselling is one of the greatest techniques to add more products to your cart and increase your average order value. That way you're going to make more profits using cross-selling and upselling techniques. All of that in this in depth blog that you have over here. And as you can see, there's also Shopify apps, which we recommend to install to help you with the cross-selling and upselling. And as you can see, these blogs are really deep and they're full of rich information that we are doing only for you guys. So use that to your advantage because it doesn't cost you any money and it's giving you a heck of a lot of knowledge, which is worth a whole lot of money. And as you know, a lot of people are paying top dollar to learn about what you learned in this free Shopify course that we created for you guys. Here's another article on how to get an improved product images for your Shopify stores to boost your sales. And here we're going over everything that has to do with product images. And believe me, there's a lot that you can do with your product images in your store. I couldn't get into all of this in-depth information in this course because then it would have been too long. But we did go over all of the important things so that everything will be optimized in your store and so that you can already start selling while on a budget. Well guys, that's pretty much everything that I have for you regarding this Shopify course, how to get your store started, even if you have no technical skills up until this point where you already have a store running and you made it this far. If you guys have any questions about this course, about Shopify in general, about our system or anything else that you have your mind on, let us know, let me know, and we will be more than happy to get back to you guys and let you guys know what's up. We really want to see you guys succeed. The best that we can do is offer you all of the tools that you need to go and get started. And that is exactly what we did in this course. So best of luck on your dropshipping journey. Stay connected to our blogs to learn about product finding methods and suppliers and everything about the dropshipping industry in general. And I'll see you guys in the future blogs and videos where you are going to continue learning and implementing your way to success.