Welcome back everyone to the SuperCloud 6 live here in Palo Alto, I'm John Furrier, host of theCUBE with Dave Vellante and our entire CUBE team from the CUBE research. And of course all of our great alumni and guests and our next up is our CUBE alumni, Deepin Desai, Chief Security Officer with Zscaler, leader in SaaS, SaaS security platform, doing some real innovative things around Zero Trust and how that impacts the new role of Genevieve AI and how that's sweeping the change of applications. Deepin, great to see you. Thanks for coming on theCUBE. Hey, thank you, John, and Dave for inviting me. So Zero Trust, obviously not a well-known topic, but it changes when you start thinking about the role of data and AI because it's a double-edged sword because data is needed for good security. And so you need Zero Trust, you got to have that market, you got endpoints, you got ingestion, now you got AI engines dropping in over the top on the new architecture. So you guys are in there. So what's the status of the Zero Trust with Zscaler? What are you guys doing that's innovative that people should know about? Absolutely. So Zero Trust is about allowing businesses access to what it needs when it needs it, right? And the hard part is the only part where you should be following three key fundamental principles. So the key principles around Zero Trust security models, if I were to call out, number one is never trust, always verify. Number two is assume breach. Number three is where you verify explicitly with least privilege access. Now as part of my role, I do have the privilege of talking to global Fortune 500 SISOs and we're always discussing this topic and it's a journey. Zero Trust transformation is a journey. Here is how I would encourage everyone to look at it when they're gauging the maturity of their Zero Trust transformation. And I'll call out the innovation that we're doing across the stage using generative AI as well. Number one is your Zero Trust solution should allow you to reduce that external attack surface. Preventing the attackers to go after your assets, exploiting vulnerabilities. Think about what we're seeing right now with the VPNs where there is mass scale exploitation happening. Number two is you should be able to prevent compromise with consistent security, with full TLS inspection, no matter where your users are, no matter where your applications are, no matter where your devices are. Number three, and this is one of the most important facets of Zero Trust where your solution should allow you to prevent that lateral propagation from happening. This is where simplifying user to app segmentation with advanced technologies like deception allows you to reduce the impact of that security incident. The goal over here is to contain the blast radius to the user or the application where that incident started. Because it used to be once you got in, you're in and you could just traverse. You're saying that's part of it. But if you go back to those first three principles, GenAI really hasn't changed any of that. Now it's brought in a new dimension. Exactly. But it hasn't fundamentally changed what Zero Trust network architectures are all about in those fundamental principles. Exactly. So Zero Trust principles remains the same, but GenITV AI will actually help us speed up the adoption of the Zero Trust implementation. And I'll explain you how as well. So the final point I was gonna make is all of these threat actors are after your data. Once they're in, once they move around, they're trying to steal data. This is where GenITV AI helps with data classification and security models. When I talked about the lateral propagation piece, this is where organizations often struggle with the segmentation implementation journey. Using LLMs, using both generative and predictive models, you're now able to implement intelligent policies that simplify, that's able to sift through large volume of data based on how the access patterns look like over the past several months, establish that baseline and quickly tell the IT and security team, if you were to implement this, here is the impact, here is the security posture improvement that you're going to get out of it. So at each of the stages, if you think about reducing your attack surface, preventing compromise, using generative and predictive models, that's where you're trying to detect breach-like scenarios where a threat actor is probing, doing recon, trying to get in the environment, as well as on the segmentation. What I like about that is that the lateral movement, that's where the damage gets done. You guys have data on that. ZScale has been very successful, the stock price, your performance has been key. You've been watching that. Do well, congratulations. But now the data that you have and a platform, that's an opportunity with GenerAi. Can you explain the opportunity and what you enable? Because if you have the platform, you've got all that security in place, now the data could work for your customers. How are you see that evolving? What steps are you guys taking specifically to make that management of the data and that security paradigm work better? Great question. So data is the new currency, data is gold in this AI revolution that we're part of. One of the initiative that I'm personally driving, this is public information, Jay talked about it as well, is exactly around what you are. So we have endpoint agents. So we have endpoint visibility. We have north-south visibility using our ZScale or internet access where we're securing users going to internet-bound destination. We have east-west visibility with the whole segmentation connecting users to the private app. And then on the data prevention stage as well, we see the data that's residing in the SaaS applications, internal application. Now combining all of that telemetry and visibility, bringing it into our AI breach prediction model is something that we're building right now. So the way to think about it is... Breach prediction. Breach prediction, yes. So you said generative AI is saying this could be a breach area. This is exactly the direction we're going in. The goal over here is to combine the visibility across the chain and rich that with the intelligence. The volume of data that we see is getting close to 400 billion transactions a day. This results in 500 trillion daily signals. All of this combined with the intelligence that the team is generating is what we're leveraging to further tune those models to predict breach-like scenarios. That's pretty large scale right there. I want to ask you how Zero Trust fits in. John was asking about stock price. I'm not going to ask you about stock price. But last... I don't think I asked about stock price. I highlighted stock price. You mentioned stock price, which was kind of a head scratcher for me last quarter because you guys beat and raised and then the stock went down. But regardless, what was interesting that came out was Nikesh from Palo Alto said, use the term spending fatigue and that set off a whole wave. But Jay on the Z Scaler call and George Kurtz on CrowdStrike said, we're not having that problem. And so you don't necessarily comment on the numbers, but I'm interested when you talk to your 500 colleagues, are they seeing spending fatigue and consolidation fatigue? Why are they seeing that? And how does and will the proper implementation of a Zero Trust network architecture address that? What are you hearing from your peers? Great. So Dave, the question is two level below the pay grade of the folks that you mentioned, but I'll share my perspective. Platformization is real. And there are two aspects to it, right? Number one is where you obviously want to reduce your cost, but the most important part over here is reducing complexity. As I talk to my peers, all of them have built up a debt of so many different security tools and these are point solutions. The speed at which the attacks are happening these days, I was reviewing one of the ransomware attack that happened last month, it took 12 hours from the point of entry, exfiltrating three terabytes of data and then encrypting all the files in the environment. Now, if you have five different point technologies that are doing things in the back end and then you have correlation happening with the yet another tool and your team is trying to respond to that, the damage is already done. So, platformization is required, this is where you need to have key areas identified and that's where, for instance, Zscaler Zero Trust Exchange helps with the four areas that I mentioned, external attack surface, compromise, lateral propagation, data exfiltration. You need to have that single platform view where each of these engines are feeding intelligence into each other and you're able to take actions at the time incident is happening and not few hours later.  Do you see a risk deep in, and maybe it's okay, we kind of saw this with cloud, we had a lot of consolidation but now you've got multi-cloud and super cloud and you've got complexities there. Do you see the risk of having point products? Yes, they'll consolidate but then you'll have multiple platforms. How do you see that playing out? So there has to be an ecosystem. Like if a vendor claims to do it all, yeah, you're running into another set of problems which I won't digress into. For instance, in our world, we don't compete in the endpoint side. There are EDRs of the world, CrowdStrike for instance, a great partner. We see it there. They're doing great on their end. There are different aspects where you need to have that seamless API integration. That's equally important when you claim to be a platform, if you're not integrating well with other platforms, then then you're not essentially a platform. So again, to answer your question, you need to know the space you're playing in and you need to do it well rather than trying to claim to do it all. Getting several different point products and then again stitching it together and calling it a platform is not really a platform. But one of the things you're highlighting that's interesting is that earlier you said you're bringing in, you're enriching data with other data. The intersection of multiple data sets or data aggregation becomes a new pattern that everyone's accelerating on because now with LLMs and new foundation models, Generative AI is about bringing data to the table and then enriching it with others. That seems to be a big part of the trend.  How do you see Generative AI impacting the customers? What's in it for them from an enablement standpoint? What is it going to enable? Yeah, Generative AI is, again, the number one piece is it's going to assist them in implementing a lot of these initiatives including zero trust adoption, where there is a fatigue, as you mentioned, because of a lot of tools. What do I prioritize? Where do I focus my energy on? With the ability of large language models to sift through large volume of data and bringing a simplified view in front of them. I gave an example of intelligent policy, policy impact analysis, generative predictive models. So it will definitely help them fast track the journey. Now, the change is bidirectional though, right? It's not just, you know, you're leveraging Generative AI to fast track a lot of these initiatives. You will need to take into account the Generative AI applications as well from the security perspective because there is risk that is associated with these LLMs. And we have seen several incidents over the past 12 months where an employee inadvertently leaked data or it could be a malicious insider. So that's where, how do you leverage zero trust to also securely enable adoption of Generative AI in these organizations? You know, Deepin, I want to get your thoughts as you talk to customers, you mentioned, you talked to a lot of other CISOs and companies. Dave and I have been covering the data area going back when we started working together 14 years ago when Hadoop was considered big data. Now Hadoop's data is key to Generative AI. There was a role that emerged called the chief data officer. And we're seeing trends now where data science and data officers are going to end up becoming abstracted away with Generative AI and the pendulum shifts to cloud ops, right? So, or DevSecOps, we're going to call it, but still data is critical. Is there a new persona emerging in the enterprise as these new teams come together to redefine or refactor zero trust for Generative AI, refactor security posture, refactor their IT? What's your vision there? What do you see happening in the field? Yeah, there will essentially be a new persona. Right now it's the CISOs and our cloud ops counterparts who are taking it on. And there is role for chief data officer even now, in my opinion, ALLMs, what goes in, what comes out, the governance around it, the security around it. A simple example that I was discussing last year with my CEO, you have these large language models. Yes, they're private. You're training that with a lot of sensitive data. What about RBAC around these ALLMs? When a CEO asks a question, and if I ask the same question or my help desk employee asks the same question to that ALLM, the type of data that's- Different answers. Exactly, right? And again, how do you train that large language model to make sure things that you don't want to be coming out is not even included as part of the set? How do you prevent hallucination? So- Well, that highlights the whole point we've been seeing here at SuperCloud 6 in this AI innovators section is that the way things were before, observability, network management, application performance management, DevOps, are all changing. It's not the one thing. They're not stovepipes. They have to work as an integrated system. That seems to be the pattern. Do you agree? 100%. Yeah, I mean, if I could apply the platformization concept on the role and personas, you're right. Because we're all, again, we're no longer the house of Mr. Doe's. We're seen as business enablers, but how do we do it more securely? And that's where those personas have to work together. So you've brought up the concerns about LLMs and hallucinations and inconsistent answers. How far away do you feel that we are from things like LLMs and other AI really dramatically changing the SECOP analyst experience? So on the security side, we still have time. It's nowhere close to replacing a security professional. Now it will help the cybersecurity professional to scale and be effective, be more efficient because it's able to scale through the data. So you're now able to make data-driven decisions in matter of split seconds because you have this thing, giving you the options and you could call out what's good, what's bad. But at some point, I do see it replacing certain tiers of what SECOP's SOC activity involves. For at least filling the gaps that can't be filled today by humans because there just aren't enough of them that are qualified. 100%. Yes. The final question for you. Give us the state of the art in terms of Z-scaler capabilities. As General AI unfolds, people start to get visibility as a certain use cases. Certain things are emerging and evolving fast on the infrastructure side. What do you guys see as clear line of sight that's stable? Where's the growth? Where's the action developing? What's your thoughts on how you see this rolling, playing out from a security standpoint? Right. So I mentioned about the use case which I'm super proud of. We're also heavily investing in co-pilots, LLMs that will enable organizations to securely adopt Genitive AI as well. Everyone realizes the benefit, the productivity, the efficiency, the efficacy. So it's not a matter of if it's, everyone is on the journey of adopting several different Genitive AI applications. So over the past 12 months, we've added and we continue to add more features, enhancements to our platform that allows organization to securely adopt Genitive AI. A simple example I can give you is you've seen cases where an employee would take financial information from internal document, try to ask a question to say, chat GPD in the prompt, you're now able to apply granular access control policy with data loss prevention engine to prevent that kind of data leakage from happening. Final, one more question just jumped in my head. I got to ask this one. What's the most important thing that you think people should pay attention to that's not obvious in the press and in the news today? What's out there that's important that no one's really talking about? What would you say? The folks have started to talk about it. I'll mention two things. One is the shadow AI aspect, right? It's one thing to define these policies and hey, you're allowed to do this, you're not allowed to do that, but having those effective controls in place that gives you that visibility and ability to enforce those security control is very important. Shadow AI is going to become more and more menacing in many of the organization leading to the breaches. The other one that... Shadow AI means what? Shadow AI is where you're leveraging unsanctioned AI applications inside the platform. Like shadow IT. Like shadow IT, exactly. In the other area? And then the other one is where we're seeing more and more threat actors also starting to use generative AI, be it wishing, you saw an example show up last month, be it fishing, be it ransomware attacks. So this is where security professionals will have to leverage AI to fight AI, right? In some ways. If you guys can get prevention, preventive, generate, I predict breaches, the bad guys are going to predict the areas to attack. Absolutely. So it works both ways. It does. It'd be great to have you on. Again, congratulations, Escalade. And thank you for being part of our super cloud journey. Jay was on our super cloud too. And great to see you. Thanks for coming on. Thank you. It's always a pleasure. Thank you. Okay. More super cloud six coverage. I'm John Furrier with Dave Vellante live in Palo Alto. Stay with us. We'll be right back with more live coverage for super cloud six AI innovators after this short break.


Hello. There's a number of reasons why I'm uncomfortable being called rap genius that I am rap genius doing my thing. One of which is that I just started working full time for the company a month ago. I'm not one of the founders. I was hired to be the sort of, well my official title is Education Czar. And so I'm new at this. I'm new to this kind of community. In fact, I had no idea about this conference until about a week ago. I was at MIT and Jamie told me about it. And when I told my bosses, they're like, oh yeah, we should have responded to those emails from Dan and you should go out. So I also did not know I was going to be presenting today so I didn't even have my six hours on the plane to rethink anything previous. But in any case, actually I am going to start off with a little bit of my own story just because, not because I had any hubris, but because it is demonstrative in certain ways of the ideas that came up to my mind in the morning in terms of engaging different communities because I was hired out of the rap genius community. I was working on the final chapter of a dissertation in English at UT Austin and I'm a hip hop fan and the final chapter was on the concept of the hip hop novel. So I was spending quite a bit of time on rap genius, productively procrastinating by annotating all my favorite hip hop artists and albums. And I had worked in the digital writing and research lab at UT Austin. So I had a mind, you know, an imagination for digital pedagogical tools and so at some point I was playing on rap genius and I realized that this would be an incredibly dynamic tool to bring into my classroom and I had no offense to anyone in this room had heard about annotation technologies, had been exposed to some of them but had not found anything compelling or in many cases never knew that any of the things that you guys talked about this morning even existed because they weren't there for the taking in the way that rapgenius.com was there for me to go ahead and add whatever I wanted to their database and so I added the great Gatsby and I added poems by William Carlos Williams and I added essentially my entire American literature curriculum with a few exceptions that were under copyright that I was nervous about and had my students in the classroom annotating these texts using rap genius. Now of course they thought it was pretty cool because it was you know rap genius in the classroom but the technology was there a really powerful tool for them to play around with and to really embody what was mentioned earlier this morning in terms of web 2.0 empowerment making them producers rather than consumers no longer reading the footnotes of a Norton anthology but in a sense creating our own Norton anthology with our own head notes and our own editorial decision making so at some point you know I was in touch with the guys from rap genius they were really excited about what I was doing and again this was not anything particularly innovative as everybody in this room knows you know scholars are interested in annotation teachers are interested in annotation so I don't consider my move at all brilliant in any way but the one sort of ridiculous thing that happened is that I ended up not going on to be a professor and yes becoming a rap genius so a little bit about the history of rap genius and many you know there's some visibility in the news but started off annotating hip-hop lyrics anybody know what the reference here in the Kanye West song is to yes there in the back that's right it's a remix of a Malcolm X speech by any genes necessary by Kanye West and you can see that dynamically demonstrated on the on the site here by clicking on an annotation and getting a little explanation that comes up there and so there's a this is where the biggest hip-hop site on the planet and have a huge archive of hip-hop lyrics much better than the Yale anthology of hip-hop lyrics which came out about it a year or two ago under much criticism because there were many transcription errors really know scholarly apparatus involved but our site of course if you see a mistake here you can immediately if you're a user correct the transcription error and of course there's this huge communal scholarly apparatus that surrounds every single text so for hip-hop scholars it's a really great tool but for hip-hop artists it's a great tool as well we're starting to verify artists to get them signed up for the site with a unique account which gives their explanations and annotations a different kind of visibility on the site and it's becoming more and more so with the exception of Jay-Z and Kanye West almost all the big-name rappers are coming to the site and creating creating these accounts as a part of their promotional tools along with Instagram and Facebook and Twitter and things like that Kendrick Lamar who you see up there probably released one of the most popular albums last year in hip-hop Nas no jumped ahead of Nas yeah Nas is a one of my favorite rappers he's verified on the site he's actually an early investor in rap genius you can see here how his annotations differ from the community's annotations with this particular highlight so he's got an account and his fans can follow him here very quickly this spread across the globe and so we have French rap on the site this French rapper actually cites rap genius in his rap sort of meta moment in the history of global hip-hop but of course we moved quickly beyond hip-hop and into rock and other musical lyrics so you can also search rap genius for rock lyrics for indie rock lyrics for classic rock lyrics like this eagle song and learn that Colita is a reference to sweet sweet marijuana if you were ever curious about that so we've also launched a literary initiative called poetry brain which has its own domain as a blog currently but eventually probably will be a separate vertical that houses our literary content and the blog is pretty great you should follow them on Twitter you should follow them on Facebook and keep up with the blog there's a lot interesting sort of cultural news items that are coming up here also highlights the the literary content that's going up on the site it's all going up on rap genius right now so for example we have T.S. Elliott the wasteland is up here was actually referenced in the New Yorker online the other day that you know rap genius scholars and editors and users had annotated the wasteland and somebody mentioned earlier Ulysses so Ulysses is being annotated currently on rap genius and I invite Catalina is that the name of the keynote to come and to annotate here to try to reproduce her handwritten notes online here with a community of public scholars Lewis Carroll is on the site Langston Hughes I mean the literary content is growing much much faster than the rap content right now because we can go back into history and add all those you know novels and poems that are in the public realm along with verifying rappers were also verifying novelists and poets so this is a book that came out in January by Adam Mansbach called the Rages Back and we got in touch with him and he put his first chapter of the brand new book on rap genius while he was on tour and you can see his green annotations here really neat sort of behind the scenes explanations of what he was doing in the chapter but fans have also come along and annotated as well you can listen to the audiobook on the right here and there's a little headnote that provides links to Amazon so you can buy the book same thing with poetry got verified poets going on in the site just this morning we launched a chapter from Tom Wolfs that's without an E Tom Wolfs novel sound on the site and FSG here is tweeting about the fact that a chapter from this guy's book is on rap genius that says something about the the publishing industry and their responses to technologies like this the Bible is on rap genius the King James version of the Bible is on rap genius and there's even a sort of table of contents and guide here that the community created to help people wade through the Bible American history the Mayflower compact all these dudes you want to know what they look like you can go to rap genius and see here's my little standage whoops what else do we have we have obviously lots of speeches from history somewhat ironically digital Millennium Copyright Act is on rap genius annotated by Stanford law professor Mark Lemley and that this is just one of many legal cases that are up there we see the eventual launch of something like called law genius one thing that's really hot right now rap genius is annotating the news breaking down the news line by line and so we started to publish blog entries about the news of the week statements on same-sex marriage from a couple weeks ago and speech that Hillary Clinton gave I don't know what this is some other legal stuff and gun violence acts and things like that Roger Ebert we were able to honor him and of course this week will probably have some of Margaret Thatcher speeches featured the Christopher Dorner manifesto was viral on rap genius for a while huge community of users on any site on any page you can see the people who explained the most here and you can see this is a pretty big number of folks that just logged on to the site to annotate to annotate this this this album this this lyric this you know manifesto Christopher Dorner that's a lot of folks right there Barack Obama's State of the Union all his speeches are on there I watched the State of the Union at the same time as I was reading it and annotating it on rap genius and I was not the only one there this is just a quick snapshot of my original assignment again it wasn't anything special in particular in particular but my class really the big thing they did was annotating F. Scott Fitzgill's great Gatsby and just anecdotally I don't know if it's okay for me to say this but my understanding is that when they presented to Anderson Horowitz they pulled up my students work so they pulled up student work on the great Gatsby inside of rap genius as part of that presentation that was successful presentation this is one of my students is actually an internet rap genius this summer Lewis LeFaire dynamically sort of close reading this line about the sort of invisibility of labor in the great Gatsby you know using visual rhetoric links really highly informed deeply analytic we see rap genius is the ultimate close reading tool so I'm developing a whole infrastructure on the site around rap genius and education providing resources for teachers and students to come to and check out check us out and guide teachers through the process of integrating this into their curriculum and really don't have to change anything any user can add any kind of content we could go ahead and add transcripts of this morning's talks on rap genius all we would need to do is copy and paste that material from the presenters files colleague of mine put up a movie dick which was a long book is a lot of chapters but he did something really cool it's not coming up because of the server but he had students choose a chapter and basically take editorial control of a chapter and then they lectured about the chapter in their class and then recorded the lectures and put them on SoundCloud and again the embed is not coming up here because the internet but you can listen to a student lecturing on Moby Dick here one of the cool things about rap genius is that we're this really is the unwashed in a lot of senses it's inclusive of everybody as scholars not restricting that to any academic institution and inviting everybody to participate in this way and encouraging them to do so through some pretty fun features that I don't think distract from close reading and I think add to the engagement of the very same skills that everybody in the humanities and another classrooms is trying to get students involved in close reading analysis being informed visual rhetoric this is an entire poem annotated in images imagine the kids that don't speak that much in class but really have a visual imagination in their in their reading and in their analysis have an opportunity to participate in a really dynamic way one of our most visible initiatives in terms of the education outreach is a project you may have read about called science genius that involves the jizza the rapper from the Wu-Tang Clan on Chris Emden of Columbia University where students are writing rigorous raps based on their scientific learning and then we're going to publish those raps on rap genius and have scientists annotate the high school students so that's going on the New York City public schools plus one in the house my wife is a biologist I convinced her when she was lecturing at the UT Austin to put a scientific article up and have about a hundred students in an undergraduate biology course annotate this article from plus one which I'm not going to read but if you're curious on how to find it on rap genius just go to our search engine and type in gonads this is the only thing that comes up it's easy to find in that sense it's hard to remember all these different authors but this was a pretty neat thing again look at all these kids look at all these students that have participated in the annotation of this on the right sidebar there all these are students that signed up for the site as part of this class and they're not you know adding to the scholarship so much as helping each other through a difficult text reading a scientific article for the first time as an undergraduate but doing so in community doing so with other with research assistants you know with with your classmates as research assistants each looking things up and you know some in some cases really dynamically visualizing the knowledge as a sort of teaching aid so that's one of the weirdest things on rap genius the this hermaphroditic fish article and of course we're still in a kind of musical state of mind so we have everything has got lyrics at the end of it right so State of the Union 2013 lyrics but our philosophy is that all all poetry is rap all text is rap and all all all rap is text or something like that and again I'm new so there's some weird things on rap genius we've got our own visual development going on for visual annotation of images and we've got video annotation which I didn't pull up a slide for to to to demonstrate anything but rap genius is fun that's one of the things I feel like it's sort of missing from this morning I don't mean that as a criticism again I'm totally new here but the success of rap genius on some level at least for me was that I didn't know where any of these tools were that you guys were talking about before but rap genius was there and rap genius genius was fun for the kids and there's a playfulness about it and a kind of more relaxed and it's not that it's not scholarly on every page we acknowledge the top scholars of a particular of a particular text or a particular author but it's fun you can play around on rap genius there's whip poor will I am you can also find recipes on rap genius chocolate chip cookies and so let's see who's the top scholar of grandma oops it's not gonna let me but in any case that's that's what I got thank you


the ISLM and Aggregate Demand, Aggregate Supply Model, a general framework for macroeconomic analysis. Dear students, in the last lessons, we talked about business cycle effects and we saw that macroeconomists are actually interested in this to understand why these fluctuations come, number one. And secondly, to understand how the economy responds to these fluctuations and shocks. So in this context, what we will talk about today is the ISLM framework. We will develop a framework which will help us to know all the three major components of the economy. We will talk about labour market, goods market and assets market. We have discussed these three separately, labour markets separately and the other markets separately. So ISLM is a framework in which we will discuss these three markets simultaneously. So this is a widely applied macroeconomic model in which we can analyze the cyclical fluctuations, number one. And number two, we can see the impact of different economic policies on the economy. So in the last lessons, we talked about Aggregate Demand and Aggregate Supply Model. And we will look at the ISLM framework. We can develop and derive it by staying in the same framework of Aggregate Demand and Aggregate Supply Model. So what will we do in this ISLM framework? We will discuss labour, goods and assets markets simultaneously. So ISLM is a framework in which we can define the ISLM as IS Curve, i.e. I Investment and Saving Equalities. And similarly, we can define the MONEY Market, L for liquidity and M for Money Demand and M for Money Supply. So this is a framework in which we can represent the MONEY Market's equilibrium and we can define the Goods Market as IS Curve. So this is a very useful tool in which we can explain and understand the business cycle fluctuations. So first we will discuss it graphically and then we will look at it algebraically. This tool was developed by Sir John Hicks in 1937 by Noble Laureate. And this tool is widely used to analyze the economy. So in the first step, we will discuss labour markets and goods markets and asset markets. So in the first step, we will discuss labour markets. And in the second step, we will discuss FELine. You know that when equilibrium comes in the labour markets, employment level is determined from there. And from that employment level, we have a polyimpliment level of output. So before this, when we were discussing aggregate supply curve in the last lesson, we would discuss in the framework of aggregate supply curve P and Y. Now the ISL model is that we will collect all these things in the R and Y space. From the labour market, we will draw the full employment level of output to R, R for Real Interstate and Output in this space. Similarly, we will discuss goods market in R and Y space and money market in R and Y space simultaneously. So if we plot output against the Real Interstate, we get a vertical line. Last time aggregate supply curve, if you remember, we had a vertical supply line in which space it was. Vertical axis was P and horizontal axis was Y. Today we are going to do it. Horizontal axis was Y and vertical axis was P. Now we will take Real Interstate. So if we plot output against the Real Interstate, we will get again a vertical line. Since labour market equilibrium is unaffected by change in the Real Interstate. So this is a full employment line. Aggregate supply curve was also vertical. I am reminding you again. But the difference is that we used to take price level on vertical axis. Aggregate supply curve which we used to draw in the long run. Horizontal axis was Y only. But the price level on vertical axis was Y. Now we are taking Real Interstate on this vertical axis. And we are showing this on this vertical line that whatever is the Interstate, the full employment level of output remains the same. This does not affect it. If you know that the investment changes due to the change in the Real Interstate. But if you know the investment, it will change the future capital stock. Current capital stock will not change due to investment. So the full employment level of output is determined by the current capital stock. So that is why whatever is the Real Interstate, this full employment level of output Y bar will remain the same. And you will get a vertical line. So what are the factors that can shift the full employment line? Let us talk about this. The full employment level of output is determined by the full employment level of employment in the labour market and the current level of capital and productivity. So any change in these variables will shift the full employment line. That is labour market, labour demand and supply will be determined by the employment level. And when we put the employment level in the production function, then you will get the output level determined. So the output level determined is given level of technology, productivity and given level of capital stock. So when productivity changes or capital stock changes, then you will get the full employment level of output. So what are the factors that will shift the full employment line? The full employment line shifts right because of a beneficial supply shock. That is beneficial supply shock means that the production function will shift you upward. Okay. Now what will be the factors that will shift the full employment line? If you increase the productivity of the full employment line, then you will get the full output of the full employment line. And similarly, if there is an increase in the labour supply, what will happen to the labour supply? The employment level will increase. Productivity, beneficial shock in Teeja, employment level was the same, but with the same employment, we were able to produce more than before. When productivity increased, people were giving more output than before. The second factor is that labour supply increases. People can think of any bishma and its reasons. We have discussed it in detail. So what will happen to the labour supply? The employment level will increase.  What else can be the reason? An increase in the capital stock. We have also discussed that capital and labour support each other. When the labour supply increases, then the output will increase. Similarly, if labour supply increases, then the output will increase. When the capital stock increases, this will increase the full employment level of the output. And the full employment line will shift to the right. So the full employment line will shift to the left. These are the three things we have discussed. If the beneficial supply is a shock, the full employment line will shift to the right. If the adverse shock is a shock, then the labour supply will shift to the left. Similarly, when labour supply increases, the full employment line will shift to the right. When labour supply shrinks, then the labour supply will shift to the left. Similarly, the labour supply will shift to the capital stock. So here we will revise this one more time. बनेफिशल सपलाई शुक किन तीजे में, तिक आपी लाईन रहीट वर शिफ्ट हो जाएगी. क्यों शिफ्ट हो जाएगी? तो दोस में, वजुहात हो सकती है. एक तो यह कि मोर अउट्फुट केन भी प्रदूज फर दस सेम आमाच्ट अप चपिटल आन लेपर. तो उतनी ही लेपर और कापिटल से नाव वी केन है, वी केन प्रदूज मोर अउट्फुट नंबर वाईन. और दुस्रा यह है कि जब भी प्रदूजग्ट वीटी शोग आता है, बनेफिशल और प्रदूच्ट आप वर शिफ्ट करते है, तो उसके नतीजे में माजनार प्रट्ट अप लेपर भी उजिली उस में इजाफा हो जाता है. तो क्या हुँँँँँ? के एक तो जितनी लेपर पहले थी इंप्लायमेंट, वो पहले से जादा प्रटूग्टेप होगी है. नमर वाँइँँ, उर Away की माजंद 발 क्या है से क्या लेपर स्प्फह olmasे खॉत Totally आपता है. तो मग Danny तब आपा य मीड़ाप falsch ति languages कर्थे है। अब आद ज़ेगाग, तो उसे full employment level of output उसे full employment level of output अद्वोज़्यगी अद्वोज़्यगी और ताड़ आब ने बाद की ती capital stock, capital stock मैं आप को बताए अग लेबर एक तुस्टी को. सबोट करते हैं, तो जब capital stock में इधाफ़ा हो जैगा, तो उसी labor से अब आप पहले से जियादा output लेई सकेंगे. So, more output can be produced with the same amount of labor. Number one, in addition, increased capital may increase the marginal product of labor, which increases labor demand, or employment, or उसकिंतीजे में full employment level of output बड़गे. आज हमने क्या बास समजी है, आज हमने f-e-line पर बात की है, f-e-line क्या है? अब हम काम करेंगे ISLM framework, हमने आई start की है, ISLM framework में हम तीनो market को labor market, goods market, aur asset market. इन तीनो को हम simultaneously discuss करेंगे, तीके, और ये किस framework में हम बात किस space में हम काम करेंगे, वर्टिका लेक्सिट पर लेंगे, वर्टिका लेक्सिट लेंगे, वर्टिका लेक्सिट पर लेंगे, तो आब हमने full employment level of output को इस space में हमने एक लाईन, वर्टिका लाईन बनाया, उसको समजा, यो उसी तरा की एक वर्टिका लाईन है, जैसे aggregate supply करवती, अपकी दोनो मरकिट को भी हम next lessons में दिसकर सकर लेंगे, ख़ेंगे.


we can hear everybody. So I'm Don Marty. I'm with SOADB. We're coming up on our GA release. So before I get started, who is already running Apache Cassandra, or have you run it in the past? Okay, so I'm not in production. All right, so other NoSQL systems, Mongo? Okay. So Hadoop, React, any of the others? Redis? Okay. Big time Redis and Spark. And then is the back end for your Spark for the storage? Okay. Okay. So yeah, Spark, Spark Silla turns out to be a really good fit. So some of the integration work that has been done for hooking up Spark and Cassandra just plugs right into Silla. Ignite, I don't know about. I know Spark for sure because we just put up, or we recently put up an article about that. I don't know about Ignite, but I can check on that one. Okay. Okay. All right. Well, now that I have a little bit of a sense of where we're starting from, Silla is a new NoSQL database and it is, the claim to fame is that it does about 10 times the throughput of Apache Cassandra while maintaining Cassandra compatibility and with a lower latency in general. And Silla is open source. Founders, Avi Kaviti and Dora Lear are the two company founders. It is a commercial open source project. They're well known for the KVM hypervisor, which is the basis of the Google Cloud and many other cloud deployments, and the OSB Unicernal, which is an early full-featured Unicernal project, which is also open source and still out there. Silla, we've got people in 10 countries around the world. We are hiring. I don't know if you saw the jobs board, but we're looking for C++ developer, QA solutions engineer, a bunch of different roles within the project and the company. So what makes Silla so different from any other NoSQL database? Well, the design is completely different. It is completely sharded per core. Everything that has to touch memory, for example, is only working on an individual core. Cores communicate by message passing. So there is a send and receive queue for each pair of cores on the system, no lock. Throughput is depending on the system you put it on, a sort of mid-range to upper-mid-range system will give you about 1 to 1.8 million CQL operations per node. CQL is just a Cassandra query language. So we go by how many CQL statements we can execute in a second. The 99th percentile latency is consistently low. This is probably not surprising when we get into more about the architecture of how this thing is laid out. And then the feature set, it is compatible with Apache Cassandra. So here's a quick graph of throughput. The little green bars down here got the write throughput, the read throughput, and a read-write mix. The little green bar is Cassandra 2.1.9, and the purple bar is Silla on the same hardware. So this is running on bare metal with an SSD. And of course, I don't know if anyone went to Brendan Gregg's talk, he points out that most benchmarks are bogus, so it depends on your individual configuration. But you can generally expect to get an order of magnitude throughput improvement. With Silla. Compatibility, you can very often take the same workload, same number of clients, put it on fewer servers. Now, as far as how much of Cassandra did Silla copy over directly and how much of Cassandra did Silla completely reimplement, the stuff that makes it easy to switch back and forth between original recipe Cassandra and Silla is all the same. So Cassandra uses the SS table file format on disk. And the Silla file format is actually identical. So if you are migrating from a Cassandra server to a Silla server, you can simply shut down Cassandra on a node, copy the files over, and start Silla pointed at those same files. There's no conversion that needs to happen. Now of course, Cassandra and Silla may make slightly different decisions on where to split up those files. Cassandra will probably produce fewer and larger files, and Silla may produce a larger number of files because of that per core sharding. But either system will be able to read in the files that were written out by the other one. If you go to the Silla site, you'll notice there is no driver download section. Because all of the drivers for all the common languages out there, including C, Java, Python, Ruby, everything that has current Cassandra support, that same driver will work with Silla. The wire protocol between the Cassandra client and the Cassandra server is implemented exactly by Silla. Same goes for the CQL language. The query language is the same from Cassandra to Silla. The config file, some of the entries in the config file refer to details in the Cassandra implementation that Silla will simply skip and have no effect. But the same config file format will work on both systems. So you can copy your Cassandra.yaml over to Silla.yaml and start up Silla, and it will work, and it will use the right IP addresses and ports and snitch configuration for communicating among those. Management, yes, it is managed with the same JMX endpoints. Now this is not because Silla is a Java program. It's because there's a separate JMX component that starts up and will translate between Silla's own native REST API for management and the JMX endpoints that are provided by Cassandra. So we're actually filling in a few more of these JMX endpoints as we go along. So any third-party tool that you're able to use to manage Cassandra, you'll also be able to use that to manage Silla. Code is all the same. You can copy the Cassandra. Database is the same, and as I mentioned before, the integrations for other tools like Spark will work with Silla as well. And this is just the details on the versions of Cassandra that the current version of Silla is implementing. So the CQL language version 3, the SS table format is the same as Cassandra 2.1.8, JMX that's also the same as 2.1.8. The configuration file is the same. Now Cassandra, when they first started up, they had an older protocol called Thrift for storing query and data. The Cassandra Thrift protocol is not implemented in Silla, so you would need to use a current Cassandra driver that uses CQL. At some point we will have Thrift support, but almost all of the new Cassandra applications have been CQL for quite a while now, and so that's where Silla is focused. Yes, question in the back? The missing Thrift, that would affect you if you had developed a Cassandra application using a very old driver that uses Thrift. So if you've gotten your Cassandra driver within the past couple of years when everything has been focused on CQL, then your CQL based Cassandra application would just work. It's just some of those like Cassandra 0.9 vintage applications are not going to run with Silla yet. Yes, Cassandra is a distributed NoSQL database. At the very basic level, it's like a distributed hash table where data is given a persistent identifier, and then it's stored on multiple nodes around a ring. That level of the system where you can choose how many copies of a piece of data are stored is tunable. So you can actually tell Cassandra, I want to store copies of this data on three out of the five nodes of this ring, but when I do my write, I want it to come back when one of them is written, and then Cassandra has to take care of replicating it itself. Or you can say, I don't want my insert statement to return until I know my data has been persisted to a quorum of the nodes where it's supposed to be. So one of the things about developing for Cassandra as a developer or as a systems architect is you can decide how you want to balance out speed versus safety. So you could, for example, have certain items of data, like some cached data from a user session that you can regenerate. You could take some data and run it very fast but with fewer copies. Or you could say this other set of data is something that I can't easily regenerate. Therefore I'm going to set the replication and the consistency level for that data to need to be on more nodes. SILA copies all that exactly from Cassandra. So if you've done the process of going through and reasoning about your data and deciding how you want to trade off performance versus safety, then all those same decisions will be reflected exactly in your SILA configuration. Yes, there's a repair functionality that will let you repair your ring when you replace a node. There are different levels that you can apply when querying your data as well. So you could say I want my reads to only come back if I'm reading a quorum of all nodes. Or you could say, well, I just need to hear back from one node about this particular piece of data. The Cassandra design is possible that you could misuse it. You could build something that was both unsafe and slow if you really tried. But if you go through and figure out the possible failure scenarios and how you want each piece of data to be protected, then you can balance out and get the right level of speed versus safety for all the data you want to preserve for your application. If there's something that you would cry if you lost, then look at quorum. So your write will not come back until a quorum of nodes has a safe copy of it. Yeah, it's the same. The Apache Cassandra project has a lot of interesting discussion of different data models. There's also a regular Cassandra meetup. So if you go to the Cassandra meetups, very often there will be a talk from somebody who has gone through exactly the kind of process we're talking about of saying, well, here's our initial data model that we came up with. And then we decided that for whatever performance or safety or ease of administration reasons, we went through a couple of different versions of it. We have done some reporting back to the Cassandra project, and there are several people on the Cassandra list and I think vice versa. So it is kind of symbiotic and it's all open source. So if you like the Cassandra model and you like the drivers, then it's a – We don't want the performance. We are going to offer training, but not yet. Right now we're focused on GA. So if you're looking to develop applications for Cassandra, then yeah, there's some good tutorials there. There's an O'Reilly book on Cassandra out too, but I think the O'Reilly one is kind of old. And either A-Press or Packet Press had a good one out more recently than the O'Reilly one. But the good news is that there's a lot of good information about it. And if you go to Stack Overflow, there's some good discussions of different Cassandra options as well. But if you can't get something – Oh yeah, from an open source point of view, you can always get on our user mailing list and ask. And then there's also a support line that you'll be able to get on and get a support line too if you're interested in that. For now, the user mailing list has all the main developers on it too. So you'll get good answers to any FILLA user questions as well. Oh, Gossip is the way that both Cassandra and FILLA share information about which nodes are up, which nodes are down. So FILLA copies the same general principle for communications between nodes of the cluster. However, you can't take a FILLA node and add it to a Cassandra cluster or vice versa. The wire protocol of what travels between nodes is a little different. Yeah, you would need to replace all the nodes in a cluster at once in order to do the migration. Each individual node is easy, but you can't do one node at a time. Now, Cassandra does have the concept of – and so both have the concept of data centers where you might have a duplicate of the same data in different regions for resilience. And so in the near future there's going to be an option of having a Cassandra data center and a FILLA data center that cooperate, but that's a near future thing. It's not something you can do right now. One of the things that some sites do when they experiment with migrating across NoSQL systems is they will do a right. So you would be writing to both systems for a while and then cut over at some point. So that's fairly common in large scale NoSQL situations where you can't afford to have something go down. But we are going to do the translator functionality soon. Okay, so how does it work? How is it possible to do all that stuff but so fast? Well, this is Lady Ava Lovelace and before she even actually had a computer to program on she came up with this which is surprisingly accurate. How multifarious and how mutually complicated are the considerations which the working of such an engine involves? There are frequently several distinct sets of effects going on simultaneously all in a manner independent of each other and yet to a greater or less degree exercising a mutual influence. To adjust each to every other and indeed even to perceive and trace them out with perfect correctness and success entails difficulties whose nature partakes to a certain extent of those involved in every question where conditions are very numerous and intercomplicated. How many have dealt with that? Yeah, so most of today's server software was designed back in the day when you had one core per processor and SMP systems were out there but it wasn't so big of a thing. Even your typical mid-range system was a single core. Today, excuse me, today you can go down to well maybe not target but certainly fries and buy a 10 core processor. You can click on a cute little web form and get a machine with 20 or 28 cores by the hour. So the difference in how these systems have to be coded for is remarkable. Instead of the processor spending most of its time doing actual work when you take an application that has thread pools or a lot of contending processes at the OS level, the cores are spending most of their time contending for locks and getting in each other's way. So what SILA does is let each core run flat out by giving each core its own dedicated set of resources. So in a classic multi-threaded application you've got a lot of threads going on and the threads need to make system calls into the kernel and of course when something wants memory the kernel has to handle allocating memory to many different threads running in the same process. And what SILA does is SILA is based on a thread per core architecture called CSTAR. And in CSTAR every core has its own chunk of memory to be allocated. And so when one core needs to allocate memory it doesn't have to take a lock. It can just get memory that's dedicated to that core. For writing to the disk it isn't a multi-threaded application. All of the writes in SILA are done with Linux asynchronous IO. Now what this means is that SILA becomes sensitive to the performance of the file system. So the performance of the database is no longer the issue and now it's how well does your file system handle asynchronous IO. And what that means is that the kind of performance results we're seeing with SILA will show up with XFS which happens to have excellent asynchronous support. But you're not going to see the dramatic high SILA performance with file systems that don't have as solid support for asynchronous IO. Now I said something a little bit earlier about the speed of development that SILA was able to achieve taking a fairly complex NoSQL development project and making it happen in a year. And this doesn't sound like the kind of thing that's possible to achieve with a thread per core architecture and every core having to communicate with every other core by message passing. That sounds like a really hard system. The big win from the CSTAR framework that SILA is based on is it gives the C++ programmer a set of abstractions to work with to make it possible to develop with that model in a reasonable amount of time with a high quality level. So the constructs of futures, promises, and continuations make it so that the programmer who's developing SILA can work with functions that return values that you can reason about rather than just handling message passing across cores by hand. Now a lot of database tuning is based on tuning how Linux handles the page cache for disk IO. And SILA has taken the approach of instead of relying on the OS to cache blocks the SILA design has a built-in cache. And then everything that goes to disk is asynchronous IO. And what this means is that instead of in a conventional layout where the database might modify one row within a 4K block and then the OS has to spew that whole 4K block out to disk in SILA it would be modifying data in memory. And then when that cache gets written out to disk SILA has application level knowledge of the cache and can just write out the data that needs to be written out. So there's no such thing as a parasitic row which is a row that needs to be or a row that doesn't need to be written out that kind of tags along with one that does. Oh, okay. Yes, yes, yes. The SS tables are written in a different way. They're written using asynchronous IO. They're using a more efficient mechanism. But the bits that end up on disk are the same as what gets there from the old school Cassandra way. Yeah, well if you look back in the day how did Linux get asynchronous IO in the first place? That was Oracle developers saying we need this feature. So Linux didn't have asynchronous IO to the best of my knowledge until the Oracle people were working on porting their database and they ran into a very similar issue. So a lot of the high performance techniques in SILA are things that have already existed in operating systems and in relational databases. However, they haven't yet been applied to NoSQL. So there's some computer science wisdom of the ages that's being dug up and reapplied to this kind of task. As well as some of the more innovative sharding and the futures promises continuations model, there's also some, well let's go back and look at things that Oracle had figured out in the late 90s or early 2000s and other relational databases have also taken advantage of. Oh yeah, sure, sure. Well repairs can be faster because when your round trips are significantly faster because you never have a Java garbage collection for example instead of having some transactions face a high latency then everything comes back quickly then a complex operation like a repair is going to complete more quickly as well and in a more predictable amount of time. Okay, so this is the simple version if you need a pony. Okay, so I didn't know about the 1024x768 requirement. I can show you the SILA CQL request served per second. This is a system that's doing about two million requests per second and you'll notice it's consistently high and this is across a three-node cluster by the way, two million across a three-node cluster and there are details on this benchmark on the SILA block. And it takes a little while to come up to speed and the overall throughput is lower. That's just the basics. Now the interesting metric here is latency and this graph is a little complicated. The throughput is here so SILA is doing over 250,000 CQL requests per second and Cassandra is doing about 150,000. So SILA is running a little bit higher throughput than Cassandra in this scenario and then the y-axis over on the other side this is latency in milliseconds. Okay, so mean latency SILA is a little quicker than Cassandra or substantially, median still substantially quicker. 95th you can see a little bit more in the 95th percentile for Cassandra latency but once you start to get into that 99th and 99.9th percentile you start to see these over 5 millisecond latencies on the Cassandra side while SILA is still consistently pretty low. So this is the latency of the slowest .1% of requests. The slowest of the slow, the ones that happen to hit a slow path in the system. It doesn't mean that one box is slow it means that if a box is serving a million requests it will serve a thousand slow requests. There are a bunch of these that actually the SILA developers are running all the time so the ones that we make the graphs for tend to be the ones that are more representative. Yeah, yeah, yeah, and we do them on different infrastructures so we've done some on different bare metal configurations and on Amazon. What's SILA written in? What's SILA written in? SILA is written in C++14. It's the first database that we know of to be written in the latest version of the C++ language. Yeah, and the horizontal lines go with the numbers on the right side and the bars go with the numbers on the left side. Yeah, yeah. Well, it's not necessarily because of low latency that the throughput is high. It's possible to have a system that has very high throughput and very high latency. And so as the throughput goes up then the latency also tends to go up in a production system. So yeah, you have to balance them. And in this case it's a test where Cassandra actually got a little advantage because it's running at a lower throughput and being compared straight up on latency. And this is just an example of a quick repair. This is using the node tool command for Cassandra. And it starts a repair at 1733.44 and it's done at 1734.10. So we'll have some more detailed benchmarks on quick repairs too. So once you have the speed what can you do with it? And it's not just a matter of paying a smaller bill to your cloud provider, although that's nice too. You can shrink the overall number of nodes in the cluster. You can make it faster to scale your cluster up and down for big events such as Black Friday for retail or for example if you're doing an online game and you have a big release of an expansion pack or something and a lot of people are playing you can ramp up the number of nodes for your game. You can do more with modeling your data instead of simplifying your data model on a performance-driven basis. Cassandra lets you do a lot and so it all lets you do the same stuff but you can do more interesting designs of your data model. And operations like repairs can happen without bogging down the cluster. There's just extra performance overhead to play with which means that administration becomes a lot less painful. So what's that? Because you have the extra performance you can use a more sophisticated data model. Sometimes in designing a Cassandra data model you end up having to denormalize things and make extra tables just to handle specific queries. With SILA you still have to do some of that. It's not going to do your computer science for you but you have more options in how you design your data. Different data models can be acceptably fast. No, not necessarily. But because you have more performance you can either use that to have fewer boxes or you can have more performance overhead to use for things like running your repairs online or doing more sophisticated queries on the same data. And so that just means that you can chill. I saw this building and I had to take a picture of it because this is the data center where people who have simple NoSQL databases can go. It's not the stuff that has to be tweaked and babysat constantly. Alright, so what's coming up next with SILA? Well, we're looking to build a community. People who want to do stuff that plugs into SILA are especially welcome. You do not have to be a C++ expert to get into it. We have some DevOps stuff that we're working on and looking for anybody who wants to make something interesting and have a good NoSQL data store for it. Of course the core database continues to improve. We're already integrated with Apache Spark but of course there's a huge stack of stuff that will connect to Cassandra and we're hooking all that up to SILA as well, including management tools. And then there's the possibility of having SILA also implement more protocols and not just the Cassandra protocol. So if you have ideas there then please get on our mailing list and we can discuss that. Stuff that we just come up with in the past couple of releases. We're doing a beta release every few weeks now. Past few releases have had a new official AMI. This one's using CentOS 7. We recently introduced SSL support and the alter table command so you can change your schema on the fly. Sleep mode, this is good. At the very first few releases of SILA we had a polling mode where every core is running flat out waiting for more inbound traffic. This is great for speed, not so great for your battery, if you're developing on a laptop. So sleep mode will now let SILA go to sleep and wait for you to run your tests on your laptop again. I'm especially happy about this one because now I can just turn my laptop around and do my SILA development and test on it. We have a project that is not actually even part of SILA. It's called Cassandra Test and Deploy. That is based on Ansible. It's a way to easily spin up a bunch of Cassandra nodes and test different failure scenarios on them. Or of course SILA nodes. Any time you want to test SILA against something else or just try something out on Cassandra, then check out the Cassandra Test and Deploy project. It's on GitHub. Go to GitHub SILADB and all our projects are there and you can find our GitHub page from the main website. There's a very simple contributor agreement that we ask people to accept in order to get code into the main SILADB project. That's just to say what you wrote and we can take it and do our stuff with it. We do commit to it being open source in that agreement. The way that the development process works is the code is hosted on GitHub and we use GitHub issues. However, we do not use GitHub pull requests. So if you have a change that you want to discuss and have the team consider for acceptance into SILA, that would be something that you would post to the mailing list. We have a document on how to make Git do that automatically. The Linux kernel developers have come up with a pretty slick system for taking a Git branch, turning it into a series of patches and posting it to a mailing list, and we use all that exact same stuff. It's essentially Linux kernel style development. As I mentioned before, you don't have to be a C++ programmer. If you're into Python, let me know. We'll do some DevOps. I'm building out from demo scripts to more deployment and easy ways to manage my SILA projects or projects that I have that are on top of SILA and I need to deploy SILA along with everything else. There's a knowledge base, so if you want to write something up for that, let me know. We'll see what kind of projects you come up with.