>> Welcome to the AWS Startup Showcase: New Breakthroughs in DevOps, Data Analytics, and Cloud Management Tools. I'm Lisa Martin. I'm pleased to welcome JT Giri, the CEO and founder of nOps, to the program. JT, welcome. It's great to have you. >> Thank you, Lisa. Glad to be here. >> Talk to me about nOps. This was founded in 2017, you're the founder. What do you guys do? >> Yeah. So just a little bit background on myself. You know, I've been migrating companies to AWS ever since EC2 was in beta. And you know, in the beginning I had to convince people like, "Hey, you should move to cloud." And the question people used to ask me, like, "Is cloud secure?" I'm glad no one is asking that question anymore. So, as I was building and migrating customers to the cloud, one of the things I realized very early on, is just cloud, there are so many resources, so many teams provisioning resources, then how do you align with your business goals? So we created nOps so that, for a mission, how do you are you build a platform where you make sure every single change and every single resource in the cloud is aligned with the business needs, right? Like we really helped people to make the right trade-offs. >> So you mentioned you've been doing this since EC2 was in beta, and we just celebrated, with AWS, EC2's 15th birthday. So you've been doing this awhile. You don't look old enough, but you've been doing this for awhile. So one of the things that I read on the website, I always love to understand messaging, that nOps says about itself, "The first cloud ops platform "designed to sync revenue growth across your teams." Talk to me about how you do that. >> Yeah. So one of the problems we see in the market right now, there are a lot of tools, there are a lot of dashboards that shows like, "Hey, you have this many issues, "here's the opportunity to fix issues. "And here are the security issues." We're more focused on how do we take those issues from a backlog and fixing those issues. Right? So our focus is more on operationalizing, so your teams could actually own that, prioritize, and actually remediate those issues. So that's where we focus our energy. >> Got it. Let's talk about cloud ops now, and how it varies or differs from traditional cloud management. >> Yeah, I think, like I mentioned, cloud management seems to be more like visibility. And everyone knows that there are challenges in their cloud environment. But when you focus more on the operation side, what we really try to do, from an issue, how do you actually fix that issue? How do you prioritize? How do you make the right trade-offs. Right? Trade offs is important because we make a lot of decisions in the cloud when you're building your infrastructure. Sometime you might have to prioritize for costs, sometime you might have to prioritize based on the SLA. You might have to add more resources to hit your SLAs. So we really help you to prioritize. And we build sort of accountability. You know, you can create roles. Most of the time, what we noticed, I truly believe that if it's everyone's responsibility, it's no one's responsibility. You know? So what we do is we help, within the tool, to establish clear roles and responsibilities. And we show audit log of people reviewing and fixing security issues. And we show audit log of people fixing and reviewing cost issues. That's one way we're trying to bring accountability. >> I like what you said, if everyone's responsible, then really no one is. And that seems to be a persistent problem that we see in businesses across industries, is just still that challenge with aligning IT and business. And especially with the dynamics of the market, JT, that we've seen in the last 18 months, things are moving so quickly. Talk to me about how you guys have been helping companies, especially in the last 18 months, with such change to get that alignment. So that that visibility and those clear roles are established and functional. >> Yeah. You know, what we really do is obviously listen to the customers. Right? And one of the challenges we hear over and over is like, you know, I know I have issues in the cloud environment that I really need help prioritizing. And they're really looking for a framework where they can come in and say, "Okay these are the people who are responsible for security. "These are the people who are responsible for the cost." So as part of onboarding with nOps, that's one of the things you do, you define your workloads. By the way, we automatically create your workload across all your accounts. And then we allow you to move resources around if you like. But then one of the first thing we do is assign roles and responsibilities for each one of these workloads. Oh, it's been incredible to see, when you have that kind of accountability, people actually do make sure that the resources are aligned with the business needs. >> So are you seeing, I mean, that's kind of a cultural shift. That changed management is a challenging process. How are you seeing that evolve in organizations who've been used to doing things maybe in a little bit of a blinders on kind of mode? >> Yeah. Well, changed management is an area where we spent a lot of time, because in cloud, changed management is almost like a fire hose. Right? There's so many changes and you could have 20 people or 20 different teams making changes. I think what people really want is sort of root cause analysis. Like, "Hey, this is what changed here. "Here's why it changed. "And here's how actions we could take, or you could take." So this is where we focus on this, where nOps focuses on. We really help people to show the root cause analysis, these three, four things, these three, four changes are related to this cost increase or these security issues. And we show like a clear path on taking action on those issues. >> That's critical. The ability to show the paths, to take the action to remediate or make any changes, course corrections. As we've learned in the last 18 months, real time is no longer for so many industries, a nice-to-have. The ability to be able to pivot on the fly is a survival and thriving mechanism. So that is really key. I do want to talk about the relationship with nOps and AWS. Here we are at the AWS startup showcase. Give me a little overview on the partnership. >> It's been an incredible. Like I said, I have a long history of working with AWS, and I started a consulting company, a very, very successful one. And so I have years of working with AWS partner teams. I think it's incredible. We were the first company in this, maybe not first, maybe very early. We were part of this Well-Architected framework. And when I came out of that training, the Well-Architected training, I was so excited. I was like, "Wow, this is amazing." You know? Because, to me, whenever you're building infrastructure, you really are making trade-offs. You know, sometime you optimize for cost, sometime you optimize for reliability. So it has been incredible to work with the Well-Architected team. Or Amazon also has this, another program, called FTR: Foundational Technical Review. We've been working closely with that team. So yeah, it's been amazing to collaborate with AWS. >> It sounds pretty synergistic. Have you had a chance influence infrastructure, and some of the technical direction? >> Oh, absolutely. Yeah. We work very closely. One of the cool thing about AWS is that they do take customers' feedback very, very seriously. And, Lisa, also other way around. Right? If AWS is going to build something, having that insight into the roadmap is very beneficial. Because if they're doing it, there's no point of us trying to reinvent the wheel. So that kind of synergy is very helpful. >> That's excellent. Let's talk about customers, now. I always loved talking about customer use cases and outcomes. You guys have a great story with Uber. Walk us through what the challenges were, how nOps came in, what you deployed, and how the business is being impacted positively. >> Yeah, I think Uber and all the enterprises, they sort of have the same challenge, right? There are many teams provisioning infrastructure. How do you make sure all those resources are aligned with your business needs? And in addition to that, not only you have different teams provisioning resources, there are different workloads. And these workloads have different requirements. Right? Some are production workloads, some are just maybe task workloads. So one of the things Uber did, they really embraced sort of nOps' way of managing infrastructure, building accountability, sharing these dashboards with all the different teams. And it was incredible, because within first 30 days they were able to save up to 15%. This was in their autonomous vehicle unit. And they spent a lot of money. And having to see that kind of cost saving, it was just amazing. And we see this over and over. And so like when customers are using platform, it's just incredible how much cost savings are there. >> So Uber, you said, in their autonomous vehicle department saved 15% in just the first 30 days alone. And you said that's a common positive business outcome that you're seeing from customers across industries, is that immediate cost savings. Tell me a little bit more about that as a differentiator of nOps' business. >> Yeah. Because as I mentioned earlier, one of the things we do, we bring accountability. Right? Most of the time when people, before nOps, maybe there are resources that are not accounted for. There is not clear owners, there's no budgets, there's no chargebacks. So I do think that's a huge differentiator of nOps, because, as part of onboarding, as you establish these roles and these responsibilities, you find so much unaccounted resources. And sometimes you don't even need those resources, and you shut them down. And those are the easiest next steps. Right? Like, you don't need to architect, you just shut it down. Like no one needs these resources. So that, I do believe, that's our strength. And we were able to demonstrate this over and over, this, on average, 15-30% cost saving in the first month or so. >> That's excellent. That's a lot of what customers, especially these days, are looking for, is cost optimization across the organization. What are some of the things that you've seen, that nOps has experienced in the last 18 months, with so much acceleration? Anything that surprised you, any industries that you see as really leading edge here, or prime candidates for your technology? >> Yeah. A couple of things. We see a lot of partners, a lot of other consulting company, leveraging nOps as a part of their offering. That's been amazing, we have a lot of partners who really leverage nOps as a go to market and ongoing management of their customers. And I do see that shift from the customer side as well. I think the complexity of cloud continues to kind of increase, like you just mentioned, it sounds like from last 18 months, it accelerated even more. How do you stay up to date, you know? And how do you always make sure that you're following best practices? So companies bring in partners to help them implement solutions. And then these partners are leveraging tools like nOps. And we've seen a lot of momentum around that. >> Tell me a little bit about how partners are leveraging nOps. What are some of the synergistic benefits on both sides? >> Yeah, so normally partners leverage nOps, you know, they will use it for Well-Architected assessments. And, you know, I've personally done a lot of these Well-Architected assessments. And, you know, early on, I kind of learned that, assessments are only good if you're able to move forward with fixing issues in the customer's environment. So what we really do, we really help customers, or sorry, we really help partners to actually do these Well-Architected assessments automatically. We auto discover issues, and then we help them to create proposals so they can present it to the customers like, "Hey, here are the five things we can help you with, "and here's how much it will cost." And, you know, we really streamline that whole process. And it's amazing that some partners used to take like days to do these assessments. Now they can do it an hour. And we also increase the close rate on SoW's because they are a lot more clear. You know, like here are the issues and here's how we can help you to fix those issues. >> You got some great business metrics there, in terms of speed and reduction in cost, reduction in speed. But it sounds like what you're doing is helping those partners build a business case for their customers far more efficiently and more clearly than they've ever been able to do before. >> Absolutely. Yeah, yeah. And... >> Go ahead. >> Yeah, so absolutely. Before nOps, everyone is using spreadsheets most of the time. Right? It's spreadsheets to collect information, and emails back and forth. And after the partner's start using nOps, they use nOps to facilitate these assessments. And once they have these customers as ongoing customers, they use nOps for checks and balances to make sure they're constantly aligned. Right? And we have a lot of success of having real revenue impact on partners' business, by leveraging nOps. >> Excellent. That's true value and true trust there. Last question. Where can you point folks, a CTA or URL that you want people to go to to learn more about nOps? >> Yeah. Basically just go to nops.io and just put on signup. Yeah. I love doing this stuff. I love talking to the customers. Feel free to reach out to me, as well: jt@nops. I would love to have a conversation. But yeah, just nops.io is the best place to get started. >> Awesome, nops.io. And I can hear enthusiasm for this, and your genuineness comes through the zoom screen here, JT. I totally thought that the whole time. Thank you for talking to me about nOps, how you guys are helping organizations really embrace cloud ops and evolve from traditional cloud management tools. We appreciate your time. >> Thanks, Lisa. >> For JT Giri, I'm Lisa Martin. You're watching the AWS Startup Showcase.

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to M I. T. Everybody. This is the Cube. The leader in live coverage. My name is David wanted. I'm here with my co host, Paul Gill, in this day to coverage of the M I t cdo I Q conference. A lot of acronym stands for M I. T. Of course, the great institution. But Chief Data officer information quality event is his 13th annual event. Lars to Maria's here is the managing partner of Brass Rat Capital. Cool name Lars. Welcome to the Cube. Great. Very much. Glad I start with a name brass around Capitol was That's >> rat is reference to the M I t school. Okay, Beaver? Well, he is, but the students call it a brass rat, and I'm third generation M i t. So it's just seen absolutely appropriate. That is a brass rods and capital is not a reference to money, but is actually referenced to the intellectual capital. They if you have five or six brass rats in the same company, you know, we Sometimes engineers arrive and they could do some things. >> And it Boy, if you put in some data data capital in there, you really explosions. We cause a few problems. So we're gonna talk about some new regulations that are coming down. New legislation that's coming down that you exposed me to yesterday, which is gonna have downstream implications. You get ahead of this stuff and understand it. You can really first of all, prepare, make sure you're in compliance, but then potentially take advantage for your business. So explain to us this notion of open government act. >> Um, in the last five years, six years or so, there's been an effort going on to increase the transparency across all levels of government. Okay, State, local and federal government. The first of federal government laws was called the the Open Data Act of 2014 and that was an act. They was acted unanimously by Congress and signed by Obama. They was taking the departments of the various agencies of the United States government and trying to roll up all the expenses into one kind of expense. This is where we spent our money and who got the money and doing that. That's what they were trying to do. >> Big picture type of thing. >> Yeah, big picture type thing. But unfortunately, it didn't work, okay? Because they forgot to include this odd word called mentalities. So the same departments meant the same thing. Data problem. They have a really big data problem. They still have it. So they're to G et o reports out criticizing how was done, and the government's gonna try and correct it. Then in earlier this year, there was another open government date act which said in it was signed by Trump. Now, this time you had, like, maybe 25 negative votes, but essentially otherwise passed Congress completely. I was called the Open as all capital O >> P E >> n Government Data act. Okay, and that's not been implemented yet. But there's live talking around this conference today in various Chief date officers are talking about this requirement that every single non intelligence defense, you know, vital protection of the people type stuff all the like, um, interior, treasury, transportation, those type of systems. If you produce a report these days, which is machine, I mean human readable. You must now in two years or three years. I forget the exact invitation date. Have it also be machine readable. Now, some people think machine riddle mil means like pdf formats, but no, >> In fact, what the government did is it >> said it must be machine readable. So you must be able to get into the reports, and you have to be able to extract out the information and attach it to the tree of knowledge. Okay, so we're all of sudden having context like they're currently machine readable, Quote unquote, easy reports. But you can get into those SEC reports. You pull out the net net income information and says its net income, but you don't know what it attaches to on the tree of knowledge. So, um, we are helping the government in some sense able, machine readable type reporting that weaken, do machine to machine without people being involved. >> Would you say the tree of knowledge You're talking about the constant >> man tick semantic tree of knowledge so that, you know, we all come from one concept like the human is example of a living thing living beast, a living Beeston example Living thing. So it also goes back, and they're serving as you get farther and farther out the tree, there's more distance or semantic distance, but you can attach it back to concept so you can attach context to the various data. Is this essentially metadata? That's what people call it. But if I would go over see sale here at M I t, they would turn around. They call it the Tree of Knowledge or semantic data. Okay, it's referred to his semantic dated, So you are passing not only the data itself, but the context that >> goes along with the data. Okay, how does this relate to the financial transparency? >> Well, Financial Transparency Act was introduced by representative Issa, who's a Republican out of California. He's run the government Affairs Committee in the House. He retired from Congress this past November, but in 2017 he introduced what's got referred to his H R 15 30 Um, and the 15 30 is going to dramatically change the way, um, financial regulators work in the United States. Um, it is about it was about to be introduced two weeks ago when the labor of digital currency stuff came up. So it's been delayed a little bit because they're trying to add some of the digital currency legislation to that law. >> A front run that Well, >> I don't know exactly what the remember soul coming out of Maxine Waters Committee. So the staff is working on a bunch of different things at once. But, um, we own g was asked to consult with them on looking at the 15 30 act and saying, How would we improve quote unquote, given our technical, you know, not doing policy. We just don't have the technical aspects of the act. How would we want to see it improved? So one of the things we have advised is that for the first time in the United States codes history, they're gonna include interesting term called ontology. You know what intelligence? Well, everyone gets scared by the word. And when I read run into people, they say, Are you a doctor? I said, no, no, no. I'm just a date. A guy. Um, but an intolerant tea is like a taxonomy, but it had order has important, and an ontology allows you to do it is ah, kinda, you know, giving some context of linking something to something else. And so you're able Thio give Maur information with an intolerant that you're able to you with a tax on it. >> Okay, so it's a taxonomy on steroids? >> Yes, exactly what? More flexible, >> Yes, but it's critically important for artificial intelligence machine warning because if I can give them until ology of sort of how it goes up and down the semantics, I can turn around, do a I and machine learning problems on the >> order of 100 >> 1000 even 10,000 times faster. And it has context. It has contacts in just having a little bit of context speeds up these problems so dramatically so and it is that what enables the machine to machine? New notion? No, the machine to machine is coming in with son called SP R M just standard business report model. It's a OMG sophistication of way of allowing the computers or machines, as we call them these days to get into a standard business report. Okay, so let's say you're ah drug company. You have thio certify you >> drugged you manufactured in India, get United States safely. Okay, you have various >> reporting requirements on the way. You've got to give extra easy the FDA et cetera that will always be a standard format. The SEC has a different format. FERC has a different format. Okay, so what s p r m does it allows it to describe in an intolerant he what's in the report? And then it also allows one to attach an ontology to the cells in the report. So if you like at a sec 10 Q 10 k report, you can attach a US gap taxonomy or ontology to it and say, OK, net income annual. That's part of the income statement. You should never see that in a balance sheet type item. You know his example? Okay. Or you can for the first time by having that context you can say are solid problem, which suggested that you can file these machine readable reports that air wrong. So they believe or not, There were about 50 cases in the last 10 years where SEC reports have been filed where the assets don't equal total liabilities, plus cheryl equity, you know, just they didn't add >> up. So this to, >> you know, to entry accounting doesn't work. >> Okay, so so you could have the machines go and check scale. Hey, we got a problem We've >> got a problem here, and you don't have to get humans evolved. So we're gonna, um uh, Holland in Australia or two leaders ahead of the United States. In this area, they seem dramatic pickups. I mean, Holland's reporting something on the order of 90%. Pick up Australia's reporting 60% pickup. >> We say pick up. You're talking about pickup of errors. No efficiency, productivity, productivity. Okay, >> you're taking people out of the whole cycle. It's dramatic. >> Okay, now what's the OMG is rolling on the hoof. Explain the OMG >> Object Management Group. I'm not speaking on behalf of them. It's a membership run organization. You remember? I am a >> member of cold. >> I'm a khalid of it. But I don't represent omg. It's the membership has to collectively vote that this is what we think. Okay, so I can't speak on them, right? I have a pretty significant role with them. I run on behalf of OMG something called the Federated Enterprise Risk Management Group. That's the group which is focusing on risk management for large entities like the federal government's Veterans Affairs or Department offense upstairs. I think talking right now is the Chief date Officer for transportation. OK, that's a large organization, which they, they're instructed by own be at the, um, chief financial officer level. The one number one thing to do for the government is to get an effective enterprise worst management model going in the government agencies. And so they come to own G let just like NIST or just like DARPA does from the defense or intelligence side, saying we need to have standards in this area. So not only can we talk thio you effectively, but we can talk with our industry partners effectively on space. Programs are on retail, on medical programs, on finance programs, and so they're at OMG. There are two significant financial programs, or Sanders, that exist once called figgy financial instrument global identifier, which is a way of identifying a swap. Its way of identifying a security does not have to be used for a que ce it, but a worldwide. You can identify that you know, IBM stock did trade in Tokyo, so it's a different identifier has different, you know, the liberals against the one trading New York. Okay, so those air called figgy identifiers them. There are attributes associated with that security or that beast the being identified, which is generally comes out of 50 which is the financial industry business ontology. So you know, it says for a corporate bond, it has coupon maturity, semi annual payment, bullets. You know, it is an example. So that gives you all the information that you would need to go through to the calculation, assuming you could have a calculation routine to do it, then you need thio. Then turn around and set up your well. Call your environment. You know where Ford Yield Curves are with mortgage backed securities or any portable call. Will bond sort of probabilistic lee run their numbers many times and come up with effective duration? Um, And then you do your Vader's analytics. No aggregating the portfolio and looking at Shortfalls versus your funding. Or however you're doing risk management and then finally do reporting, which is where the standardized business reporting model comes in. So that kind of the five parts of doing a full enterprise risk model and Alex So what >> does >> this mean for first? Well, who does his impact on? What does it mean for organizations? >> Well, it's gonna change the world for basically everyone because it's like doing a clue ends of a software upgrade. Conversion one's version two point. Oh, and you know how software upgrades Everyone hates and it hurts because everyone's gonna have to now start using the same standard ontology. And, of course, that Sarah Ontology No one completely agrees with the regulators have agreed to it. The and the ultimate controlling authority in this thing is going to be F sock, which is the Dodd frank mandated response to not ever having another chart. So the secretary of Treasury heads it. It's Ah, I forget it's the, uh, federal systemic oversight committee or something like that. All eight regulators report into it. And, oh, if our stands is being the adviser Teff sock for all the analytics, what these laws were doing, you're getting over farm or more power to turn around and look at how we're going to find data across the three so we can come up consistent analytics and we can therefore hopefully take one day. Like Goldman, Sachs is pre payment model on mortgages. Apply it to Citibank Portfolio so we can look at consistency of analytics as well. It is only apply to regulated businesses. It's gonna apply to regulated financial businesses. Okay, so it's gonna capture all your mutual funds, is gonna capture all your investment adviser is gonna catch her. Most of your insurance companies through the medical air side, it's gonna capture all your commercial banks is gonna capture most of you community banks. Okay, Not all of them, because some of they're so small, they're not regularly on a federal basis. The one regulator which is being skipped at this point, is the National Association Insurance Commissioners. But they're apparently coming along as well. Independent federal legislation. Remember, they're regulated on the state level, not regularly on the federal level. But they've kind of realized where the ball's going and, >> well, let's make life better or simply more complex. >> It's going to make life horrible at first, but we're gonna take out incredible efficiency gains, probably after the first time you get it done. Okay, is gonna be the problem of getting it done to everyone agreeing. We use the same definitions >> of the same data. Who gets the efficiency gains? The regulators, The companies are both >> all everyone. Can you imagine that? You know Ah, Goldman Sachs earnings report comes out. You're an analyst. Looking at How do I know what Goldman? Good or bad? You have your own equity model. You just give the model to the semantic worksheet and all turn around. Say, Oh, those numbers are all good. This is what expected. Did it? Did it? Didn't you? Haven't. You could do that. There are examples of companies here in the United States where they used to have, um, competitive analysis. Okay. They would be taking somewhere on the order of 600 to 7. How 100 man hours to do the competitive analysis by having an available electronically, they cut those 600 hours down to five to do a competitive analysis. Okay, that's an example of the type of productivity you're gonna see both on the investment side when you're doing analysis, but also on the regulatory site. Can you now imagine you get a regulatory reports say, Oh, there's they're out of their way out of whack. I can tell you this fraud going on here because their numbers are too much in X y z. You know, you had to fudge numbers today, >> and so the securities analyst can spend Mme. Or his or her time looking forward, doing forecasts exactly analysis than having a look back and reconcile all this >> right? And you know, you hear it through this conference, for instance, something like 80 to 85% of the time of analysts to spend getting the data ready. >> You hear the same thing with data scientists, >> right? And so it's extent that we can helped define the data. We're going thio speed things up dramatically. But then what's really instinct to me, being an M I t engineer is that we have great possibilities. An A I I mean, really great possibilities. Right now, most of the A miles or pattern matching like you know, this idea using face shield technology that's just really doing patterns. You can do wonderful predictive analytics of a I and but we just need to give ah lot of the a m a. I am a I models the contact so they can run more quickly. OK, so we're going to see a world which is gonna found funny, But we're going to see a world. We talk about semantic analytics. Okay. Semantic analytics means I'm getting all the inputs for the analysis with context to each one of the variables. And when I and what comes out of it will be a variable results. But you also have semantics with it. So one in the future not too distant future. Where are we? We're in some of the national labs. Where are you doing it? You're doing pipelines of one model goes to next model goes the next mile. On it goes Next model. So you're gonna software pipelines, Believe or not, you get them running out of an Excel spreadsheet. You know, our modern Enhanced Excel spreadsheet, and that's where the future is gonna be. So you really? If you're gonna be really good in this business, you're gonna have to be able to use your brain. You have to understand what data means You're going to figure out what your modeling really means. What happens if we were, You know, normally for a lot of the stuff we do bell curves. Okay, well, that doesn't have to be the only distribution you could do fat tail. So if you did fat tail descriptions that a bell curve gets you much different results. Now, which one's better? I don't know, but, you know, and just using example >> to another cut in the data. So our view now talk about more about the tech behind this. He's mentioned a I What about math? Machine learning? Deep learning. Yeah, that's a color to that. >> Well, the tech behind it is, believe or not, some relatively old tech. There is a technology called rd F, which is kind of turned around for a long time. It's a science kind of, ah, machine learning, not machine wearing. I'm sorry. Machine code type. Fairly simplistic definitions. Lots of angle brackets and all this stuff there is a higher level. That was your distracted, I think put into standard in, like, 2000 for 2005. Called out. Well, two point. Oh, and it does a lot at a higher level. The same stuff that already f does. Okay, you could also create, um, believer, not your own special ways of a communicating and ontology just using XML. Okay, So, uh, x b r l is an enhanced version of XML, okay? And so some of these older technologies, quote unquote old 20 years old, are essentially gonna be driving a lot of this stuff. So you know you know Corbett, right? Corba? Is that what a maid omg you know, on the communication and press thing, do you realize that basically every single device in the world has a corpus standard at okay? Yeah, omg Standard isn't all your smartphones and all your computers. And and that's how they communicate. It turns out that a lot of this old stuff quote unquote, is so rigidly well defined. Well done that you can build modern stuff that takes us to the Mars based on these old standards. >> All right, we got to go. But I gotta give you the award for the most acronyms >> HR 15 30 fi G o m g s b r >> m fsoc tarp. Oh, fr already halfway. We knew that Owl XML ex brl corba, Which of course >> I do. But that's well done. Like thanks so much for coming. Everyone tried to have you. All right, keep it right there, everybody, We'll be back with our next guest from M i t cdo I Q right after this short, brief short message. Thank you