Hey, what's up everybody welcome back to the YouTube video my name is John Hammond still showing off the junior CTF again That was going on over the past weekend And I wanted to showcase some of those some of the small challenges that I was able to solve and demonstrate them for you So the next one was a trivial challenge. Apparently this was in their easy category So it's only about 300 points now they had dynamic scoring So they were all originally 500 and then fluctuate with however many people actually solve the challenge so okay lost code trivial brute force and Again, based off of this gravity falls cartoon here I honestly didn't even read this description because I'm like, okay, whatever. I don't really care about all this stuff I just want to know the technical challenge here. So okay Your hacker skills can be useful help Gideon to crack this safe. You need this special pick lock So they're using this like weird cryptic hosting thing. So I go ahead and download this this zip archive And I'll just create a folder for it lost code Now let's hop on over to a terminal and take a look at it Unzip this archive here, and I can change directory and take a look at the files that we have and they're all windows executable So whatever I'm assuming I can run them with wine. Thankfully I can I don't know if I can get rid of that error message Oh sweet, I can okay So the thing here says it's a safe brute-forcer try to guess any character 0 through 9 8 through f 32 times so it's a 32 wide string and it'll give us a 1 or 0 depending on what it is Dash H will show this help, but I guess if you just pass in dash dash B and you give it a string of characters It will supposedly Like do things for you. I'm gonna I'm just gonna leave this error here. I'm perfectly fine with seeing it I guess if you guys don't care So according to this are one and like I don't know what that what index that refers to you may bear a Some other characters it isn't the correct spot and same with so the one means that that is a correct character for the correct spot So if I change this to like a zero here that first one will turn into a zero because no that is the wrong character So we know it has to be one Okay, so the idea is to brute-force this right so we can script it pretty easily Here I'll show you the code that I ended up writing out and we'll crank it out together So I'm gonna get some blind text open and working here Drag this down Taking my notes off of the side. Don't look so Juniors lost good Get flag dot pie or whatever. We really want to name it And I'll add a shebang line as usual Even though I always struggle typing these things and it literally bogged down every video I create so I'm using sub-process as a module to be able to actually like call the function and then I imported lowercase letters and Digits because that's what it said it was made up of So I'll actually shrink this What I did is I created I concatenate those videos those things together pool equals sql lowercase and digits and I created like a general purpose like get results or like a check function So what I did is I would have sub-process check the output of that command. I was using wine lost Dot exe dash b and I concatenate on our What we're sending through to it and I actually have to split this up because sub-process takes its arguments as an array So I split this and I think that's all I did and Then I wanted to actually get the line like the very last result Just in case so I think the output should give it to us anyway once we're sending it to us, but Yeah, I ended up splitting that Initially, too I'm actually not gonna worry about that. I'm looking at my notes. I'm like, I don't think I have to do that because it should return to us Anyway, we can just test it. I mean we could just run get result print and Like a times 32 will move get flag into the actual directory Make sure we're working with the actual code and sublime text. Okay, cool. Am I in loss now? Muck our script executable be able to run it. Okay, so it does it does pass things in okay And we can view them as the string sweet So now what I ended up doing was like creating a list of the characters that I know So I just set this to be a random list and I can set any index all like however I want while I loop through it. So what I did is I literally loop through all the characters in the pool Just like I kind of did with a times 32. I can do that for every single character for C in pool I can say our string can be that character 32 times and the result will be the result that we get once we send it our string and For anything that we see as we loop through that result We can get the index of all these ones and say that our new characters the string that we're building That index should be the character that we're looking at So we can keep track of the actual one successes that we get and build them up into a list That is built over time as we loop through all of these so for I in I get I want to go through the index here If the results I if the current index we're looking at is a one a One then we know that that is the correct character So we can say our characters the one that we're looking at that index can be the current character We're looking at so ideally at the end of this loop. We should have all the characters in a list So let's join them as a as a string and If we wanted to we can print out the character that we're looking at and The result that we get Just so you get some visual thing as to what's really happening here So I can run get flag and it prints through all of these things. Oh, and what did I do wrong here? Oh I think because I didn't split the initial output it might be reading a new line character So actually I'm gonna go back to my get result and go ahead and split that We ended up taking the string of it split right so We can take the string of We don't need to take the string of anything as it's already done We just need it on the actual output that we got we can split the actual output by new lines and just get the first Result from it. Okay. How now hopefully ideally No, still wrong. Okay. Well It looks like it's just having trouble because it's an array We can just kind of change it to a dictionary if we wanted to and Then we can print out the values because the values are what we're actually iterating The rule we're adding to so now let's try it Okay, cool. We're looping through it and Sweet we get what should be the flag. See this eight is in the right spot Looks like this six is in the right spot So we don't actually have to print these all out if we don't want to and I guess if we wanted to We can get flag all this stuff. See these error messages Can we get rid of those? I don't know if we can very easily do it. Okay, sweet. We can and there's our flag So we can wrap that in a bash script if we wanted to just to just crank that out So that's what you ended up submitting. That is the key that they that the program will take obviously To get all the successes lost One one one one and that is the flag. So you go ahead and submit that and There you go, so sorry all those pickups right in the script for some reason that seemed to work on my notes Yesterday, but now it didn't today. Maybe I don't know whatever a Dictionary is probably a better thing to use anyway other than an array because like you can't really create an index at those places That haven't already been created let those indexes that haven't already been created yet You can have already that with the dictionary. So that's what we ended up doing. So thanks for watching guys Hope you enjoyed this one I think it was kind of cool to do some scripting for this one because the brute force was simple But easy enough for us to just crank out With an interesting idea just send the same character 32 times over and get all those indexes words in the right place And we just compile all those correctness things and we have the flag. So alright Thanks again guys. Hope you're enjoying these and I'll show off some later Challenges in the next video. See you soon


So, this series is meant for people relatively new to Cyberpunk 2077, looking for information on the game. And today, with 10 things you need to know about Cyberpunk, hopefully you learned something you wasn't already aware of. Guys, if you're new around here and want to show your support, do hit that like button. If you want more Cyberpunk, make sure you subscribe. Also, I'm giving away copies of this game and I'll announce winners a week before the game drops. If you want to be in with a chance of winning one, drop a like on this video, leave a comment down below and make sure you are subbed to this channel. Okay, so 10 things you need to know about Cyberpunk 2077 and these won't necessarily be good things either. But hey ho, let's go! Okay, so to start, this game is nothing like Grand Theft Auto and yes, for sure, you can go around stealing cars and beating people up. That's very much like GTA, but the core design of this game is very, very much different. Cyberpunk 2077 is a proper RPG, not a wannabe RPG, I mean GTA V has a few of those RPG elements to it, but it isn't actually an RPG in my opinion. Cyberpunk 2077 is, from its structure up, it's built as an RPG. It has utterly deep levels of character customisation. It has utterly deep levels of character progression and fine tuning and upgrading.  The point is to Cyberpunk, you can play it how you want. With tons of side quests, upgrades, people to meet, romances to have and even cheat to win variables, Cyberpunk even though it looks a lot like GTA on the surface, it goes way, way deeper. Okay, so life as V, who is the person you create to own this place. This all takes place in and around Night City, an absolute huge area with six districts which all have mini districts and a city which is surrounded by even more space. It's been estimated Night City is about twice as big as Los Santos within GTA V, but it doesn't end there with Cyberpunk as Night City is also very vertical. This means buildings are extremely tall, compact and also for the most part, explorable and extremely detailed. The city is very busy and packed full of things to do, find, complete and take part in. And then there is the surrounding Badlands, which is a lot of emptiness but goodness at the same time, trust me people, you won't be sure of things to do right here. So we've just spoke about the city and the surrounding area, but it's also being confirmed that every single street within Night City has been named, and that's just one level of depth we see in this game. And well just think about that for a second, every single street, every single road has its own very name, it's like wow. Also confirmed, over 1000 NPCs, non-playable characters, live days by routines, which can consist of waking up, going to work, working all day, crazy amounts of different jobs, returning home etc etc, or who you can interact with also. So again, think about that for a second, over 1000 NPCs, characters you will see walking around those streets will be living by daily routines, people that is unheard of. And because of these few features I just spoke about, the world is said to truly feel alive, like nothing you could have imagined.  But yeah, the atmosphere, the non-playable characters living their own lives, the way in which things act out, and the way you can intervene with people's lives, is crazy stuffed people. Talking about lives, your life path, which you have to choose, has significant effects on your playthrough. Choices and characters are completely different at times throughout the game, depending on your life path selection. So your life path is basically a three way option, street kid, corpo and nomad. Now we won't get into the details of each, but each drastically changes the way you play the game, and well, the way the game plays you. Each life path comes with benefits, so for instance, as a street kid, you'll be known by a lot of the street level gangs, where if you go nomad, and you're from outside the city, that role is obviously reversed, and like I said, depending on the actual life path you choose, street kid, corpo or nomad, the entire game and all those encounters will obviously play out differently. And this is an option I feel you definitely want to think about before deciding which to go with. So with life path picked, it's now about character customisation, and well guys, I think what's probably not after here is more than you need. From eye-collected genitalia sizes, the game has it all, and I could go on forever explaining what can be done. It is truly staggering and well, I cannot wait to see some of your creations, and well it goes beyond just general look, because there's also cyberware. Now cyberware is any cybernetic technology permanently grafted to the human body, especially technology that interfaces with the human nervous system. Cyberware generally refers to technology that interacts with, or acts in place of, the human nervous system guys, and while within this game, there are truly tons of options here for you to further more customise your character, but obviously cyberware goes further than just looks and cosmetics. It affects the way in which you play the game in many different ways, from interacting with computers and cars, to combat and movement. We then have your skill tree, and like a real RPG, this thing is super deep. Now when you create your character, you will have set attributes you put points into, which you can further more do so while playing the game and earning more attribute points, and also at the same time earn perk points. Now from this skill tree to just about everything on this top ten list, I have made deep guides on which you will find linked via the playlist in the video description. Now the skill tree is built up around five major attributes, like I said, all of these have different branches where other perks can be built into, all affecting the way in which you play the game, from hacking to combat to movement, to weapon benefits, and much, much more. The skill tree here people is utterly deep, it's unreal. And lastly guys, weapons. So as you can imagine, weapons are another major part of this game. I mean you don't have to use them if you don't want to, but they are there on offer, just in case if you decide to. So we have three main weapon types in this game, which I will state, many weapon manufacturers contribute towards giving us quite a big arsenal of weapons. Many yet to be revealed, I do believe, but we have power weapons, smart weapons, and tech weapons. Power weapons are conventional guns in the sense that they use traditional ammo, calibers, and cartridges. This category includes a wide variety of weapons including polymer one-shots, which are disposable, revolvers, SMGs, machine guns, shotguns, and pistols. High rate of fire accompanied by high recoil is a feature most power weapons have. Tech weapons are weapons that use railgun technology, firing projectiles that are propelled with an electromagnetic charge. What they give up in terms of rate of fire, they make up for in penetration effects, depending on how long the charge is held for. These weapons are caseless ammunition, typically jacketed in steel for the sheath. Now smart weapons use gyro jet technology to fire caseless guided ammo at enemies. This technology was first developed in the 1960s, but was unreliable during combat. In 2077 there are different and pinpoint accurate. Smart weapons are tied to weapon grips and upticks to accurately scan and track movement of targets. But yes, it really doesn't end there with weapons because there are melee weapons too, like swords, sledgehammers, and more. Upgrades to your skill tree so you can tear turrets from their bearings and use them. And wow, much, much more people as you can imagine. And guys, that is it. The first 10 things I feel you need to know going into or newly learning about Cyberpunk 2077.  So guys, if you enjoyed this video and want to see more, leaving a like button truly helps me out and I appreciate that support on my channel. But guys, the end of the video was here. Hopefully you enjoyed it and hopefully I will see you on that next one.


Hello, and welcome back to theCUBE. We're here in Palo Alto, California. I'm your host, John Furrier. We have a special guest here in the studio. As part of our Cloud Native SecurityCon coverage, we had the opportunity to bring in John Turro, who is the partner at Madrona Venture Partners, formerly with AWS, to talk about machine learning, foundational models, and how the future of AI is going to be impacted by some of the innovation around what's going on in the industry. ChatGPT has taken the world by storm, the million downloads, fastest to the million downloads there before summer saying it's just a gimmick, others saying it's a game changer. John's here to break it down, and great to have you on. Thanks for coming in. Thanks, John. Glad to be here. Thanks for coming on. So first of all, I'm glad you're here. First of all, because two things. One, you were formerly with AWS, got a lot of experience running projects at AWS. Now a partner at Madrona, great firm, doing great deals, and they had this future at modern application kind of thesis. Now you're putting out some content recently around foundational models. You're deep into computer vision. You were the IoT general manager at AWS, among other things, Greengrass. So you know a lot about data. You know a lot about some of this automation, some of the edge stuff. You've been in the middle of all these kind of areas that now seem to be the next wave coming. So I wanted to ask you what your thoughts are of how the machine learning and this new automation wave is coming in. This AI tools are coming out. Is it a platform? Is it going to be smarter? What feeds AI? What's your take on this whole foundational, big movement into AI? What's your general reaction to all this? So thanks John again for having me here. Really excited to talk about these things. AI has been coming for a long time. It's been kind of the next big thing, always just over the horizon for quite some time. And we've seen really compelling applications in generations before and until now. Amazon and AWS have introduced a lot of them. My firm Madrona Adventure Group has invested in some of those early players as well. But what we're seeing now is something categorically different. That's really exciting and feels like a durable change. I can try and explain what that is. We have these really large models that are useful in a general way. They can be applied to a lot of different tasks beyond the specific task that the designers envisioned. That makes them more flexible. That makes them more useful for building applications than what we've seen before. And so that, we can talk about the depth of it, but in a nutshell, that's why I think people are really excited. And I think one of the things that you wrote about that jumped out at me is that this seems to be this moment where there's been multiple decades of nerds and computer scientists and programmers and data thinkers around waiting for AI to blossom. And it's like, they're scratching that itch every year is going to be. And it's like, the bottleneck's always been compute power. And we've seen other areas, genome sequencing, all kinds of high computation things were required, high performance computing. But now there's no real bottleneck to compute. You got cloud. And so you start to see the emergence of a massive acceleration of where AI's been and where it needs to be go now. It's almost like it's got to reboot. It's almost a renaissance in the AI community with a whole nother macro environmental things happening, cloud, younger generation, applications proliferate from mobile to cloud native. It's the perfect storm for this kind of moment to switch over. Am I over reading that? Is that right? You're right. And it's been cooking for a cycle or two. And let me try and explain why that is. We have cloud and AWS launch in whatever it was 2006 and offered more compute to more people than really was possible before. Initially, that was about taking existing applications and running them more easily in a bigger scale. But in that period of time, what's also become possible is new kinds of computation that really weren't practical or even possible without that vast amount of compute. And so one result that came with that is something called the Transformer AI Model Architecture and Google came out with that, published a paper in 2017. And what that says is with a Transformer model, you can actually train an arbitrarily large amount of data into a model and see what happens. That's what Google demonstrated in 2017. The what happens is the really exciting part because when you do that, what you start to see, when models exceed a certain size that we had never really seen before, all of a sudden they get what we call emerging capabilities of complex reasoning and reasoning outside of domain and reasoning with data. The kinds of things that people describe as spooky when they play with something like chat GPT, that's the underlying term. We don't, as an industry, quite know why it happens or how it happens, but we can measure that it does. So cloud enables new kinds of math and science. New kinds of math and science allow new kinds of experimentation. And that experimentation has led to this new generation of models. So one of the debates we had on theCUBE at our super cloud event last month was what's the barriers to entry of say open AI, for instance? I'll see, and I weighed in aggressively and said the barriers for getting into cloud are high because of all the CAPX and how we shoe formerly VM where now at ZScale he's an AI machine learning guy. He was like, well, you can spend $100 million and replicate it. I saw a quote that said for 180,000 I can get this other package. What's the barriers to entry? Is chat GPT or open AI? Does it have sustainability? Is it easy to get into? What is the market like for AI? Because a lot of entrepreneurs are jumping in. I mean, I just read a story today. San Francisco's got more inbound migration because of the AI action happening. Seattle's booming. Boston with MIT's been working on neural networks for generations. That's what we found the answer.  So there's total excitement for this. People are enthusiastic around this area. You can think of an iPhone versus Android tension that's happening today. There's in the iPhone world, there are proprietary models from open AI who you might consider as the leader. There's Cohere, there's AL21, there's Anthropic. Google's gonna have their own and a few others. These are proprietary models that developers can build on top of, get started really quickly. They're measured to have the highest accuracy and the highest performance today. That's the proprietary side. On the other side, there is an open source part of the world. These are a proliferation of model architectures that developers and practitioners can take off the shelf and train themselves. Typically found in hugging face. What people seem to think is that the accuracy and performance of the open source models is something like 18 to 20 months behind the accuracy and performance of the proprietary models. But on the other hand, there's infinite flexibility for teams that are capable enough. So you're gonna see teams choose sides based on whether they want speed or flexibility. That's interesting and that brings up a point I was talking to a startup and the debate was do you abstract away from the hardware and be software defined or software led on the AI side and let the hardware side just extremely accelerate on its own because it's flywheel. So again, back to proprietary. That's with hardware kind of bundled in bolted on. Is it accelerated or is it bolted on? Is it part of it? So to me, I think that the big struggle and understanding this is that which one will end up being right? I mean, is it a Betamax versus VHS kind of thing going on or iPhone, Android? I mean, iPhone makes a lot of sense, but if you're Apple, but is there an Apple moment in the machine learning? In proprietary models, there does seem to be a jump ball that there's gonna be a virtuous flywheel that emerges that for example, all this excitement about chat GPT what's really exciting about it is it's really easy to use it's the technology isn't so different from what we've seen before even from open AI. You mentioned a million users and in a short period of time all providing training data for open AI that makes their underlying models their next generation even better. So it's not unreasonable to guess that there's gonna be power laws that emerge on the proprietary side. What I think history has shown is that iPhone, Android, Windows, Linux there seems to be gravity towards this yin and yang and my guess and what other people seem to think is gonna be the case is that we're gonna continue to see these two poles of AI. So let's get into the relationship with data because I've been immersing myself with chat GPT fascinated by the ease of use, yes but also the fidelity of how you query it and I felt like when I was doing writing SQL back in the 80s and 90s where SQL was emerging you had to be really a guru at the SQL to get the answers you wanted. It seems like the querying into chat GPT is a good thing if you know how to talk to it labeling whether your input is and it does a great job if you feed it right. If you ask a generic question like Google it's like a Google search, it gives you great format sounds credible but the facts are kind of wrong. That's right. That's where general consensus is coming on. So okay, what does that mean? That means people are on one hand saying ah, it's bullshit because it's wrong but I look I'm like, well that's compelling because if you feed it the right data so now we're in the data modeling here. So the role of data to be critical is there a data operating system emerging because if this thing continues to go the way it's going you can almost imagine as you and look at companies to invest in who's going to be right on this what's going to scale, what's sustainable what could build a durable company it might not look like what people think it is. I mean, I remember when Google started everyone thought it was the worst search engine because it wasn't a portal but it was the best organic search on the planet that became successful. So I'm trying to figure out like okay how do you read this? How do you read the tea leaves? Yeah, so there are a few different ways that companies can differentiate themselves. Teams with galactic capabilities to take an open source model and then change the architecture and retrain and go down to the silicon. They can do things that might not have been possible for other teams to do. There's a company that we're proud to be investors in called Runway ML that provides video accelerated, sorry AI accelerated video editing capabilities that were used in everything everywhere all at once and some others. In order to build Runway ML they needed a vision of what the future was going to look like and they needed to make deep contributions to the science that was going to enable all of that. But not every team has those capabilities maybe nor should they. So as far as how other teams are going to differentiate there's a couple of things that they can do. One is called prompt engineering where they sort of shape on behalf of their own users exactly how the prompts get fed to the underlying model. It's not clear whether that's going to be a durable problem or whether like Google we consumers are going to start to get more intuitive about this that's one. The second is what's called information retrieval. How can I get information about the world outside information from a database or a data store or whatever service into these models so they can reason about them. And the third is this is going to sound funny but attribution just like you would do in a news report or an academic paper if you can state where your facts are coming from the downstream consumer or the human being who has to use that information actually is going to be able to make better sense of it and rely better on it. So that's prompt engineering that's retrieval and that's attribution. So that's that brings me to my next point I want to dig in on is the foundational model stack that you've published. And I'll start by saying that with chat GPT on the if you take out the naysayers who were like throwing cold water on it about being a gimmick or whatever and then you got the other side I would call the alpha nerds who are like they can see wow this is amazing this is truly next gen this isn't yesterday's chat bot nonsense they're like they're all over it is that everybody's using it right now in every vertical I heard someone using it for security logs, I heard a data center hardware vendor using it for pushing out app sec review updates. I mean I've heard corner cases we're using it for the cube to put our metadata in so there's a horizontal use case of value so to me that tells me there's a market there so when you have horizontal scalability in the use case you're going to have a stack so you publish the stack and it has an application at the top applications like Jasper out there you're seeing chat GPT but you go after the bottom you got Silicon Cloud, foundational model operations the foundational models themselves tooling sources actions where'd you get this from how'd you put this together did you just work backwards from the startups or was there a thesis behind this could you share your thoughts behind this this foundational model stack? Sure so well I'm a recovering product manager and my job that I think about as a product manager is who is my customer and what problem he wants to solve and so to put myself in the mindset of an application developer and a founder who is actually my customer as a partner at Madrona I think about what technology and resources does she need to be really powerful to be able to take a brilliant idea and actually bring that to life and if you spend time with that community which I do and I've met with hundreds of founders now who are trying to do exactly this you can see that this stack is emerging in fact we first drew it not in January 2023 but October 2022 and if you look at the difference between the October 22 and January 23 stacks you're gonna see that holes in the stack that we identified in October around tooling and around foundation model ops and the rest are organically starting to get filled because of how much demand for the developers at the top of the stack if you look at the young generation coming out and even some of the analysts I was just reading an analyst report on who's following the whole data stacks area you know data bricks, snowflakes a variety of analytics, real-time AI data's hot there's a lot of engineers coming out that we either data scientists or I would call data platform engineering folks are becoming very key resources in this area what's the skill set emerging and what's the mindset of that entrepreneur that sees the opportunity how does these startups come together is there a pattern in the formation is there a pattern in the competency or proficiency around the talent behind these ventures yes I would say there's kind of two groups the first is the first is a very distinct pattern John for the past 10 years or a little more we've seen a pattern of democratization of ML where more and more people had access to this powerful science and technology and since about 2017 with the rise of the transformer architecture in these foundation models that pattern has reversed all of a sudden what has become broader access is now shrinking to a pretty small group of scientists who can actually train and manipulate the architectures of these models themselves so that's one and what that means is the teams who can do that have huge ability to make the future happen in ways that other people don't have access to yet that's one the second is there is a broader population of people who by definition has even more collective imagination because there's even more people who sees what should be possible and can use things like the proprietary models like the open AI models that are available off the shelf and try to create something that maybe nobody has seen before and when they do that you know Jasper AI is a great example of that Jasper AI is a company that creates marketing copy automatically with generative models such as GPT-3 they do that and it's really useful and it's almost fun for a marketer to use that but there are gonna be questions of how they can defend that against someone else who has access to the same technology it's a different population of founders who has to find other sources of differentiation without being able to go all the way down to the silicon and the science yeah and it's gonna be also opportunity recognition is one thing building a viable venture product market fit you got competition and so when things get crowded you got to have some differentiation I think that's going to be the key and that's where I was trying to figure out and I think data, scale, I think are big ones where's the vulnerability in the stack in terms of gaps, where's the white space I shouldn't say vulnerability I should say where's the opportunity where's the white space in the stack that you see opportunities for entrepreneurs to tackle I would say there's two at the application level there is almost infinite opportunity John because almost every kind of application is about to be reimagined or disrupted with a new generation that takes advantage of this really powerful new technology and so if there is a kind of application in almost any vertical it's hard to rule something out almost any vertical that a founder wishes she had created the original app in well now it's her time so that's one the second is if you look at the tooling layer that we discussed tooling is a really powerful way that you can provide more flexibility to app developers to get more differentiation for themselves and the tooling layer is still forming that's this is the interface between the models themselves and the applications tools that help bring in data as you mentioned connect to external actions bring context across multiple calls chain together multiple models these kinds of things there's huge opportunity there well John I really appreciate you coming in and I have a couple more questions but I will take a minute to read some of your bios for the audience and we'll get into I want to embarrass you but I want to have context you said you were covering product manager 10 plus years at AWS so now see we're covering from AWS which is a whole nother dimension we're covering all in all seriousness I talked to Andy Jassy around that time and Dr. Matt Wood and it was about that time when AI was just getting on the radar when they started so you guys started seeing the wave coming in early on so I remember at that time as Amazon was starting to grow significantly and even just stock price and overall growth from a tech perspective it was pretty clear what was coming so you were there when this tsunami hit that's right and you had a front row seat building tech you were led the product teams for computer vision AI text track AI intelligence for document processing recognition for image and video analysis you wrote the business product plan for AWS IoT in green grass which we've covered a lot on theCUBE which extends out to the whole edge thing so you know a lot about AI ML edge computing IoT messaging which I call the law of small numbers that scale become big this is a big new thing so as a former AWS leader who's been there and at Madrona what's your investment thesis as you start to peruse the landscape and talk to entrepreneurs you have got the stack what's the big picture what are you looking for what's the thesis and how do you see this next five years emerging? Five years is a really long time given some of this science is only six months out I'll start with some no pun intended some foundational things and we can talk about some implications of the technology the basics are the same as they've always been we want what I like to call customers with their hair on fire that have problems so urgent they'll buy half a product you know the joke is if your hair is on fire you might want a bucket of cold water but you'll take a tennis racket and you'll beat yourself over the head to put the fire out you want those customers because they'll meet you more than halfway and when you find them you can obsess about them and you can get better every day so we want customers with their hair on fire we want founders who are who have empathy for those customers understand what is going to be required to serve them really well and have what I like to call founder market fit to be able to build the products that those customers are going to need and because that's a good strategy for an emerging not yet fully baked out requirements definition enough where directionally they're leaning in more than in they're part of the product development process that's right and you know when you're doing early stage development which is where I personally spend a lot of my time at the seed and A and a little bit beyond that stage often that's going to be what you have to go on because the future is going to be so complex that you can't see the curves beyond it but if you have customers with their hair on fire and talented founders who have the capability to serve those customers that's got me interested So if I'm an entrepreneur I walk in and say I have customers that have their hair on fire what kind of checks do you write what's the kind of the average you're seeing for seed and series probably seed rounds and series A you know it can depend I have seen seed rounds of double digit million dollars I have seen seed rounds much smaller than that it really depends on what is going to be the right thing for these founders to prove out the hypothesis that they're testing that says look we have this customer with her hair on fire we think we can build at least a tennis racket that she can use to start beating herself over the head and put the fire out and then we're going to have something really interesting that we can scale up from there and we can make the future happen So it sounds like your advice to founders is go out and find some customers show them a product don't obsess over full completion get some sort of vibe on fit and go from there Yeah I think by the time founders come to meet they may not have a product they may not have a deck but if they have a customer with her hair on fire then I'm really interested Well I always love the professional services angle on these markets you go in and you get some business and you understand it walk away if you don't like it but you see the hair on fire then you go in product mode That's right Alright John thank you for coming on theCUBE really appreciate you stopping by the studio and good luck on your investment great to see you and thanks for coming on Okay thank you John CUBE coverage here and Palo Alto I'm John Furrier your host more coverage with CUBE Conversations after this break


Welcome to the Crypto Minute Miami edition. It's the one year anniversary of Ethereum's beacon chain. Goldman Sachs is working on a way to accept Bitcoin as low and collateral. Bitcoin network now handles more volume than PayPal. Imagine being a no-corner. MicroStrategy buys another $414 million of Bitcoin. Thanks for calling the emergency cryptocurrency hotline. Please hodl. We're all gonna make it! Will all season come faster if I keep refreshing? Twitter adds an Ethereum tipping option. It always lets you spot Bitcoin ETF in Canada this week. Randy Deckerberg joined Bitcoin as an advisor. CashUp now supports Bitcoin tap groups. Budweiser changed their profile name to beer.e. Ross Holberg is launching an NFT. The SEC just rejected the Bitcoin ETF application from Wisdomtree. Square is changing its name to Block. And Square rebrands to SpiralBTC. Buy the dip. I guess Facebook is gonna start allowing crypto ads again. Do you take Miami Coin? Is ETH gonna flip Bitcoin? The market decided to have a Block Friday sale. I mean art basil. Yeah, there you go. We are still so early. $106 million of Metaverse land was sold last week. Keep stacking and I'll see you next week.


There's fear in the world. Conspiracy theories saying 5G networks are evil, that the 5G waves will let the government control our minds, boil our organs from the inside, and turn the world into an unlivable hellscape. But me? I think it's good. Once I heard about this 5G, I needed to get as close as possible to the source. I like being up here. The chemtrails are much thicker. Delicious. It was a long journey, so I made sure to put extra fluoride in my drinking water. It makes my teeth sharp, like the wolf meat. I made this journey for my son. He's sick. The doctor said my wicked rituals and energy therapy wouldn't help. I need to find a cure. If these towers can do all these things that people are afraid of, then maybe, just maybe, I can figure out a way for them to help my son. People call me a conspiracy theorist like it's a bad thing, but who's to say there aren't good conspiracies? Like how JFK was really killed by the CIA. I think they made the right call. And some are harmless. I mean, the Earth is definitely flat, but I totally get why everyone thinks of it as a bang bouncy ball. It's way more fun. Some are just misunderstood. The people who said the world would end in 2012 were correct. It's just not really 2012 yet. Every day under the tower I feel stronger. I feel the radiation seeping into my pores. It even neutralized the negative ions from the crystal I swallowed back in Woodstock 99. I destroyed all of my favorite tinfoil hats as they just blocked the signals. I'm sorry son, but you'll just have to wait. Something is telling me I need to find more ways to harness the beams or I'll lose my strength. I don't care if I grow a third leg. They'd be sweet. I'd be so fast. The moon landing? Yeah, that was fake. And I'm glad it was. It would have been way too expensive to go there on the taxpayers' dime. If everyone's mad, they won't let us see the aliens in Area 51. But honestly, those things are scary. I've seen Independence Day. Let the boys in blue handle that one. Vaccines? Yeah, they make you infertile, but that'll be way cheaper than any of the other procedures I was considering. After weeks under the tower I can hear her whispers in the waves. She tells me what she sees, what she feels. She can hear those who insult her. The ones who spread lies, claiming she would do harm. Worse are those that would treat her like an object. A tool for ambitious new online experiences and enhanced streaming capabilities. My son is the worst of them all. I think he could take advantage of her just because he's sick. He's not strong enough for her. Just a taste of her power and his puny body would instantly explode. You know, everyone flips out because the Illuminati runs the world government, but I can't thank them enough for creating her and giving me a reason to live. She fills my body with her power, her love. She wonders what if she gave me more if I let her in deeper. She fills my hesitant seed. She asks me, is it now or losing myself? If I gain the world, I just need to trust it. I need to trust her.


Hi, this is Yoastapil Bhartiya and welcome to another episode of T3M, our topic of this month and the topic of this month is data. And today we have with us once again, Eric Summer, CEO of Decodable. Eric is here to help you on the show. Thanks for having me back. And when we look at data, today's focus, because data is a very big topic, is mostly about building real-time data pipelines.  So we're a stream processing platform. You know, that means that we connect to source systems, we acquire data, we do whatever kind of processing makes sense for a particular use case, and then write that result into everything from event streaming systems to data warehouses, data lakes, and real-time OLAP database systems. I would like to understand a bit about the evolution and the origin of stream, you know, data real-time because there are a lot of terms that we use there. From the early days versus today, what was the driver, why we need, you know, streaming data, real-time data pipelines. And of course, then we'll talk about the other aspect. But I do want to understand a bit about the history origin. Stream processing and real-time data is not necessarily new. It's been around for quite some time. I think the thing that has changed over the last couple of years to kind of bring it to the fore again for people is this idea that the use cases that people are seeing are just becoming more interesting. So in retail, it's about package tracking and delivery tracking. It's about what's currently in stock and inventory management. It's about engagement of customers and logistics, again, delivery tracking and just like knowing where things are, supply chain management and those kinds of things. So I think it's really that the use cases have finally caught up to the technology and made the technology interesting for people again. And I think that that is what has brought things like Kafka and Apache Flink and all these other kinds of open source projects to the fore. And since you brought Apache Flink and open source, I want to understand a bit about of course, if you look at a lot of data-specific projects, a lot of game from Berkeley, you know, actually, I think I called them the whole mafia, most of them they came from there. So talk a bit about the importance of open source for decodable and which open source product that you folks consume and contribute to. I think these days, most enterprise data infrastructure has some either open source, open core component or a set of de facto standards that are critically important to that particular product or service. I mean, you know, Snowflake, for instance, has recently gotten support for things like Apache Iceberg as external table support. At decodable, we are heavily based on Apache Flink, which is this 10 year old project that came out of a university in Berlin that is all about, you know, the processing and real-time transformation of data. So Apache Flink and DeVizia, which is a changed and captured product for pulling data out of relational database, operational databases for things like replication. And eventually in our case, stream processing and what happens downstream in terms of the criticality of these projects and the health of the community, I think that, I mean, quite frankly, the more robust the open source project is, the more there is a community and ecosystem around this. And, you know, from the customer's perspective, this is a good thing because they're not locked into decodable or any other vendor, you know, whoever they may be with respect to their data, which gives them leverage, quite frankly, over people like us. And for us, it creates this enormous ecosystem of connectors of, you know, adjacent technologies and these kinds of things that allow us to better serve the use cases that customers are looking for. And so we are pretty active in those two communities. And then there's a bunch of what I would call, I don't want to say ancillary communities, but sort of other open source projects that are critical to us around file formats and all these other kinds of things that I think have been adopted as, like I said, de facto standards in data warehousing and data platforms in general.  Yeah. I mean, Apache Flank has been the foundation of some of the most sophisticated real time businesses. So some of the noteworthy open source users of Apache Flank are people like Uber and Lyft and Netflix, Stripe, you know, various folks use entire businesses predicated on the idea of being able to perform analysis or take action in real time, which gives us quite a bit of confidence that this really is the right platform. So Apache Flank is absolutely the de facto industry standard for real time stream processing, which is one of the reasons why we chose it. And of course, like I said, the ecosystem around it and the, you know, the support for all these other technologies is absolutely critical, I think, for both us and customers. When you look at specifically real time data by plan, how challenging it is for engineers or teams to kind of, you know, of course, build a whole strategy there or is something, you know, easier, as easy as the setting of a lamp is that server. So let's talk about how complicated or challenging it is in reality. And then we'll talk about, you know, what folks like decodable do to help them. I think a lot of people get sort of twisted up around data strategies and these kinds of things that are very abstract and hard to pin down. So, you know, having looked at hundreds at this point, maybe thousands of these kinds of data platform projects or sort of the AI ML stuff that kind of comes out the other side of it, that people are probably even more interested in, I would say, like, start at the end and work backwards. What do you want to get out of this? What is the either cost savings or revenue growth opportunity? You're like, whatever it is that you're measuring, that's like, start there and work backwards rather than boiling the ocean and thinking about data and data strategies. Cause I think like the data is a means to an end, right? Like the actual thing that's meaningful is what happens, you know, out the other side. So, you know, I think that that to me is the is the makes a lot of things clear and sort of helps people to cut through some of the busy work or sort of the the ideological debate about sort of like, how do you structure these these platforms and strategies and those kinds of things.   I think that more so than anything, rallying around a small set of technologies, pick one thing for each function in a platform. And again, I would never take your eye off of what are you looking to get out of this versus, you know, the the the logo bingo card that I think a lot of data platforms wind up turning into I mean, from our perspective, you know, having a way to capture process and ingest data into analytical systems, having jobs that run either on that stream or in those analytical systems that power applications, I think is a pretty there's an easy way to distill this down without getting too wrapped up in sort of the hundreds of different logos and projects that that potentially get into this. So I think I mean, that's probably not that's probably not the most actionable advice, but but I think that, you know, from our perspective, reducing the number of platforms that people have to deal with for decodable that is stream processing, ETL and ELT and a single platform, you know, that sits between all the different systems, sort of like the network in between different applications and hosts winds up reducing the complexity of the overall platform. We think that's important for people to be able to be successful. When we talk about data, of course, one of the there are a couple of critical things that it comes to is, of course, data integrity security is important backup HR at high availability disaster recovery, because application can go and come back with data if that's gone, that's critical. Talk a bit about the scope of decodable when it comes to once again, security or other aspects associated with data. Decodable's role in this, like I said, is to really say in between a bunch of different systems. And so when you think about business continuity and disaster recovery and availability, typically, there's a couple of things. One is what's the durability of this data? Once it's captured, is it going to be around if the lights go out, you know, and then secondarily, if an entire cloud availability zone or region disappears? And I think that, you know, for us, that means capturing the data that means persisting in a way that is resilient to temporary failures, you know, think that we will eventually recover from and then facilitating the transfer of that data and the fanning out, you know, from one cloud region to potentially multiple cloud regions, such that if you lose a region or availability zone that you actually have that data. It's a relatively sophisticated set of constraints to solve for. And I will say that it's always like most things a cost versus risk profile tradeoff, right? So it really depends on the criticality of the data, the use cases. I think if you're talking about purchase and transaction data, this data is critical for like a retailer, then there might be other kinds of data where you're not necessarily willing to make that investment. So I do think that you need sort of a framework for thinking about these kinds of things. For us, the way we help facilitate that is we are the system that is reliably capturing this data and then not just processing it, but moving it around between, like I said, various availability zones and regions of the cloud case. So we can be a system, I won't claim to be like a backup or disaster recovery solution, but we can be part of a larger strategy around sort of how do I deal with the loss of, you know, you know, U.S. East One in Amazon or something like that, you know, those kinds of situations. And sometimes it's not just disaster recovery, just like global businesses that have many, many regions where plenty of presence throughout the globe and need to centralize parts of that data for analysis in the system like Snowflake or Databricks for analysis and model training and those kinds of things. So I think that all of those kinds of things are working in concert to come up with a larger architecture, deployment architecture around, you know, how do you structure and get a platform that is serving not just the use case, but solving for some of these other concerns around security and disaster recovery and those kinds of things. What kind of cultural shift you're saying when it comes to data? It's a really interesting question.  And I think the big shift that we are seeing that I think is consistent with the larger trend in enterprises generally is this notion of self-service, you know, and that means, you know, enabling the people who need to do the analysis that needs to train, build and train the model to be able to acquire the data that they need, assuming they have the rights to do so, acquire the data they need, when they need it, without having to hop between team boundaries and file tickets and fill out forms and those kinds of things in order to get their job done.  So I think this notion of self-service is the difference between projects that take, you know, days and weeks and months and years, right, and I think that to me, and obviously, like there's the ML AI trend and, you know, all of the sophisticated analysis that people are doing around this data, I won't claim to be an expert in sort of the analysis. I mean, I'm more on the infrastructure and data platform side, but I think that in order, I mean, we know that those people get the best results out of those efforts when they are as close to the data as humanly possible, and that means being able to give them the right tools to get their job done. And I believe that that's largely centered around this notion of self-service. What advice do you have, how these efforts of all approach data, and not only approach, hey, do we really need real-time data pipelines or not, but actually, too, once again, as you said, they're buzzword-like, data strategy and things like that, but they should approach it appropriately so that, irrespective of, because eventually everybody will be using some form of generative AI or data there. So not only they are prepared, but they're also, once again, more efficient with whatever they are doing. I think that this is gonna be largely centered around the organizational component of it and then the technology component of it. And I think from a technology component, obviously I have a horse in this race and so I'm a little bit biased, but I mean, I think that having a small set of powerful primitives to have a data platform that is comprised of real-time ingest and processing, obviously some kind of analytical database or system for sort of the historical storage and processing of data and then whatever tools around that, whether it's programming languages and notebooks and model training and like all the other sort of data quality and governance tooling that you need around that, I would say make sure that you understand how all those pieces fit together. Not just like logos on a whiteboard, like I said, but really and truly understand how the team is going to be able to work with those tools in order to accomplish what they're trying to accomplish on the other side. And then I think on that organizational side, it's really about enabling self-service, making sure that people are really clear about what the goals are, not just what we're asking them to do on a daily basis, but like ultimately what they're gold around so that they have a really solid understanding of why they are doing what they're doing so that when they go to actually do that work, they can make reasonable trade-offs around all of the different attributes and especially focus on the most important parts, whether it's data quality, whether it's timeliness, all the other drivers that are gonna be super critical in order to make any particular project successful. So if you're a chief data officer or CIO, I mean, those are the kinds of things that I would be thinking about. I think that the technology and the vendors that you bring in to facilitate that are probably the easy part. Of building that, obviously, like I said, I mean, I think we're a big part of that and there's plenty of other technologies that sort of go along with that. But that's sort of how I look at it and where I've seen people be the most successful. Eric, thank you so much for taking time out today and of course talk about data, Flink, of course, decodable. And as a result, I would love to have you back on the show. Thank you. Really appreciate it. Thanks so much.


Is human nature really that bad? I don't know though, is there some balance there with valuing yourself and finding people with stronger character? Yeah, I mean absolutely. It's just one of those things where it's not necessarily human nature that I'd say that is.  We live in a world where a lot of people are hurt. We live in a world where men and women are hurting each other all the time and so most men and most women that you meet have a wall up and they're trying to protect themselves because they're afraid of getting hurt and they're just taking, taking, taking, taking as much as they can or they're giving, giving, giving, giving and they are trying to be a martyr by talking about how much they're giving and how awesome they are by giving and so we live in a world where there's a lot of, a lot of takers out there that it's not that they necessarily want to hurt people but that they've lived in a world where the only way that they can get by is to take and to be selfish and to, you know, take advantage of whatever situation they possibly can and so it's not that they're bad. It's just that that's what they're used to. That's the only world that they know and they understand. There's a game out there. It's called the Red Black Game and there's a bunch of different kind of companies and seminar groups that use this. The one that I'm familiar with is a company called PSI Seminars and if you play that game you'll learn a lot about how people unintentionally hurt each other and it's really, really interesting. It's a real fascinating game. If you guys have a PSI Seminars around you you should go and check out that program because it's just that one game in and of itself is incredibly valuable just to, just to watch it you know and see how most people are just, they're taking or they're giving and they're being martyrs or whatever. It's real interesting. DD says, I totally hear you on people not having respect for themselves. There are times I try to guide men into having respect but they are wet noodles. Such a turnoff. Yeah, I mean it's, you can't really, I was actually, I was hanging out with with another dating coach today. I was hanging out with this guy who's a men's dating coach and we were chatting about this, this idea right. We're talking about kind of like the MGTOW movement and a lot of the incels, the involuntarily celibate guys that are out there and what can be done with them and one of the things that we talked about is how you can't really help them necessarily. I mean you can give them a path but the path is already there for them to help themselves and as long as you're trying to do something to help them you're just enabling them to be weak for the most part and they usually don't get up on you know usually change doesn't happen by you being like hey here try to be better try to be stronger try to value yourself try to respect yourself. Change usually doesn't happen from that. Change usually happens when somebody feels so much pain that they're like I can't live with this pain anymore and I have to do something about it and so they go out and they try to do a bunch of new things until they find something that either works or they completely give up all together and a lot of those and a lot of guys have given up and so you can't really, you can't force a guy to respect himself just like we can't force you to respect yourself and value yourself. It's a path that you have to go on and you have to step up and do otherwise you're going to have problems with it for the rest of your life and that's why it's so important for personal growth. Personal growth is in my opinion by far the most important thing that you can possibly do in your life is just continue to grow continue to work on things that are going on within yourself so that you can become the person who attracts the things that you want in your life. We were talking about that too actually this concept where there's a lot of a lot of people out there there's all these people out there and they all want these really high standards and you can see it all over our chat boards on our YouTube channel and in our community and all over the place is you have all these people and in the men's communities and stuff it's all the same it's the same across the board on the men's side and on the women's side you have all these people and they keep raising their standards for the kind of person that they want to be with but the problem is is they're not raising their standards for themselves they're just raising their standards for the other person right and it was it was this thing I was talking about yesterday in the live stream where I was talking about this guy that I know that wants he was telling me the last time I was hanging out with him that he only wants to date women who have been working on he's in his I think he's in his 50s or something and he only wants to date women who have been exercising and taking care of their bodies for at least the last 20 years you know and you look at him and you're like well dude you have a big gut and you don't exercise and you smoke and your your skin is like peeling off from you know this autoimmune disease that you have and you're you I mean like a woman who has been working out for 20 years why would she want anything to do with you right like you you you have these high standards but you don't have high standards for yourself just for the other person and so you're not willing that there's all these people out there that aren't willing to do what's necessary to deserve the other people the type of person that they want they just raise their standards up and then they do whatever they want to do and they complain that nobody's stepping up for them and they're wondering whoa why why don't you know why aren't guys willing to step up and you know be a man for me you know and it's like well are you willing to step up and be the kind of woman that the man is looking for you know and and so there's well that was a really long tangent but it's an important thing right it's important to wow I really I really went way off on that one okay so Donna says sounds good to me do you want me to meet you there oh are you talking about Romania okay


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