EP #7: How Observability and AI Can Help the Human Brain in Solving and Preventing Cybercrime With Ramfis Adrichem

41 min listen

Ramfis Adrichem - Security Specialist

Nowadays, cybercrime is becoming more and more common. In an era where we're sharing so much personal data online, our data has become a real commodity. As a security specialist, Ramfis deals with cybercrime and threat handling all day long. In order to solve these problems, Ramfis relies first and foremost on the human brain, but then uses concepts like observability and machine learning to assist his brain. Therefore, we're excited to have him on the show to talk about his work and his out-of-the-box practices of observability and AI!

More specifically, Ramfis and Anthony talk about:

  • Why our data is becoming more and more valuable

  • Why cyber security is evolving from small 1-person initiatives to real businesses

  • Why Ramfis thinks the human brain is the most important computer

  • How concepts like observability and AI can help him in solving and preventing cybercrime

You can find a written transcript of the episode below. Enjoy the recording!

Please note that Ramfis did this interview on a personal account and that his statements reflect his personal opinions.

Episode transcript

Ramfis (00:00): Automize where you can, but utilize the human brain when it’s needed.

Annerieke (00:08): Hey there, and welcome to the StackPod. This is a podcast where we talk about all things related to observability, because that's what we do and that's what we're passionate about, but also what it's like to work in the ever-changing, dynamic tech industry. So if you are interested in that, you are definitely in the right place.

Annerieke (00:28): In this episode, we invited Ramfis Adrichem. As a security specialist, Ramfis deals with preventing cyber crime and threat handling all day long. Ramfis and Anthony talk about what kinds of cyber crime Ramfis deals with, why he states that the human brain is the most important computer and that you should never underestimate its power, and how Ramfis thinks observability tools and concepts like AI can support the human brain in solving and preventing cybercrime. Welcome to StackPod – and let’s get into it.

Anthony (01:02): Hello everybody. My name is Anthony Evans, thank you for listening to the StackPod, sponsored by StackState. Today I'm going to have a really interesting conversation with an actual customer of StackState, a guy by the name of Ramfis. Ramfis, do you want to introduce yourself, where you work and what you do?

Ramfis (01:25): Yeah, that'll do. Thank you, Anthony. Well, my name is Ramfis Adrichem. I am a security specialist, and my focus is on threat handling and looking at the threats currently running, things like ransomware but also what keeps me really awake at night, concerning security. No crying babies on that, but security side, the development and the evolution on things like business models within the cyber crime situation, but also threat actors like for instance countries.

Anthony (02:02): Yeah. Well, especially because you're working not just for a corporation. Yeah, you're definitely at the front line of more than a few threats, right?

Ramfis (02:22): Well, that's true but the trick is that everyone nowadays is under threat because if you look from a cyber crime perspective, it's all about money. If you have money, criminals can find a way to easily penetrate your environment and steal your information, then there's something to gain. From a threat actor perspective, it's more challenging because some countries are into espionage, other countries are into just stealing intellectual property or trying to undermine your government or your society. So that's more challenging and difficult. Certain governments are pretty interested in certain types of information, consider governments like Iran which is really focused on - because of boycotts - on information from universities. So, knowledge, especially knowledge. That can be challenging, because how do you handle those types of threats?

Anthony (03:29): Yeah. Yeah. I honestly empathize a lot in your scenario. I think we were talking about this previously, but there's been such a movement if you will to productize crime, especially on the internet and this new metaverse that is now around where you've gone from the anonymous people that would do things for the sake of truth and righteousness and exposing corporate greed and all this fun stuff, to now nations actually rigging elections, right? It's one of the biggest things that I personally find really bamboozling, especially living in America that more people aren't talking about election meddling. They're more worried about their neighbors meddling in the election than foreign actors, which is probably more so than ever before. You've also now got people that ride around in Lamborghinis and whatnot because they've made a criminal enterprise out of hacking data, receiving payments in cryptocurrency, getting knowledge, exposing it, corporate blackmail and all this kind of stuff. You've got so many different components there, right?

Ramfis (05:12): The thing is, from a traditional standpoint if you look at a hacker you see people with the dark hoodies or with anonymous masks lurking around in the dark edges of the internet. That's not true. Nowadays, especially if you look at cyber crime, take ransomware as an example, what you see is that it's a business. It's a business just like any other business, it has its own business developers. That's a first, it started just by locking your system with crypto algorithms, and stating, "Listen, pay me five bitcoins and then you're off of me." The second thing was people didn't pay, or companies like anti-viral companies or for instance, the police, health companies, got hit by ransomware to get a key to unlock their data. So the next thing was, listen, I'm going to extract also your data and extort you with that. So, pay up or I will publicize your information.

Ramfis (06:19): Okay, so then you get into the privacy problem. For instance, your human resources environment got hit and they get all your information from your employees, so social security numbers. Think of it, that's identity fraud already. It could be an identity fraud problem. The next thing was, listen, you have paid up, okay nice, but they still have your data. Then the data becomes a commodity. Now it becomes tricky, because if it's a commodity for those hackers or those groups, what you see is that they're going to do the same thing as a regular business will do. "Hey, listen, I have this real great data lake from multiple customers." Are they customers? Yeah, because you have paid them to get your data back and unlock your system, so now you're a customer. Really, you're a victim, so those criminals have all the data from those victims and they're going to do data sciences on it, try to find intellectual property or data that could be interesting to trade. Now we're going to sell off your data, or data they have collected from multiple victims into data sets and put it on the market.

Ramfis (07:40): That's also one of the reasons why it’s becoming a business, the simple fact that it's so easy to gain CPU power, memory, internet access, bandwidth, it makes it easy and it also creates new opportunities. For instance, what you see is that a lot of those previous hackers are now providing services in the sense that software as a service, or better known as crime as a service, can just pay some bucks, $50, and you can do a denial of service. We're talking about a serious Denial of Service. Not just what you were just saying to me, but pulling down a bank. The business model is even scarier, because if you're going for those services, they say, "Listen, give us a percentage of your earnings." So they're not only facilitating you, but they're also making revenue out of that because they're providing you the service, so there's the initial $150 but if you earn something because someone is going to pay up, they get a percentage. About business development, there's some enterprise, some digital transformation. We're still talking about digital transformation, those guys are doing it.

Anthony (09:17): We'll start our own little business. You heard it here first.

Ramfis (09:20): But taking into account that traditional organizations, which are doing the digital transformation, can learn something about those. Although it's criminal, but how they perceive those business models, because they're doing it and they're providing services and also get some earnings from that, so we see almost subscription-based things like Microsoft is doing. If Microsoft does something that is pretty interesting for your earnings, probably more that it's more beneficial for you to take into account that if they provide certain services in a dynamic way they can share in the profit on that, but also your profits are gaining. It's a more flexible dynamic, enterprise computing. Hey, that could be interesting. Criminals are doing it now, because they're smart, they don't have any drawbacks, they don't have to think about: ‘I have personnel, or a traditional business models, or traditional sales.’ No, they're not hampered by that so they can do that. You can see that they're successful at it, because if you look at the rate for instance ransomware, it just went up and still going up. It's going strong, and the sad thing is that you see now on YouTube those guys showing off their Lamborghinis and Ferraris and their big house and mansions.

Ramfis (10:47): Now we're talking just by security problems, which is just a simple thing like ransomware. But there are a lot more. Think about identify theft, or spearfishing, CEO fraud. It's all there, and they're all now becoming business models, or a long-time being business models. They're changing, for instance, one example we had is that, and it's pretty stupid, another government or someone posing as another government, try to get into a previous account I had. It was really legit. Everything was legit about it. You couldn't see, this is a fraud. It was so good. I'm speaking the Dutch language, which can be pretty tricky from a grammar point of view, and it was perfect. So someone paid someone speaking native Dutch to write down all those sentences, with all those nuances, the inclines. All the specifics from a formal text, it was perfect. But it was a fraud.

Anthony (12:09): Yeah, I've noticed that more and more now that actually, I got one yesterday where it was like janine@pseudoemail.com, and it's like, "Hey Anthony, I need you to run a favor." I'm like, it looked like someone was legitimately trying to get in contact with me. If it wasn't for the email address, where I was like, that's funny. Pseudo email, really? I would have reacted to it, potentially, and just said either, "Hey, sorry, I don't know who you are." Then they know who you are, they know you're their kind of thing. You're a human and they can then get that information. Then I think about people like my parents, they have junk email filtering but they are not technical people in any way, shape or form.

Ramfis (13:27): That also makes tricky. Previously you had the mail from a Nigerian prince, he had some hardship but if you help him you get his millions. Nowadays, there are really professional targeters. They have used LinkedIn, scout you, do some social engineering on you, and also see who your relations are. Sometimes they become friends with your relations so that they get a reputation. Reputation is really the most important thing if you look at it from a digital universe, because reputation can get you anywhere. Social engineering works thanks to reputation, because it creates trust. It builds trust. The more reputation you get, the bigger your trust is. That's the scary part, because although you're a tech guy, you can see things, you know things, you know a lot more than the general public, reputation, you're also susceptible reputation because I'm a respectable guy. I have a good reputation, so probably you will trust me. If I say something and I'm slowly moving in-

Anthony (14:44): If you say so, Ramfis.

Ramfis (14:45): -then you give me your piggy bank and I can go to the Bahamas based on all that money in the piggy bank. That's the tricky part of it, it's human interaction. It doesn't have to do anything with computers or data, it's just using data which is publicly available, making it usable for other means than it was intentionally purposed.

Ramfis (15:11): Then we come directly into the statement of why we are looking at StackState. It seems quite a giant leap, but it isn't because what you see is that we have all kinds of systems: security schemes, lock systems, application monitoring, all kinds of stuff. What you see in principle, it's all based on traditional enterprise computing. It's now extending to the cloud, adopting cloud methods, but it's still - in principle - an enterprise thinking. Slowly it's evolving into cloud, but what I get is also the past at history. Huge amounts of data, and it's actual data, not metadata on data. It's enormous. What it also triggers is false positives, you get really a large amount of false positives. Even if you use AI, which will help you optimize patterns and do great searches, but it will still create a large amount of false positives. Now it comes into, okay, how to look at it. How can I handle all those large amounts of data, that determines data lakes but it could be data oceans because it's so large. What you see is that they are missing the interesting computer here.

Ramfis (16:50): Take your ears, feel it, within that piece between your ears there's something great and marvelous, that's our human computer. Our brain. Our brain can do something that AI will not do, and that's the things that are not that obvious. The computer, the brain, is really sensitive to relations, which could be visualized. So if I make a drawing, you can visualize stuff, and if you can do that, you can see, "Hey, that's off. That shouldn't be there." That's one of the reasons why we start looking at StackState because we need the power of visualization, but from a security perspective. The thing about StackState, see it from a graph perspective and because of course we are looking at graphs, is we need to also arrange rich data, make sure that objects we are looking at are complete. The network data, the application data, middleware data, relations with databases. But then, something should occur that you are going to look at it, because huge amounts, I told them ocean of data and all those relations are also oceans with some small islands between them. So how are you going to do that? This morning, I talked to a previous colleague of yours and I was talking in a house metaphor. Let's say for argument last week you had problems with your roof, it was leaking.

Ramfis (18:39): It was fixed, everything was fine, and now you're working one day, you're working past your own bedroom. The master room, and there's water on the floor. If you look at it from the perspective of you have all the data, and all those different systems, you're going to look at is there water on the walls, is there water on the roof? You're going to examine every piece of system, walking through, looking at all the positives, accepting the false positives, and hopefully you find it.

Anthony (19:24): Yeah, and you're going to take into consideration gravity and existential things that aren't there in a data form, that you know are there and can impact.

Ramfis (19:34): You start a lot of work. Okay. I'm going to tell you, I'm not going to do that. I'm going to do it the smart way, I'm going to use my brain. So, I have a small drone with all kinds of sensors which will help me to eliminate the false positives. I'll go outside my house, throw the drone into the air. It will give me a picture, a visual, what our eyes can see. We can see all kinds of different sensors, it will provide me data and it will help me to get rid of all the false positives and things which are not true. The conclusion at the end could be, there's nothing wrong with my roof. Then it should be something else. If I'm going to do it in the first place, so examining everything, look at all the systems, it is a large amount of work. It takes a large amount of time, probably you'll have to get assistance on that. Just to conclude, hey, there's nothing wrong with the roof. But what is it?

Anthony (20:47): Yeah.

Ramfis (20:51): Should I tell you what it is? I found it earlier because my drone says, and I have all those sensors in place which will help me lose those false positives, or grouping the information that's interesting for me to examine. So, the shortness of time it takes me. Okay, the data conclusion. Why is there water before your master bedroom door? Your son of 12 has taken a bucket, went to the bathroom, filled up the bucket, walked past the master bedroom to his own room to play with his newly received boat. So now you're going to say: "Ramfis, you're going off the rails." Am I? Because that's a new pattern we never see before, this is the first time. This is also what we see with the cybersecurity, it could be a completely new threat which has never been seen before, so everything you learned in the previous older experiences will not help you identify that new threat, because it's completely new data. Your son of 12 has never taken a bucket, filled it up in the bathroom, because he had never had that new boat.

Anthony (22:05): Yeah.

Ramfis (22:07): Those are the things are the challenges. Now, the human brain comes into perspective, it will help you to see the things that should be obvious or not obvious, the illogical thing. StackState is my drone, and I am currently working with the consultant, which is assisting us to make it my drone. Because yes, I have seen: there's application monitoring, all kinds of stuff flying all around in the cloud, on the air, AI coming out of my ears. There's so much AI and it also generates false positives, but I do not have that drone. That magical drone.

Anthony (23:03): Yeah, I think this is one of the reasons why I like working with platforms. When I joined ServiceNow back in the day, I looked at it, a lot of people would look at it and think of it as a ticketing platform, which is true. That's why many people buy it, but the way I see it is it's a database with a workflow engine on top. So, back in 2009, when I was interviewing there, I built an HR workflow for onboarding. At that point, everybody was just buying ServiceNow because they could do ticketing and it was easier than Remedy at that time. But I looked at it and I was like, well, I can do any kind of enterprise workflow that I want.

Ramfis (23:37): Mm-hmm (affirmative).

Anthony (23:37): It could be assessing a vulnerability, it could be doing change management, it could be onboarding an employee, as long as I've got the imagination to put it into a workflow. Not a lot of people are born that way, though. They require outcome, and use this product, put it here. I find that StackState is very similar, right? We're a time traveling database, so we're a graph database that can keep taking snapshots of itself, and you can choose almost to a fault what data you're sending to StackState. Right? And what our agent is collecting, and what data we're bringing back. Then you can leverage that platform capability to build your own checks, to interpret signals differently. But ultimately, at the end of the day, you see the relationships and you have the ability to intelligently determine how you want to do that in a way that wasn't possible prior to having the database, right?

Ramfis (24:40): Check. It's a little bit more than that, also, because I get actual factual information if I implement this correctly. All my assets, which are currently running, because that's the other part. You were talking about, things like Remedy in ServiceNow. You look from a configuration management database, it's always lacking.

Anthony (25:02): Yeah.

Ramfis (25:04): The administration is always far, far behind.

Anthony (25:07): It's always spreadsheets. You talk to the network guys, like, "Oh, I've got my spreadsheet from Cisco," then I talk to the cloud guys like, "Oh, I just pulled everything from AWS that's in there." Okay.

Ramfis (25:20): But the tricky part now, it's just that we also have things like Docker or container technology, Kubernetes is really popular. I'm moving more and more on that service or applications, or microservices. They're all around the place. They could be on your premises, they could be in a colo, they could be at Amazon, Azure, whatever. They're part of your organization and your responsibility.

Anthony (25:49): Yeah.

Ramfis (25:50): So, having a view on the actual running systems you currently have, those assets, that could be all kinds of assets, I was talking about systems, but it could be applications, whatever. That's one part that's really important. From a security perspective, if something pops up, the first thing I want to know, is it mine? Because responsibility is really important. No, that's not mine, or, yes, it's yours, but was it done within a normal process? Was there a request for that system or application? No, it's not. Oh, so someone has popped it up. Who was it? So now breaking down on why is the system there, because maybe there is a hacker who has penetrated your network and it's just popping up in the new system, because it's Docker or whatever.

Anthony (26:47): Or maybe somebody, because Docker is a community product for the most part, you can download pretty much any container you want, that container could have a bunch of third-party stuff embedded in it that you weren't expecting. All of a sudden, somebody can get in your environment just because you ran that container; it then took everything with it because those containers are sometimes running with privileged access, as well. So, being able to track all of that and see that, I could see as being incredibly important.

Ramfis (27:27): That's the reason why it's...I just put in a coin and you're like: "Oh, listen this, and this, and this.” This is exactly what I mean, because when I started giving this example, a subject matter expert directly sees the problems that can arise. You triggered on that, so thanks for that.

Anthony (26:46): You're welcome.

Ramfis (26:47): But that's one part of the equation. The other part, I was talking about anomalies, hey, this is strange, this is not correct. Triggering my brain. But asset management is really important in that, is this mine? Is this running? Is there a process underneath for creating that? If you can see that directly or pretty quick, it gives you an advantage. It shortens time to finding trouble. The other thing is, of course, okay, what are those relationships? Relationships between applications, relationships between service business, relationships to API gateways using data or services from outside my network, and because identity usage, account usage, creation of assets and databases. Think for instance...software defined is great, but if there's a control issue and software defined can mean that someone is just taking a lot of data, or creating new service...

Anthony (28:50): Yeah, you can siphon everything. Yeah.

Ramfis (28:52): Creating new network, VLAN. Because it's software defined - changing load balancers, adding systems to it. It's within that large buildup of data. When you officialize things, and you know how your architecture is created, it's open and transparent, you see those things. To give an example, yesterday I had to demo about StackState. I took two systems, random, just two systems. So there's no view on that, nothing fancy, two systems and I'm looking at the dependencies. I see those systems, both systems, 100% CPU. It's running for a while now, so I'm now looking at, okay, what was it yesterday and the day before? They're all running red hot, 100% CPU, so something is wrong.

Anthony (30:01): Yeah.

Ramfis (30:04): Those things, AI can help you with or whatever can help you with, but if you have experience, "Hey, something is wrong. What is it?" You can ask directly and that's important. That's the power for visualization and putting in everything in place. The data, I have my own knowledge, I'm used to getting stuff done, I have experience with colleagues who can help me interpret data, or can say, "Hey, listen, if something looks wrong for me, it could well be that it's not a problem." So I can directly show someone who is more a subject matter expert than I am, he says, "Okay, no, not a problem because we're running a special badge." It's also a way for communication, so that's the reason I'm really enthusiastic.

Ramfis (31:02): A lot of people are really fans of ELK stack, Kibana, because it can also visualize data, Splunk. But it's always from a metric sense. So data, actually hard data: okay, this network is not doing this, or the database is growing or the amount of transactions is growing. I'm not interested about that, that's something for our application guy or our network guy. I'm interested in: “Hey, what's popping up? What's wrong with that? Or, hey, should it be there?”

Anthony (31:34): Yeah. The time traveling capability provides the context, right?

Ramfis (31:42): Yes.

Anthony (31:43): You can actually, without any kind of AI, figure out if something is abnormal. You can literally go and say, to your point, with the 100% CPU, you could go back and say, yesterday, what was it? Maybe let's go back seven days, maybe this always happens at this time of day, do you know what I mean? On a Friday, or whatever. You know? Or maybe let's go back two weeks from now to see when exactly it started running at 100% so that we can see everything that happened beforehand, what changed, what caught what. Oh, all of a sudden we've got an AWS configuration, or somebody ripped part of the VPC away so now all of a sudden I've got two EC2 environments doing the work of five, so that's why that's been...

Ramfis (32:32): Yep.

Anthony (32:33): Without that context and that ability to derive, a lot of people are putting faith in AIOps platforms where it's like, "Okay, just send me all your data, then I'll be able to convert all of that data into a ticket or one incident and using AI, I'm going to guess the root cause," and then you're going to be able to figure out and then take action on it. I think they're missing a step, they're missing the contextualization of the data, which we kind of had with the CMDB, then people have kind of given up on this notion of the CMBD and they've just gone into AI, whereas really getting the middle ground, getting a good data set, allows you to run even a bad algorithm on good data can turn out good outcomes. But a good algorithm on crappy data? It just doesn't work.

Ramfis (33:29): You're hitting the nail right where it should be on, the head. That's what I'm now looking into and experimenting with, because data, that's science on its own. AI can help you, it can help you really tremendous. If you are good in optimizing and thinking about it and utilizing the technology, great. Two thumbs up. But don't underestimate where still the power lies for the human brain. What you see is that the belief, especially in AI and data itself, combining those two, you hit it right on the nail by saying if you have crappy data, AI will not help you. That's really, really the trick. The human brain is really important at making sense, but sense on the level that's important. Listen, there will not become really great problems even from a hundred CPU situation. If a service will hamper, then they will probably add the new system. They've always done that.

Anthony (34:50): Yeah.

Ramfis (34:53): So enterprise thoughts and the way how to solve problems, especially within this new cybersecurity edge, will not work. That demands something else and I believe in the human brain, in that sense. The human brain has to get information in such a way, visualized, because we are visual people. People are visualizing everything and it gives us the insights we need. If you have large amounts of text, or even if you look at some products which just show you graphs, and by graphs I mean, lines, pie charts, percentages in that, it will not help you because my brain can consume it in a sense, it can absorb that there are some information but it doesn't see the relationships or the tricky parts within their data. If I'm putting in notes, I see lines and see something is changing from color or whatever, that gives me insights. I'm using actually my brain, I'm thinking 3D and if you're looking at the regular things like I see here, products like Splunk are tremendous, Elastic: great, but their presentation is still 2D.

Anthony (36:14): Yeah.

Ramfis (36:15): I'm a 3D thinking person, because I have a human brain.

Anthony (36:19): Yeah, I know. I think as well with logging data and metric data, there needs to be a genesis. In other words, somebody needs to decide that the data point is important for whatever reason, whether it's monitoring, debugging, whatever. So then they have to put it into their code, right? Whereas with topology data, you know, if it's got an IP address, it's there. It's in your system. Whether we have more data on it, or not, is irrelevant. Or what it does is irrelevant. The fact is, we can then map it and then we can extend it from there. If we do have metric data, okay, great. Let's add the metric data to that component. If we don't, then that gives us at the very least an idea as to where we need to improve. What more data can I pull in on this one component where I'm missing stuff. Whether it's, I'm missing integration, whether you just never collected the data on that particular subset of information. Going back to the app dynamics thing, one of the things I always found silly with those tools is the first thing they'll ask you is, okay, in order to get value out of the tool, you need to install the agents.

Anthony (37:40): It's like a chicken and an egg scenario, because you're like: ‘but what if I don't know where the application is running?’ And then you won't know. Then all of the sudden, you think you're running it on two machines, but in fact somebody updated it like a month ago. It's running on five, and yet you've only got two fifths of the data set. It's a real problem.

Ramfis (37:58): Yeah, it's a problem but it's also the challenge for StackState, for my business case of course, in the future is that agents and how to retrieve information, but also the reliability of API's you are consuming as a platform from others, and the development within that. So, what you see is that load balancers, VMware, name it. There's always an API you can now hook up, too. But the reliability of that VPN, API, sorry, could also help you be successful or fail the goals you are trying to achieve because those API's by themselves could be that resilient.

Anthony (38:43): Yeah.

Ramfis (38:46): Or real time as you would like to have them. So, that's a challenge for the coming future, especially if you look at the development on platforms and specifically controlled platforms. Yes, it's going with a tremendous pace, but the quality is sometimes lacking behind. So changes on that, and looking at it from an organization perspective, are difficult. The difficulty not lies in the simple fact that there are a lot of changes quickly behind each other, but also it's not clear. Is it a fix? Is it a feature? Or is it something else? That's also where I try to think, okay listen, I'm repairing something, I'm fixing a security issue, or I'm providing an additional feature which was lacking because I initially promised it with the release of the product, but it wasn't there in 4.1 so I'm now putting it in 4.2.

Anthony (39:57): Yeah.

Ramfis (55:56): Because you're putting it into an integrated, tightly integrated, but in a huge environment because everything is bigger than it was yesterday and the day before. That's a challenge. I find that a challenge. I'm looking forward to also - in regard to StackState but to all our vendors - how are they going to perceive that and move along? Agent versus agentless, features, security, but also if you promise that you are going to put a feature in your progress version X, shit, it didn't get the deadline and I'm moving it on.

Anthony (40:40): Yeah.

Ramfis (40:42): That can hamper you as a customer, that can provide us with challenges we probably don't want to have, because we like stability, integrity, continuity, safety, and we want to have responsibility only for the stuff we need to be responsible for.

Anthony (41:00): That makes sense, and I think that's a beautiful thing to end the podcast with, because we have actually run out of time. But again, I really appreciate you taking the time, you're obviously very passionate about this subject. Maybe in about six months or so from now, we can check in to see how far you've come with your project?

Ramfis (41:25): Yeah, that'd be great.

Anthony (41:28): Yeah, no, it's been a pleasure talking to you. Anything you want to end with, any last minute notes or anything?

Ramfis (41:32): Yeah, automize where you can, but utilize the human brain when it's needed.

Anthony (41:40): Awesome. Awesome. Thanks again, Ramfis.

Ramfis (41:43): You're welcome.

Annerieke (41:49): Thank you so much for listeing, I hope you enjoyed it. If you’d like more information about StackState, you can visit stackstate.com and you can find a written transcript of this episode on our website. So if you prefer to read through what they’ve said, definitely head over there, and also, make sure to subscribe if you’d like to receive a notification whenever we launch a new episode. So, until next time.

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About StackState

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