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    "text": " This is Jeremy Gosney. I'm coming to you live from Password Village HQ2 in sunny Austin, Texas. Today's high is 104 degrees. So today I'm going to be talking to you about what it's like cracking at Terrahash's scale. I'm not going to make this a vendor pitch in any way. I'm going to focus on the technical challenges that we encounter as we support clients who have hundreds of GPUs in their clusters. When I first started on this, I thought 100 GPUs was a really big deal. Now we tend to view 100 GPUs as like, meh. It's good, but it's kind of middle of the road. There's a lot of distributed solutions for HashCat out there. But as far as I know, Terrahash is the only one that aims to operate at warehouse scale. What I mean by that is, more so with the most recent version, but with every version that passes, we aim to embrace the concepts of data center as a computer. While we currently don't have any clients who have an entire warehouse full of password cracking hardware, that's the point where we're trying to go. We try to enable the software to be at that level, even if our clients are not quite at that level. I'm going to go through the history of how I got started in massive distributed cracking. And I'm going to go through the technical challenges that we faced as we continued to evolve our hashtag distributed solution. I'm going to start the slide deck. All right. So, in the beginning, I kind of got into this because I was messing around with distributed computing. I was doing stuff with MPI, some other MOSIX software, rocks clusters, things along those natures. But all that was all CPU-based. And then when GPU cracking came about, we started looking for solutions that would enable us to distribute GPU workloads. So, I found a piece of software at a Hebrew university called VirtualCL. And I did a presentation on a 25 GPU proof of concept cluster that me and Bitweasel put together at passwords12 in Oslo. And when I woke up the next morning after giving that presentation, we had gone viral. On the front page of Slashdot, Gizmodo, Boing Boing, NBC News, The Register, we were everywhere. And in hindsight, I really should have used a lot more than 25 GPUs. I mean, we had more GPUs, that's just what we chose to dedicate to this proof of concept. And had I known in advance in any way that the general public would react how they did to this proof of concept, I would have used like three times the number of GPUs. So just, you know, some of the headlines that came out the next morning that I woke up to, which was pretty surreal because I didn't really see this as a big deal. So, VirtualCL was cool at the time because it was the first solution that I knew of that enabled us to sort of transparently distribute OpenCL jobs across the arbitrary cluster. So what VirtualCL does is it provides a virtual OpenCL platform. And the target software, in theory, doesn't need to be aware it's communicating with a virtual platform. It just sees any, you know, it sees VirtualCL as any other, you know, OpenCL platform, just like AMD, NVIDIA, Intel, what have you. So the target software just simply links against libOpenCL, which is provided by VCL, which then communicates to a broker daemon, and then that broker daemon distributes to the agent daemons, and the agent daemons, which are installed on the compute nodes, actually communicate with the real OpenCL library, be it AMD, NVIDIA, whatever. So in theory, all this is transparent to the end application. But in practice, it didn't quite work that way. We had to have a special fork of OCLHashCat called VCLHashCat, which had some workarounds for VCL quirks and such. It also required InfiniBand, because it was really latency sensitive. It ended up being a very chatty protocol, and it required essentially real-time communication. Bandwidth wasn't so much of an issue. It pretty much consistently pulled, you know, less than one gig of bandwidth, but it was really sensitive to latency. Even just a couple milliseconds was acceptable, but as you start getting up into double digits of latency, you had a noticeable impact on hash rate. And of course, InfiniBand hardware is expensive. It's way more expensive than Ethernet. VCL was also closed source, and it required frequent updates. So this is back in the time, this is 2012 through 2013, where every GPU driver that was released broke some shit. And then we had to go back in and implement workarounds and fixes, whatever the fuck AMD or NVIDIA broke in their driver that day. And VCL was no exception. So with VCLHashCat, we had to have our own workarounds for VirtualCL, and then the VCL team, or VirtualCL team, had to turn around and implement any workarounds for any new driver releases every time they were released. Which is a pretty substantial job. The problem is, VirtualCL was created by grad school students. This was a grad school project, and they created this their senior year. And when they graduated in spring 2013, that was it. The project died. So when the next version of FGLRX was released, I think that was version 13.9 in September of 2013, we go to install a VirtualCL cluster for our client. This is actually a 64 GPU cluster. We were just starting out, so this was actually a pretty big deal for us. 64 GPUs was pretty fucking cool at the time. But we get the OS installed, we get the hardware built, all that shit, and then we go to install VirtualCL and FGLRX, and VirtualCL just shits the bed. So I panic and email Hebrew University, and I'm like, you guys really need to update VirtualCL for Catalyst and FGLRX 13.9. And they're like, well, we can't. Everyone who was working on this project has gone. And the intellectual property remains with Hebrew University and MOSIX, so they can just have those people work on it outside of that. I offered to take over the project for them, because it was kind of essential to what we were doing, and I didn't want to see the project die. I thought it was really neat. But the professor over there who was in charge of the project was not amenable to that idea. He actually had pretty grave concerns when we first started using VirtualCL for password cracking, because he envisioned us turning the world into a giant botnet. And of course, that wasn't really on our agenda, at least not at that time. But yeah, he really didn't like the idea of his VirtualCL shit ending up in our hands. So we were left high and dry without a software solution, and so we desperately needed something. So we conceived the idea of Hashtag. Initially, we thought about making our own VirtualCL clone, since Hebrew University was not going to give us the VirtualCL source code. And we're like, well, fuck it. We'll start our own VirtualCL with hookers and blackjack. But the more we actually sat down and thought about what that entailed, we determined the level of effort for that was just way too high. And the timeframe in which we needed it was way too short. We literally had hardware in-house that we're building for clients who now have no software solution because of VirtualCL. So we decided to make a distributed wrapper. And that's when we came up with the idea of Hashtag. Like I said, we had to iterate really quickly on this. So we went from a whiteboard session to production in less than two months. And this was basically me and Tom Steele locking ourselves in the office and ordering over $100 for the Taco Bell and just banging this out as fast and furious as we could. It had a traditional client-server-agent architecture. And it was Hashtag focused, but it actually had a generic plugin interface. We had plugins for... and this was before there was just one Hashtag. So we had Hashtag, OCL Hashtag Plus, OCL Hashtag Lite plugins. And then we had John the Ripper plugin. And then we had just a generic interface to where any cracking tool could be made to work with Hashtag as long as it adhered to a standard format that we had defined. And then it also had the ability to run arbitrary commands when idle. This was less for our clients and actually more for me because we were running Hashtag in-house as well for our cracking-as-a-service type stuff. And this is back in a time when GPU mining was still profitable. So when we weren't working on a password cracking job, I wanted this thing to be generating coins in the background. So the arbitrary idle exec command was literally just put in place so that people could mine Litecoin or Namecoin or whatever when their cluster was idle. So, you know, totally different time. But, I mean, it actually worked. It did exactly what it was supposed to do and it was good. And the whole goddamn thing was implemented in this fancy new language called Node.js. Again, this is early 2013, so Node.js was the new hotness and everyone was starting to move that direction with microservices and everything. It's not on this slide, but we implemented Hashtag version 1 entirely as microservices. And I shit you not, there was like 23 individual packages that comprised Hashtag and almost all of those were microservices. And we went that direction because that's kind of like programming zeitgeist at the time, right? Like everyone was pushing microservices and pushing asynchronous and, you know, NoSQL and all that other horseshit, right? And in the end, it created more headaches than not if we just had a monolithic fucking program. You know, maintaining like one server binary and one agent binary is way simpler than like, you know, maintaining 23 individual microservices and making sure that they're all synced and communicating properly and, you know, that the versions are correct, you know, across all 23 packages. And there's just a lot of unnecessary headache that was introduced with that, that, you know, we attempted to manage. But even then, like our clients would still kind of mess it up. You know, like they would upgrade like 10 of 20 packages and then like the other ones would just be stuck at old versions or, you know, one demon would die and another demon would know that it's dead, so then it would shit the bed. And anyway, so yeah, no JS, microservices, no SQL, MongoDB, all that shit. I tried to use all like the latest and greatest everything because at the time, that seemed like the best idea. And also, Hashtag was more than just workload distribution. So a lot of our clients at the time thought that Hashtag was just a GUI front-end for Hashcat. And we're like, no, it's not. It's not just a web UI for Hashcat. It actually does like, you know, multi-user and, you know, workload distribution with like a really complex four-dimensional queuing mechanism. Like you're trying to explain all this to the client and they're just like, you know, eyes glazed over and like, so it's a web UI for Hashcat. Like fucking yes, okay, it's a web UI for Hashcat. But it actually did a hell of a lot more than that. In fact, when you installed Hashtag on your box, it basically hijacked it and configured it exactly the way that we needed it to be configured. It would actually provision the entire server from just installing like the initial meta package. So it's a complete stack of packages. They get stack, Hashtag, that's where it comes from. So it handled driver installation, driver configuration, driver updates. And again, this is back at a time, and even nowadays still kind of a challenge, but this is back at a time when GPU drivers were a pain in the ass to install. And then once you got them installed, it was a pain in the ass to update. So the fact that we kind of handled this automagically was a pretty big value add. Also handled XOR configuration, headless servers. This is actually during a time where when you had a headless server, you had to have fucking dummy plugs connected to each of the GPUs. There's probably very few people watching this who actually remember that. But yeah, if you had a completely headless server, each one of those GPUs had to have a special dummy plug attached to it that would connect the necessary pins to trick it to think that there's actually a monitor attached. And we actually had to run XOR because even though these are headless, they don't have GPU clock rates or set like power tune profiles or any of that. So even though this is a headless server, we still had to have dummy plugs to think there's a monitor attached, still had to run XOR, and still had to configure XOR properly to enable us to control the fans and control the clocks and all the other shit that we needed to do. And then along those lines, Hashtag also did GPU tuning for you and also automatic overclocking. And this was kind of a double-edged sword. So when the R9 290X came out, at first it was like entirely unusable because of the way that AMD aggressively implemented power tune firmware, I think it was 2.0 at the time. But the GPUs would throttle so aggressively that it was like way slower than the 7970. And it was just essentially unusable. So then I wrote od6config. Sorry, did I say PowerTune? PowerTune is fucking NVIDIA. Overdrive. I'm sorry. Oh, no, no. PowerTune is AMD. PowerMizer is NVIDIA. All right, whatever. So od6config enabled us to properly configure the R9 290X. But it was good, but it was still kind of mediocre. But at that time, when I measured it, it was only drawing 300 watts, which is important because our server platforms like the Brutalis can only handle GPUs that drop to 300 watts. You draw more than that, you're going to blow a fuse on the PCI slots on the motherboard. So we had a automatic overclock profile for the R9 290X that we pushed out to our clients that made the GPU draw just about 300 watts. And then a little bit later down the road, about a year, year and a half later, AMD pushed out a new driver update that actually caused the power consumption to go up by a good 50 to 75 watts. And we did not know this. This kind of went undetected by us until we started getting a rash of clients who were seeing dead PCIe slots on their motherboards, and all of them had R9 290X-based solutions. And I finally busted out a kilowatt and figured out what the fuck was going on. But we actually had the ability, through all these mechanisms that we had in Hashtag at the time to enable the automatic overclocking and configuration and all that, to push out a new underclock profile to all of our clients to try to drop the power consumption down to keep it from blowing PCIe slots on the motherboards. So it's both good and bad. The fact that we're able to automatically overclock GPUs to help get the most speed out of the GPUs is awesome. But then in that one specific scenario, it was bad because we started blowing PCIe slot fuses. But then it was good that we had this functionality because then we could correct it through software, over the air, and automatically correct it on all of our client systems all at the same time. Which saved us a lot of money because we were spending hundreds of thousands of dollars on new motherboards. So yeah. And then, of course, Hashtag also had cluster resource monitoring. So there were some drawbacks. Like I said, the entire Godend thing was written in Node.js, and while Node.js is asynchronous, it also runs on a single CPU thread. That may have changed at some point, because we're talking like 2013, that's seven years ago, and I haven't even touched Node.js since. But at the time, everything ran on a single CPU thread, which was stupid, because our cluster controllers had a minimum of 12 CPU cores, 24 threads, and we're sitting here stuck in one thread on the server daemon. So yeah, any long-running methods would just totally block the entire application, and we had massive scalability issues. I think the very first alpha that we released to clients could only support four nodes. The second one, we were able to get up to nine or ten nodes. Basically, any time a client ordered a cluster larger than the previous largest cluster we had ever built, we ran into some kind of scalability issue, identified some bottlenecks, and had to work to remove those bottlenecks. Unfortunately, 90% of the time, the bottleneck was the fact that JavaScript was too slow for the task that we were trying to do. So we had to reimplement those bottleneck methods in a faster language, and then just shell out to them. So keyspace calculation was reimplemented in C. Now, calculating keyspace for a mask attack is really lightweight, but when you talk about massive dictionaries or rule files, trying to count the number of lines in a file with JavaScript is painfully slow. So that's largely why that entire portion was reimplemented in C, and we just shelled out to that binary to eliminate that bottleneck. We didn't dramatically reduce it. Regex. Anything Regex, we actually implemented, we had reimplemented it in Perl, because Perl is amazing at Regex. JavaScript is not very good at Regex. The primary thing that we were doing with Regex here was validating the hash lists. So a client uploads a hash list, and some of our clients wouldn't even bother throwing 200 million hashes at hashtag in one job. But then hashtag has to sit there and receive the massively large hash file, and then validate each individual hash, and then stuff them in a database. That was a massive bottleneck. So we ended up reimplementing all that in Perl to try to get that down. And then the agent, the entire fucking agent, we ended up just implementing the whole thing in Perl. And the only reason we picked Perl at the time is because that's the only language we could iterate fast enough in to rewrite the entire damn thing. But yeah. Like I said, we also used MongoDB because NoSQL was the rage at the time. And out of all the NoSQL databases we tested, Mongo was the most performant. But then we had clients do things like throw 200 million hashes at hashtag. And that would cause some not only bottlenecks and blocking processes, but some really UI weirdness as well. They would go through the process of creating a job, uploading the hash list, and they would just sit there with the little spinner for a while until it timed out. So they'd go to create the job again because it timed out. And then they'd go back 10 minutes later and it was actually active and running. But because they went through the process four times trying to get it to start, now they have like four of the same process running on the cluster. So we threw Redis in front of Mongo as an in-memory caching layer. And that actually had a significant impact, like positive impact. And it enabled us to ingest hashes a lot faster. But still not a very great solution overall. The Web UI client was implemented as a single-page application in Angular. It was really clunky. It was really heavy. It was really slow. It constantly caused the browser to freeze if you left it up for more than 30 minutes. And it just had a ton of bugs. And I think probably the most annoying thing is if you're like a hashcat power user and you sit down and try to use the hashtag Web UI, it was just cumbersome as fuck. There's no way to move quickly in that UI. And it gets very repetitive and very tedious. So there was a lot of drawbacks to having that Angular Web UI. I absolutely hated it. And anyone who was really experienced with cracking and hashcat and stuff, they also hated it and begged us not to have that again. And we were just severely limited by what we could do in a browser. For non-hash formats at the time, we were scraping everything server-side. So you would actually upload a PDF or upload a .docx or whatever, and we would scrape the file server-side to get the necessary bits, like Office to hashcat and PDF to hashcat, shit like that. The problem then became, what do we do with things like TrueCrypt volumes or really large 7-zip volumes or RAR files? Are you really going to upload a 100-gigabyte disk image through the browser just so we can scrape off two kilobytes? No, that doesn't make any sense. So yeah, the browser was an issue. And then we implemented access controls, but there was nothing fine-grained. We assumed that because everyone operating the hashcat cluster was on the same team, that they all had equal rights and equal access to the cluster, and therefore all users are admins. We didn't have different roles or anything. It was just, if you have an account, then you obviously have a right to be there, so therefore you are an admin. And I think some of our clients didn't have a problem with that, because everyone on this team does have equal rights and access and privilege to the cluster, so this is totally fine. Other clients were like, this is absolutely unacceptable. And I eventually got to the point where the old version one code was completely unmanageable. Even just implementing what should have been a minor bug fix ended up requiring major refactoring. It just wasn't, because we threw it together in less than two months, so we didn't think of everything in advance that would come up, and try to implement new features, and try to work around some of the bugs in there. So we decided we needed an entire ground-up rewipe for Hashtag version two. And at some point, I got the idea to create a commercial fork of Hashcat with native distributed capabilities. And at the time, I was so fucking stoked on this idea. It seemed like the best idea in the world to me at the time. So we set out to do just that. Now this is right before Hashtag went open source, and then we started work on this literally the minute that Hashtag went open source. So we implemented in Golang in C with Postgres database. Now we chose Golang because of high concurrency with goroutines, right? So with Node.js, everything ran on a single CPU thread. And we wanted the opposite of that for version two, right? We're going to learn the lesson on that. So we wanted just lots and lots of goroutines to run everything so that we'd use all 24 threads plus on the box. And Golang actually also allows for pretty rapid development. So we thought it was a pretty good choice. But we still stuck with the traditional client server agent architecture. We have numerous clients and then one server and then however many agents. Meaning that that server is both a bottleneck and a single point of failure. So the first thing we had to do is we had to split Hashcat in two. We took everything that had to do with actually starting a cracking job and executing an OpenCL kernel on a GPU with the agent and nothing else. The agent was just barebones trimmed down Hashcat launch attack on GPU and that was it, nothing more. And everything else we placed into the server library. And then for any code that wasn't already in Hashcat such as the JSON API and the protobufs for agent server communication workload distribution fine-grained multi-role access control. All that was implemented in Go and then we just linked against the C libraries. And this effort actually laid the foundation for the Hashcat 4.0 refactor with libHashcat. And then because of how clunky our old web UI was we actually ditched it entirely for a cross-platform CLI which made our power users like really happy but our less experienced users were not very thrilled with that decision. They still wanted a GUI which, fair enough. So our plan initially was to maintain both Hashcat and Hashstack trees in parallel. And then if there was any features that we added to Hashstack or any new kernels that we wrote we would just backport those as we chose to Hashcat. This didn't really work out very well in practice though. So when we released version 2 it was in sync with Hashcat 3.5 but it didn't stay that way for very long. And of course we had to add a cross-platform GUI to the roadmap as well because we had significant feedback from our clients that they really wanted the goddamn GUI and they were not happy that we took away the web UI. It turns out this was the single dumbest motherfucking thing I've ever done in my life. I completely underestimated how much work was involved in creating a commercial fork of Hashcat. Like, genuinely I... Yeah, I really don't know how to convey how fucking stupid of an idea this was. We spent like 100% of our time just backporting things from Hashcat into Hashtag instead of actually adding new features or writing new kernels. Like, literally months of work you know, just backporting Hashcat commits. And it got really frustrating. You know, it's like, hey, are we working on these tickets? Are we working on these features? No, we're working on backports. You know, I got so sick of hearing working on backports, working on backports, like when are we ever going to be done with backports? And then when Adam refactored Hashcat for 4.0, that also meant that we had to refactor Hashtag as well. And we hadn't even finished implementing all the backports at the speed that Adam was, you know, committing things to Hashcat. So this was just like, absolutely unmaintainable, right? Like, fuck me. And then there was also this other little minor problem where we picked the wrong HTTP library for the API. And this basically limited the service capability to about 12 nodes. Now, 12 nodes might seem like a lot. That's 96 GPUs. You know, that's a pretty respectable size cluster. The problem is this particular client had ordered 320 GPUs, 40 nodes. And you can't really deliver a 40 node client to a cluster with a server that can only really handle 12 nodes. So this required not a complete rewrite, but a damn near complete rewrite time. Fuck, because we use that HTTP library extensively throughout the code. And there was no, like, drop in replacement. So basically anywhere where we were dealing with HTTP, we had to rip out the old library and then, you know, add in all the stuff for the new library. But they did resolve the issue. It was just really painful at the time to have to mess with that. But the server still continued to be a single point of failure and still proved to be a source of bottlenecks. As we, you know, tried to build larger and larger clusters, we kept running into problems with the server being a bottleneck and a single point of failure. And we also had clients coming to us asking us if they could order multiple cluster controllers and cluster them together, you know, either high availability or, like, load balance them or something. And we didn't have a solution for that, but I started thinking about a solution for that. And again, like, 40% of our clients were just completely pissed that we had no GUI. So it got to the point where it's like, let's just rethink, like, the entire fucking thing and just, like, go as far away from these mistakes as we could possibly get. Like, we never, ever want to make any of these mistakes ever again. We've learned a lot of lessons from, you know, what we've done over the last seven years. So, but we need more than just a complete ground-up rewrite. Like, we need an entire paradigm shift. Like, we need to rethink this entire problem entirely and come up with something that's completely different from what's ever been done. So we need to eliminate the single point of failure of the server. We need to enable, like, actual infinite horizontal scaling because, again, we're, you know, we always try to strive for, like, warehouse as a computer, right? We're looking at warehouse scale computing as, like, the target that we're trying to hit. There should never be any, you know, limiting factors outside of budget for, like, how many nodes we can support in HashtagWrite. And then, of course, the other big requirement is we have to actually move at the pace of Hashtag development. We have to, you know, as Adam commits things to Hashtag, we need to have those not instantly available in Hashtag, but within a very reasonable time frame. So the very first idea that we came up with was let's not do a traditional client-server-agent model. And let's not even try to cluster the cluster controllers. Let's just eliminate the cluster controllers entirely and make everything peer-to-peer where all nodes are equal and there are no dedicated servers. And the biggest requirement to this is that work can be submitted to any node. So let's say you have, like, you know, 20 users and you have, you know, 10 boxes. Like, there shouldn't be just one node that all of them have to hit. They should be able to hit any node in the cluster and, you know, have the same view from any node. And it also needs to be more user-friendly than ever. And also needs to run on Windows. I'll tell you why in a minute. So set out to, like, figure out how we're going to implement this. Because we have the high-level requirements, right? Like, we know it needs to be peer-to-peer. We know all the nodes need to be equal. You know, all that shit. But how are we actually going to do this? So pretty obviously we're going to need some sort of distributed state machine. Right? And that's just straightforward and self-explanatory. But, you know, like, we just throw a message queue at it and some PubSub and, you know, Bob's your uncle. But what about disagreements? Or, you know, maybe even more than disagreements, what about, like, an actual, you know, rogue cluster member? Or, you know, a broken cluster node that's maybe not necessarily intentionally malicious, but it certainly, you know, has, you know, the appearance of malicious behavior. Right? So I started coming up with solutions for this. And I somehow got into my head, like, dude, what if computers could vote? Right? Like, you just make it to where, like, you know, if there's a dispute, then one of the nodes, like, requests a vote, right? And so they take a vote and majority wins. And if there's a tie, like we have an even number of nodes in the cluster, then, like, literally fucking flip a coin, you know, randomly, or play rock, paper, scissors. Like, why not? And if a peer doesn't accept the results, like, then the rest of the cluster just shuns that peer. And I started thinking about, like, is this actually, like, a thing? Like, can we actually do this? Am I, like, completely fucking stoned? It turns out that I really didn't have to put that much more thought into it because this already exists. Like, what I thought up is actually a thing called the wrapped consensus protocol. Now, it's cool that it already exists because that, you know, means we don't have to do a whole lot to implement it. But it's also cool because it kind of validated, you know, my ideas and theories on how to make this work. And told me that I wasn't just completely fucking insane. And we found several implementations of the wrapped consensus protocol. And the one that we ended up liking the most and settled on is Akka, which runs on the JVM. It's written in Scala. Now, I hate Java. I have shunned Java for damn near my entire life. You know, I completely condemn Oracle JVM. But, there is OpenJDK. We don't have to use Oracle. And we don't have to write in Java. We can use Scala and Kotlin. And since I've started to work on this, I actually really like Kotlin. I don't really have too many negative things to say about it. It's actually kind of fun to work with. And then Akka also has some plugins or modules that, you know, help us even further. Such as Akka persistence. What Akka persistence does, it takes our distributed state machine, which is normally resident in memory, and persists it to disk. And then Akka distributed data, of course, would be like what implements the distributed state machine. And then on top of all of that, we built a custom multicast-based protocol to enable cluster nodes to automatically discover each other on the network and then automatically join the cluster. Now, we made this opt-in because obviously that's kind of a security risk if anybody can just join the cluster at will. But for some scenarios like where all of your nodes are on a dedicated subnet that's properly firewalled off and you have proper layer 3 and layer 4 access controls, this would be a solution that would be really handy. So, again, thinking about warehouse-scale computing or data center as a computer, let's say you just have racks and racks and racks of compute nodes, you would just unbox one, literally throw it in the rack and power it on, and it would discover the cluster as soon as it's powered on and join the cluster and start doing work. That's kind of the vision that we have for this. And, of course, since it's all peer-to-peer, that enables it to actually be just literally that simple. But, of course, the default is you manually specify which peers you want to be part of the swarm. Okay. And then, in April this year, we had the unique opportunity to present itself to purchase loft crack. Dildog approached me and we entered negotiations and we purchased it earlier this year primarily for the GUI, which is written in C++ using QT as the windowing framework. And while it's not currently cross-platform, that's a really easy platform, or that's a really easy language and library to make cross-platform. So, instead of hashtag version 3, we now have loft crack version 8. And while loft crack 7 uses John the Ripper as a backend, we've ripped all that out and we are in the process of integrating what was hashtag version 3 as the new backend for loft crack 8. And the final thing we're doing with this is we are dropping the TeraHash hardware requirement. Traditionally, you know, we do not let people purchase hashtag as a standalone product. We say, like, you know, the only way to get hashtag is to buy a TeraHash appliance and it comes preinstalled and that's the only way to get it. But loft crack has always been sold standalone. So we are changing the model now to where, you know, keeping with loft crack being sold standalone, we're going to be selling loft crack 8 as a standalone product without the TeraHash hardware requirement. So, yeah, that's where we are now with development. That's how the product has evolved over time. And that's some of the challenges that we have faced trying to obtain our goal of true warehouse scale computing. Thank you and hope you've enjoyed this."
}