WTF is happening at xAI | Sulaiman Ghori

32:47On the experimentation side, how are you guys trying to maximize for the number of experiments or good shots on goal that you can do? There's often a time constraint. We will frequently launch multiple experiments, especially on the model side, at the same time. And in some cases, it's not even because of a time constraint necessarily in terms of I need to try X amount of things in Y time. it's in two weeks this prerequisite will be ready either in the hardware or in the training data or something but in the meantime i need to deploy something today uh what can i do and so you run two three experiments and you find out what you can deploy today um and bring in revenue or customer result whatever it is today and then two weeks you switch over um like that's something we do all the time especially at map road have you seen anything where a timeline should have been much longer on like a project that you were working on um and somehow you guys were able to kind of like bring that in by you know weeks or months all the time every time uh all the time every time we come come away from like an email meeting or something internal where um someone pushes hard to get something done or someone external who doesn't isn't responsible for the thing ask for a requirement ask for something to be done in an what we originally think unreasonable amount of time.

34:09You know, we spend a few minutes like thinking about it, complaining maybe a bit. And then the rest of the time is dedicated to getting it done in that time. Yeah. Frequently, the estimated time to get something done, well, all the time to get something done is based on some set of assumptions. And then once you get this timeline, that's like half or one-tenth of what you would have otherwise done, you look at the assumptions and say, okay, proportionally, how much is this impacting my timeline? And then you knock it out or you change it. And then suddenly you get a 2X improvement in your timeline.

34:44You do that a few times. You can meet whatever requirement you really want. At a certain point, you get to the physical limitations, but you're never there from the start. I know for full self-driving, and same thing with the rockets of SpaceX, the Elon timeline was significantly longer. like an Elon time might be a quarter or half of what it actually eventually takes, but then it also, you know, happens four times faster because of the initial timeline. Is it more or less like at XAI because it's more, I mean, I guess more on the software side now. Um, but even on the data center side, things seem to be happening just way, way, way, way faster.

35:26Uh, and they also seem to be happening on like the same timeline as he's roughly saying, he's like, this is going to happen roughly this number of months in the future and that it actually does. I think he himself has calibrated his timelines. Like differently? Yeah, now that he's deployed a number of extremely, like a wide variety of deployed hardware at scale. So I think his own estimates for things are definitely a lot better. And so that's definitely the case. I think he also updates his timelines faster now too. Like sometimes daily, I think he's talking with us and figures out what the update on the timeline should be based on various parameters.

36:07And sometimes they come from him too, right? Especially on the infrastructure side. If a deal or we can be put up in a batch for the production of a certain chip, well, we can save a month or two maybe. Maybe even more than that. Depends on what the deployment is specifically. And then on the software side, it's the same. He always says, you can always attempt to do something in one month that would otherwise take a year. And you'll probably get it done maybe in two. Still a lot faster. I remember in the early days of SpaceX, there was this internal, I think Elon would say internally, every day that we delay is like$10 million in lost revenue.

36:49And I have no idea what it would be like for XAI. Things are moving so fast. Is there kind of an internal thing in your head of every day that we don't push harder, make something happen, we're losing out on X amount of value that could be created? Yeah. For MacroHeart specifically, we do have a few pretty specific revenue targets. I can't dilute the numbers specifically, but in my head, whenever something gets delayed or accelerated, I can pretty quickly calculate how much money we just made or lost. Is it just wild swings? Yeah, I mean, the numbers are huge just because the expected return is so huge and the timeline is so fast.

37:34So a few days is actually proportionately fairly large compared to how much you would otherwise expect the revenue to be. Elon's like famous for making really, really big bets pretty quickly. Like what's the biggest decision that's been made in a single meeting where like huge, huge amounts of capital or time or commitment were done? I think one of them was certainly the decision to go with a model that would be at least 1.5 times faster than a human for macrohard. It's looking significantly faster than that. 8x maybe, maybe more. The, like, for other human emulator type attempts in the other labs, the approach has been let's do more reasoning and build a bigger model.

38:21we've like that decision put us in totally the opposite track of what everyone else is doing and everything that we're doing really is downstream of that like well not everything but pretty much everything um it impacted and it was very early on uh that this was decided it was sort of expected also um that this is the move especially given the analog to full self-driving um no one's going to wait around uh 10 minutes for the computer to do something that I could have done in five, but if it can be done in 10 seconds, well, I'd be happy to pay whatever amount of money for that. Um, it's just obvious really.

39:00So normally like us engineers would, you know, if it's, would push back and say, Oh, you know, here's the 20 different reasons, uh, that it needs to be this way. Um, but if the decision is made and you work backwards, life finds a way. I remember Elon saying, uh, I think it was at the like Y Combinator, he was doing a like q a with gary tan and gary talked about like ai researchers and he was like no they're just all ai engineers now yeah we did this was someone said that in one of the meetings um we did with him uh talking about recruiting like he was here's the job description or something like that and like for 10 minutes he just goes on the engineers just engineers doesn't matter good engineers engineers just someone who's fundamentally a problem solver it doesn't matter if they did like this you know x y thing and this infrastructure or this you know particular architecture or whatever engineers why is it so important why is that definition so important um it keeps things broad it means that people can come in to us from like a extremely wide variety of places and this has been the case i mean there is i think less so in the ai world but i think there's a lot of spacex stories where people came in from strange walks of life that would not have otherwise seemed to be the case and then ended up doing huge things at spacex in the engineering world as a result so keeping it broad means that those people can have a path to us and and help us accelerate for you personally what's the most like fun thing about working there day today no one tells me no and tells you no no one tells me no um yeah if i like have a good idea i can usually go and implement it that same day and show it off and we'll see if uh if it makes sense we'll we'll we'll run whatever eval or um show it to a customer or show it to elon or whoever and we'll get an answer usually that same day uh as to whether or not that was the right move there's no deliberation there's no waiting for any bureaucracy uh i like that a lot i was expecting to sacrifice some amount of this coming from extremely small startups to a larger company.

41:16Like, I guess joining at 100 people, I mean, to me, it was like a 10x leap of anywhere else I've been. But I guess relatively to Elon companies, it's pretty small. And it does feel very small. There's not a lot of overhead in anything. Did you have any other big assumptions going in that proved completely wrong? I thought there would be more top down. And there's some, but not really that much. especially because of how many there's basically only three layers of management there's um like ICs there's the co-founders and some of the new managers and then Elon and that's it and so because there's so many reports to the managers now um nothing really comes from them top down like we'll usually come up with a solution they'll okay Elon okays we're good if there's feedback than we update, but it's a lot more bottom-up than I expected.

42:09Is it like trying to be designed so that everyone is building things and there is fewer manager-managers and more just builders? Yeah, when I joined, I think every manager also wrote code. And I think largely today they still do. Not as much now that some of them have 100-plus people reporting to them, but everyone's an engineer. I remember actually on my first week, I sat down for dinner and this guy sits next to me and I say, you know, like what team are you on? How are you going? How are you doing? I just joined. And he tells me, oh, I'm on sales and like enterprise deals. And I was like, oh, I don't want to talk to this guy.

42:48He's a sales guy. And then he starts telling me about this model he's training. He's an engineer too. The sales team is an engineer. They're all engineers. Everyone is an engineer. I think at the time, there was probably less than like eight people who were not engineers at the company. in some capacity. And even then, like, yeah, it was really cool. Everyone contributes to the machine. Is it a little bit more like you have a single person working on some project and they just, you know, if you're an engineer and you're working on the thing, you can have a much closer relationship to the customer and like understanding their problem and then rapidly like implementing solutions and stuff.

43:25Yeah, yeah. The less layers you have, the less information is lost. There's less compression, basically. Because you have to communicate less times and language is lossy compared to what's going on in your brain. So if you have to go from customer's brain to words to, you know, salesperson's brain to words to manager, every layer you're losing. It's like telephone. A huge amount of information, yeah. And so if you can cut as many layers as possible, then you've only got one compression step of the customer telling you what to do or what they want. or what their experience is or whatever, and US Danger can solve it directly.

44:06Is there anything specific that you've never heard of or seen at any other company that XAI does that allows things to just happen way faster? The fuzziness definitely between teams and what everyone is responsible for is definitely not what I expected and I don't think exists nearly as much in any large company or even remotely similarly sized company. like if I need to fix something on our VM infrastructure I will do it I will show it to the guy who owns that and they will be like okay and it's merged immediately and deployed like there's not a lot of strict regiment like everyone is allowed to update everything and there's some checks for dangerous things but largely you're trusted to do the right thing and do it right which is really cool I remember when Elon was still like really working on Doge, there was at one point, I think they deleted like Ebola prevention or something.

45:09And then they rapidly like re-put that back in. What things have been like deleted because of this rapid process of trying to figure out, you know, what doesn't need to be done and then like re-implemented. There's very rarely anything like irreversibly destructive. I'm actually not really aware of anything where something was irreversibly destroyed. but like I said, yeah, frequently something will be deleted or removed or something like that and someone will be like, hey, I needed that, you know, an hour or two and then you go and roll back. Or, you know, sometimes it can be months where, you know, someone's building this project and they're depending on, I don't know, some piece of infrastructure, something like that and it turns out we rebuilt that thing three times by the time you go and deploy and need it and so you update and go that way.

45:57Do you think it's helpful to have so few people working there on the engineering team? Yeah, definitely. The more people you have doing it, I definitely say a job for one person done by two will take twice as long. And it applies for every skill, I think. And especially now that you don't need to physically write as much code as you did previously. You can be more of the decision maker and the architect. Everyone can be an architect. You just don't need as many hands. So one brain can do a lot more. You tried starting multiple companies, and you were doing a whole bunch of different projects prior to this.

46:38What about working here? And what about the mission, the culture resonated? Why did you decide to work on this? I've definitely always been very Elon-filled. He's been a big personal hero of mine, especially growing up. I've seen the Falcon landings, the first ones. I went out to launch five of Starship, which was so worth it. It was the first one they caught. It was really cool. It was definitely the coolest thing I've ever seen. So being part of anything remotely related to that sounds awesome to me. Is there a reason why you chose this company instead of SpaceX or Tesla? Yeah, so I'm definitely an entrepreneur by heart.

47:18And XAI is definitely the smallest company, the newest of all of them. I think my assumption is, and this is largely proven true, I think, is where you can have the most leverage and change as an individual person because proportionately you're a much larger percentage of the company than you would be at these other companies. Not to say that they're not doing cool things or everyone's not as important, but just the proportional change of life. The leverage to decision is way higher. Yeah. Yeah. not even the decision but to implementation to seeing the results like it's very quick and I guess another assumption that I thought would be the case but it's wrong that I had is that I would be faster on my own you know to build XYZ thing or try XYZ experiment I'm actually usually faster at XAI just because I have a groundwork and a team who's probably already done a lot of the steps that I would otherwise have to do by hand.

48:22And there's no one saying no. You mentioned like it's kind of a fuzzy blurred line between people working on different people working on different things. Has there been any ability for you to kind of go to other people in the organization and just ask for help? All the time. What does that look like? I walk up to their desk and I say, hey, here's my question. What are you working on right now? Can I support any of that? And can you help me with this? That's it. Everyone's in the same building. Actually, funny enough, we started testing some of our human emulators internally within the companies as employees.

49:04And in some cases, like we didn't really tell anyone about this. And so in some cases, there'll be someone like someone doing some work. and someone is like, hey, can you help me with this thing? Or like, can you do this thing? And the virtual employee is like, yeah, sure. Come to this desk. Come to my desk. And they go there and there's nothing there. It's like the quad situation where it's like we're going to show up. And I think when they first rolled out their vending machine, it was like, I'm going to see you tomorrow. And then it's obviously like a piece of code. Yeah, exactly. And so multiple times I've gotten a ping saying like, hey, this guy on the org chart reports to you.

49:39Is he like not in today or something? it's just like an emulation and it's uh it's an ai it's a virtual employee um but yeah generally we all expect to be like in the same building and reachable to each other um so uh it goes like always and uh i can ask for help i people ask for me for help all the time what have been the biggest like blunders that have happened hmm so um with the human emulators with the customers that we're working with um when we try to understand like we always try to understand what the job that they're doing is and all the facets of it um frequently we'll you know talk to them we'll interview we'll even watch them well actually we'll do the watching the last step so we'll talk to them we'll interview they'll give you go see the write-up or we'll just meet up with them and write notes as to how they do their job and then um like a week later we'll look at the uh mistakes that the virtual employee is making and realize like well it's always making mistakes in these places and in these specific cases what's going on and we go watch the human doing the same thing and there's like 20 different steps that are missing that they just totally left out and we go to them they're like oh yeah we do that like i forgot to tell you my bad it happens all the time um a lot of things people like i guess assume automatically it's all for granted in their head totally an autopilot the same way that you um can like be driving for an hour and not remember a single second of it and not be paying attention can be totally in your own world um this is the same for every thing that a human does uh repeatedly and that's what we're trying to solve basically is all the uh dumb stuff that humans do repetitively right now that they don't need to um try to solve for that case exactly how do you decide like which which thing to go after?

51:26What's like the, in your head when you're thinking about that, what are the biggest things outside of driving that humans do all the time that they just don't need to do? Anything repetitive on a computer. So like customer support is a big one where it's just taking in free form input from arbitrary customer in arbitrary form factor and translating that into a standard workflow that is purpose-built for like uh an AI to take care of that so that human could go and do something more creative and uh use their brain like in a more effective way um totally the same like it's a total parallel to what happened uh in the coding world like okay I don't need to write the same uh you know implementation 20 different times anymore I can describe it in like three words and it's done.

52:19It's a huge compression step. And this is the same thing basically, but for arbitrary digital workflows. On the human emulator side, you run into this problem of humans not existing. And then like someone says, come to my desk and the person doesn't exist. Is there any other thing that's been kind of surprising on rolling that out internally? Surprisingly, we've been able to generalize to more cases than we thought. We test and are pleasantly surprised a lot of times. Like just today, we gave Elon a few cases where we did not train on this task at all, but it did it flawlessly, like perfectly, like way better than we would have expected.

52:57Yeah, the generalization is better than we expected for sure. And we're still at a very early stage, so it's only going to get better. And it's, again, the same parallels to full self-driving where there's stuff not in the training data that the car does react to perfectly due to generalization of an otherwise very, very small model. It's a matter of basically weight efficiency. For the Elon meetings that you've been in, what does that actually look like? They're pretty simple, honestly. I've been lucky that most of the ones I've been in have gone mostly pretty smoothly. What does smooth look like?

53:42Smooth is limited feedback or thumbs up. That means like, okay, you're going in the red direction. Keep going. I'll hear updates next week or whatever it is. If there's feedback or total reversal of direction as a request, then we messed up somewhere. And the question is where. So that's usually we don't even have time to identify that. But that's something you just build up implicitly as a muscle as you go on. And sometimes assumptions also change based on new information. That always happens in every case. So when it comes to the top down, it's a little chaotic. I know with SpaceX, the cost for parts and building things is super, super important because everything basically costs a fuck ton of money and time to do right.

54:33For this sort of thing, I imagine It's a little bit less focused on, you know, he's not like necessarily drilling down on, do you understand every part of every process? What does it look like when he's kind of giving feedback?

54:51Usually it's either at a very high level or at a very low level. It's not really often in between. So either on the high level, it's like a product direction or a customer sense. focus on this segment exclusively or don't do this thing at all or whatever. And then at a low level, especially when it comes to compute efficiency or latency, you'll always have a specific suggestion or let's try this. And he's open to being like proven wrong, but it has to be proof. It has to be like, let's try it and see what the results are. It can't be just someone's opinion. there has to be an experiment done, which has led to some surprising results sometimes, and we go with it.

55:36What have been those? So the compute efficiency of going with the small model has led to, well, a lot of improvements that we wouldn't have otherwise thought. Some of them are secondary, some of them are primary. The obvious ones are, well, obvious, being able to go much, much faster than human, but also as a result, and Tesla found this too, with full self-driving, going with the smaller model, they're able to iterate much, much faster. So not only does the model react to situations faster and can be more, I guess, tolerant of timeframes, you can also just deploy iterations much faster. If it was four weeks before, maybe it's one week now.

56:19So that actually goes back to the experimentations, why we can have 20 different ones going in parallels was a result of that particular decision early on in the chain. And was the initial idea like go just do big, large models and then it changed? Sort of. We definitely wanted to go faster than everyone else. But the question of how much faster was, well, the answer to that was amplified, basically, multiplied by a lot. There's this like a lot of bias and stuff in Wikipedia. And Elon has been like focused on kind of creating an alternate version that's just kind of like, you know, more truthful in effect.

56:55How do you go about basically cleaning up the internet in that way to figure out what is truth? It's a really hard problem. It's very hard, especially because the internet is not usually the ground truth for whatever thing it is. So wherever we can, we try to drill down to the fundamentals, which is very hard. like I don't know what is the fundamentals like in physics of the constitution that's not really a question I think I can answer or anyone could really faithfully answer very well but you try to do something like that um drill down as close as you can and then build up from that which is hard too because there's not actually a big body of like writing that does that um one of the few probably examples is like James Burke um with his connection series is where he'll take two totally seeming unrelated concepts and then connect them um through physics and inventions um it's really cool and we're trying to do the same but uh it's fairly novel how do you find better data data is not the only thing that goes into the results yeah like how you actually train on that data.

58:11And I know it's a pretty broad term, but it is true. Like how you actually evaluate against that data and train against it and your different methods for updating the weights do matter a lot. You can try to faithfully recreate the input or the output given any arbitrary input. And well, you can create basically a horrible copy-paste mechanism if you want, which is a classic problem in ML. There's a bit of an art to it to avoid that problem. But I guess we're a few steps removed from that at this point. We're not measuring the fitness to any particular data set. At this point, we're trying to measure to an arbitrary output.

58:56So it matters a lot how you construct your evals, which is really hard for truthfulness because then you need to know the truth, which isn't always... Well, I mean, that's really the problem we're trying to solve, right? So it's kind of chicken egg. Yeah, there's, like, a lot of different approaches and a bunch of smart people working on it. Yeah, if anyone has suggestions, please send them through. There's, like, a lot of different ways to look at it. There's been, like, moments in time where I've seen Elon on X and someone has said, like, this is obviously not right, and it's, like, some Grok output, and he's like, we're going to fix this.

59:31And then, you know, 12 hours later, 24 hours later, he's like, all right, it's fixed. When that happens, like what happens internally? He shows us what went wrong. And then quickly, whoever is awake at the time will start up a thread to go and solve it. Usually individually and pull in a few others if need be. And then give a post-mortem on what happened. And everyone will understand then what went wrong and how to avoid it in the future, ideally. yeah the like generally making mistakes once is okay but making the same mistake twice is big problem throughout spacex's history there's been a number of and same thing with tesla there's been a bunch of these like surges where randomly elon will like come in at midnight and say you know like everyone that can't come in like send out a company-wide email and say like come in we need to be working that sort of thing um has there any been anything like that it's more for the big models that happens more than anything.

1:00:30For Macroheart specifically, we've been operating in a war room for four months. So we've kind of always been on that push. Do you guys have a sign on the door that says war room? Yeah. Amazing. Actually, well, yeah, we grew the original war room. And so we moved everything out. And I'm told Elon walks into the war room and it's totally empty. And he's like, where is everyone? What? And he walks over to where we are now, which is just the gym, which we cleared out and put everyone in now. And then conducts his impromptu questions of what's going on. That was a long night. What is it like on one of those nights where a lot of things kind of get shaken up and moved forward or like there's one of these surges?

1:01:15What does that feel like? I think actually I saw this from one of the co-founders of XA posted this recently. Igor, who was great to work with. I liked him a lot. It was actually really cool to work with him, side tangent, because his work on StarCraft AI way back, like, I guess 10 years ago now almost, it was one of the first, like, cool ML work that I tried to replicate myself in high school, which was very hard. It was really cool. So it was really cool to work with him. Like, I totally never thought I would get the chance to. But anyway, I saw him post this thing a few days ago where he's like, okay, there are some months where only a few days go by.

1:02:01And then there's some nights where months happen. And that was like one of them for sure. Months might be an exaggeration. I think we would have gotten to the technical result. We would have in a few weeks anyway. But doing it in one night was a huge push. And it was a long night. has there been any moments where the company just didn't leave the office for like five days or like a week yeah the surges for the models usually results in a lot of people staying in overnight um and you mentioned there's like five or six pods that people can sleep in and they like toggle out yeah there's some there's some sleeping pods and we have some bunk beds now too um which are less less nice but they exist um and then when the tent picture came out everyone kept sending that to me and I was like honestly we have tents but I've never seen that many out at once um so yeah I know you worked on a bunch of different projects as a kid and I think I don't know if this was the first one but it was like fidget spinners and and making fidget spinners um I don't think it was in your garage but maybe it was like in your room yeah what kind of stuff like that tinkering mindset how much of that have you kind of taken to this uh quite a bit quite a bit yeah um so I learned programming when I was quite young.

1:03:15Um, my dad got me a book when I was like 11 and I liked it a lot. Um, well, I liked it a bit, but I really started to like it when I realized you could make money from it. And so, um, I met some people online who were basically writing scripts for games as hacks and would sell them online, um, for small amounts of money. But you know, making a couple of hundred bucks online was huge for me. Um, the first time that you like have someone give you money, it's the strangest feeling. It was huge. It was crazy. Yeah. I remember having and I asked my dad for like a PayPal, like custody account or whatever, and getting the money in, and it was like the coolest thing of all time ever for me.

1:03:51Yeah, it was really big. And so I did that for like a couple months and saved up enough money to, at the time I was really interested in added manufacturing, like 3D printers. RepRap was the big thing then, so that was kind of where what kicked off the modern 3D printing revolution. You built your own, right? Yeah, you had to. That was the only option. RepRap is literally just a bunch of university students, basically, who said, like, let's see if we can build a machine that can build almost all the components for itself, which was why it was called RepRap. And they basically built in a variety of universities these rooms where you start with one printer, and then it prints the parts for the next printer, and you go all the way up and you scale up.

1:04:40There's lots of problems with this, as it turns out. And that's what they were solving. And it eventually kicked off the modern 30-per-new resolution. But I was very obsessed with it. And so I took one of their parts lists and bought everything from Alibaba. And a month later, things came in. And I assembled it all one night, which went poorly, actually, when I was unbundling the copper cable for the power supply, which was a very sketchy power supply and did catch fire in the end. All the copper windings came loose and frayed everywhere. And one went two inches into my thumb. um just can't your phone doesn't work or did you go to the hospital or something no so it was a school night and it was like 3 a.m because I wasn't very good at building things at 13 um and I spent like an hour in the bathroom trying to pull it out with tweezers it just wasn't it was like it was bad so I just cut it off and I was like it and so bit by bit over the next few weeks it came out and I would snip it off in the mornings um it was fun um yeah uh but I got the printer assembled.

1:05:40And around that time, yeah, the fidget spinner craze was going off. So I bought a thousand skateboard bearings from China and basically set up a little factory in my bedroom where every two hours at night I would wake up and I would clear the print bed, start a new print of fidget spinners and I would sell them online. And then before school, I had a little assembly line in my garage where I would put in the bearings, spray paint, dry, and then run around to all the other bus stops of the other schools, sell them to my distributors, which were just other kids of other schools, sell all day at school, come back, collect from my distributors, and then sell online, ship, built a little healthy business.

1:06:15And after two months, I ended up getting shut down by the county. Their official quoted reason is that the companies that sell the school food have technically an exclusive license to sell anything in school property. But I think they just didn't like that I was distracting everyone and making money doing it. But it taught me a good, like, healthy disrespect for authority, I think. That has kind of been like a constant theme. What is that actually, how is that materialized in your life with, like, the healthy disrespect for authority? And you even mentioned institutions. Like, you don't necessarily trust institutions.

1:06:52How did you kind of come to that, and what does that look like?

1:06:57I've always known from very young. like I want an unconventional outcome. And so going through a conventional path would pretty much necessarily not lead you to an unconventional outcome. So I grew opposed to any form of convention and institutions necessarily enforce convention. I think creativity and interesting outcomes come mostly from free-spirited individuals in almost every case, if not all of them. I guess it's a bit of a high-minded way of saying it, but yeah, individuals are the most creative you can get. And so staying true to that is the way to go. I do love John Collison's idea of everything is so hard to build and so hard to make, especially put into the real world, that if you look around, it's basically like the world is just filled with people's passion projects.

1:07:53Yeah, it's a total miracle. and there's a story behind every little thing way more than you would think i remember reading about the um i think it was ykk zippers apparently every good zipper like there's two or three companies in the world that make zippers which are actually pretty little little miracles they're very cheap but also mechanically like relatively complicated for how much you pay for them and there's only a few companies that are capable of building or that have set up to build them um And it's, yeah, basically it's one Japanese guy's passion project over 40 years to figure out how to do this properly.

1:08:27And this is the case for pretty much everything. Anything very specific and at scale is probably only done by a few companies or a few people in the world. So, yeah. I mean, you hear about it every so often, right? Like some company in Germany, arbitrary company in Germany shuts down and Volkswagen has to halt all their lines or something like that. Happens all the same. It was a big thing in COVID. Right before we met, you had made a liquid fuel, I think, rocket engine. It was like a very small thing I saw downstairs. But you said we were talking before this that you did it in like 24 hours just on a whim.

1:09:02How did that happen? Yeah. So it was a project over like roughly four weeks. And I started by literally just buying a bunch of textbooks and trying to figure out like what are the design principles behind a rocket engine? Like how do I design it? There's not like, it's totally different from learning software where you can just go in GitHub and download people's code and modify it. There's no file for a rocket engine. You have to learn how to, like, what are the material properties? What's the chemical properties? How do you actually machine it? How do you design the parameters and know what to expect from, in terms of thrust output?

1:09:38And how do you not overpressure the engine and all these kinds of things? How do you design the injector, which is, the injector was very hard. That was probably 50 % of the time. Is that the hardest thing? yeah the injector was very hard and it was like the biggest flaw in the end um so yeah i spent like three four weeks doing this and uh expedited a bunch of parts from china like cnc'd and all that stuff um and uh it was right before thanksgiving i was gonna go fly back to the east coast and visit my family and i was like okay either i fire it like build it and fire it tonight it was all just a bunch of parts at that time uh or i do this in two weeks and i'm like i'm not drank a lot of coffee in the morning and spend the whole day like hacking away at Eliminant extrusions and built out the test frame and then the engine itself and Let it off that night Yeah Which had a lot of um We'll say Concessions made to make it happen that night I Do find it absolutely hilarious that you like you said were you like a couple feet away?

1:10:44Yeah, so I designed it like I wasn't stupid it i designed it so that i could remotely fire it but um i didn't the power supply hadn't come in yet to remotely power the computer that was on board so i had to use a usb cable from my laptop to power the onboard computer and i didn't have a long enough usb cable uh the longest one i had was like six foot so i just stand right next to it and light it off and i was like there's like a 30 chance that this thing explodes or or launches fire everywhere and actually um i don't know if it shows in the video I think it does show in the video but my jacket did catch fire because I wasn't that great at designing the injector and it did create a lot of overpressure events which meant there was a lot of basically uh unburnt fuel spewing out which was ethanol and so that's liquid and just landed something that landed on my on my jacket and caught fire um so yeah that's a trophy still the burnt jacket Bye.