Read the full interview that got engineer Sulaiman Khan Ghori ‘fired’ from Elon Musk’s xAI in less than a year

Sulaiman Khan Ghori, former engineer at Elon Musk’s AI company xAI recently shared that he has left the firm. “I have left xAI. Nothing but love to my former team and coworkers!,” Ghouri wrote in a post on X (formerly Twitter). The exit was announced days after Ghori appeared on the Relentless podcast hosted by Ti Morse, leading to speculation that the engineer was ‘fired’ for being “too candid” and “unfiltered”. “Fireside chat turned into actual firing…,” commented one user on the YouTube video of podcast interview. “I guess he was a little too candid,” said another. “Former xAI employee Sulaiman Ghori supposedly leaks Macrohard’s roadmap in interview and gets fired! This is the part where he reveals details of the ‘human emulator’ they’re building that does ‘everything’…”we get a full human emulator we can put to work”,” wrote a user on X, adding to the speculation. According to his linkedIn profile, Ghori joined xAI in March 2025. His alleged ‘firing’ comes less than a year after he joined the company.

Interview that got Sulaiman Khan Ghori ‘fired’

Tyler: Today I have the pleasure of sitting down with Sully Ghori, and he is one of the engineers at xAI. I’ve been kind of fascinated by xAI since like 2023 when Elon first started. I think it’s like one of the fastest growing companies of all time. Can you just talk about like what the [ __ ] is happening at xAI?Sully: Yeah. Um, we don’t have really due dates. It’s always yesterday. Um, there’s no blockers for anything—at least nothing artificial. Uh, the whole Elon thing about going down to the root, uh, the fundamental, whatever the physical thing is, we get there pretty quick if we can, as quick as we can, which is funny in software. It’s not really like a thing that you think about is the physics too much, but we do try quite a bit and we’re not really fully a software company given all the infrastructure pull down. Um, it’s kind of hardware at this point. Yeah, it’s like hardware constrained. It’s probably our biggest edge is the hardware because nobody else is even close on the deployment there. Um, although the talent density on software is like incredible. I’ve never been anywhere like that. It’s really cool.Tyler: For Elon, he is very good at figuring out like what the bottlenecks will be even like a couple months or even years in the future and then trying to work backwards from that and make sure that like he’s in a really good position. Um, how does that work day-to-day with just normal people like at xAI and like adopting that kind of mental framework?Sully: Usually when we spin something up new very quickly, either one of us or he comes up with this uh metric that’s usually very core to either the financial or the physical return, or both sometimes. Um, and so everything is just focused on driving that metric. Um, there’s never like a fundamental limitation to it or like whatever the fundamental limitation is, it better be rooted deep down and not something artificial. Um, and there is a lot of uh perceived limitations, um especially in the software world coming from like especially in the last 10 years of like web dev and all these kinds of things. People just assume or accept certain limitations especially when it comes to speed and latency and they’re not true. Um, you can get rid of a lot of overhead. Like there’s a lot of stupid stuff in the stack and if you can knock out a lot of that, you can usually 2x to 8x most anything—at least anything invented relatively recently. Uh, some stuff not so much, but yeah.Tyler: When was the last time that you experienced this where there’s some like conventional wisdom that says that there’s this is the timeline and then you guys just were able to completely shred that?Sully: Um, most recently it’s our model iterations on Macro Hard. Um, so we’re working on some novel architectures actually multiple at the same time and uh we’re coming out with new like iterations like daily, sometimes multiple times a day from pre-train um in some cases, uh which is not something you ordinarily really see but it comes from well, a) we have a pretty great supercomputer team and they’ve knocked out a lot of the typical barriers it takes to train a lot of this stuff even with how variable our hardware is. Like, it’s you know, within a day of standing up a rack you can usually be training, sometimes within the same day, um even within a few hours in some cases and this is like not normal. Like normally the timelines are like days or weeks it takes a lot. Well, in most cases at least, yeah, in the last 10 years you abstract this away and let Amazon or Google take care of this, um and so whatever their capacity is is what their capacity is, but that’s not like you can’t have that be the case when in AI now. So, the only solution is to die or uh or build it yourself.Tyler: Can you tell me about like how what your experience was like joining, why you joined, and then kind of what the like onboarding process was for the first like couple weeks?Sully: Yeah. So, um I was working on my own startup when I moved to the Bay. Um and actually during that time, Greg Yang, one of the co-founders of xAI, had reached out. He’s great at recruiting as it turns out. Um, what did he say? Uh, so I got an email and I thought it was spam because I was getting a lot of these like, you know, emails to founders at the time of like, “hey, you want to chat?” or “I like what you’re doing, you want to chat,” whatever. I was going to mark it as spam to like delete it. And I saw the domain x.ai. I was like, “oh, wait a second. I know these guys.” And they just uh I think it was probably eight months in at that point. Um, and so I was like, “okay, yeah, let’s chat.” And so we chatted a bunch of times. Um, then uh I wanted an aqua-hire but uh I think we were too early at the time and that company kind of went mostly because it was fairly obvious that you can’t build Macro Hard with like a million dollars. Um, but the uh idea was sound.So I spent the next like six, seven months wasting all my money um building like aerospace projects and working on uh an aerospace astro-mining concept. Um, that also I realized like probably wouldn’t work, but it was worth a try. And so I emailed Greg again like uh, “hey can uh like you want to chat again?” He’s like, “yeah sure you want to interview tomorrow.” I was like, “okay.” And um uh I apparently did well and I moved on Monday and I started uh then and it was really great. Um, nobody told me what to do. So like my first day they just gave me a laptop and a badge and I was like okay. Um and I was like okay now what? And so I went to go find Greg cuz I was like, “I don’t I don’t even have a team.” I’ve not been told what to do. Like Greg just brought me on cuz I think he liked what I was doing previously and it was related to what the long term was for Macro Hard which wasn’t really even a project at the time.And I ended up working on actually uh as Grok was spinning up at the time where our integrations with X and so they’re like “can you help?” and I was like “yes I can help.” And so my first week was working uh with the one guy. I found out very quickly like everything that we built like I could sit and I could stand up from my desk which I didn’t even have a desk assigned to me. I just sat at random people’s desks that weren’t there that day. Um and I could point to whoever built that thing at xAI um like from my desk. It was very very very cool. And there was like almost no people working there at this point. Just like a couple hundred, right? Uh yeah, about a hundred or so on the engineering staff. And then I don’t know what the uh uh infra buildout team looked like at that time. And it’s kind of hard to tell because some people move up the ladder from like the actual building and construction crew onto our payroll. But um it was pretty small at the time—like much much like an order of magnitude smaller than the other labs. Um and we had still just done Grok 3. Um yeah, which yeah was pretty cool.Tyler: One of the things that I kind of love um is how fast xAI went from being founded. I remember Elon initially saying like we’re not even sure if this can be a success with you know people having you know a multi-year advantage on on speed and like timing and then you guys got done with the first like Colossus data center in like 122 days. Um and that was just like unheard of and Jensen’s out here saying singing the praises of xAI and Elon. Uh, what kind of culture did that allow to be formed?Sully: It definitely enabled like us on on model and product to kind of assume we would have the resources to do what we needed to do. Um and that’s definitely the case. Like we’re not super duper resource constrained. Like we’ve still found a way to push up against that wall. Um but that’s just we have 20 different things going at the same time. Like more than that, like many more things than that. There’s an absurd amount of of runs and training and all that stuff going on at the same time in parallel usually by like a few a handful of people. Um which is how we’re able to iterate very quickly on on model and product side. Um and utilization has definitely been very high. The the speed allows us definitely to I guess think more long term. Um, so I think Grok 4 or 5 really what it was was already planned out and and designed in terms of size and what we expected um way early—like before I joined. I joined around Grok 3. So it’s like thinking at least a year in advance. You can yeah you can think much more in advance and assume that those estimates will be hit um just because everyone’s like pretty great and reliable which frees you up a lot in terms of like what your limitations are I guess.So for us for example the assumed minimum latency was about three times higher than it actually needed to be and the buildout allowed for that basically. Um, what do you mean by that? So the one of the novel architectures we’re working on um is not really possible unless you scale up your experiment rate because it’s it’s not building on any existing body of work. You need a new pre pre-training body and you need also uh a new data set but that’s not really constrained by the resources—like the physical uh infrastructure resources mostly. Um although there’s the uh the Tesla computer thing which I think maybe we’ll get into maybe not but um uh so actually this one’s public. So one thing that we’re thinking about is okay like we’re we’re building this human emulator with Macro Hard. Um how do we deploy it? Because you actually need like if we want to deploy 1 million human emulators we need 1 million computers. Um how do we do that? And the answer showed up two days later in the form of a Tesla computer because those things are actually very capital efficient as it turns out. And we can run um potentially like our our model and the like full computer that a human would otherwise work at on the Tesla computer for much cheaper than you would in on on a VM on AWS or Oracle or whatever or even just buying hardware from Nvidia. That car computer is actually much more capital efficient and so it enables us to assume that we can deploy much much faster at a much higher scale. Um and so we’ve adjusted our we adjusted our expectations for that basically.Tyler: Are you basically able to just bootstrap off of the like car network?Sully: So that’s one of the one of the potential uh solutions basically. Yeah. So like okay well we want 1 million VMs. Um there’s like 4 million uh Tesla cars in North America alone. Um, and like let’s say 2/3 or half of them have hardware 4. Um, and like somewhere between 70-80% of the time they’re sitting there idle probably charging. We can just potentially pay—and they have, you know, networking, they have cooling, they have power—um, we can just pay owners to lease time off their car and let us run um like a human emulator uh digital Optimus on right on it. And uh they get you know their lease paid for and we get uh a full human emulator we can put to work. Um and that’s something without any buildout requirement. It’s a purely software implementation that’s required.Tyler: Yeah. The the asset is sitting there and you can just go and use it. Amazing. What for the human emulators uh in Macro Hard what is the like purpose of that of scaling up you know millions of many humans?Sully: Um, I mean the basic concept is very simple, right? With Optimus, you’re uh taking any physical task a human can do and allowing a robot to do it automatically at a fraction of the cost at 20, you know, with 24/7 uptime. Um, we’re doing the same with anything that a human does digitally. So any anything where they need to digitally input uh a keyboard and mouse inputs, which is usually what humans do, um and look at a screen back and make decisions, uh we just emulate what the human is doing uh directly. So no adoption from any software is required at all. Um we can deploy in any situation in which a human is in potentially currently.Tyler: Um interesting. What is what is that actually going to look like uh for rolling it out?Sully: Um I don’t think we’ve detailed our plans publicly yet specifically on uh on how we’ll roll out. It’ll be slowly at first and then very quickly basically like uh like the difference for us given that infrastructure buildout already has happened or we can go on the Tesla network or we can build out our own data center Tesla computers actually. Um the difference for us from from going from 1,000 human emulators to a million is actually not very big. It’s not it’s not the biggest part of the challenge.Tyler: Elon, I know one of the things that he does best is he basically just goes from fire to fire on whatever the company is and just kind of like puts it out and unfucks whatever problem is exists. Uh, what has that been like? What when have you like seen some problem exist and just had it unfucked very rapidly do this kind of process?Sully: Um definitely on the infra build out this is the biggest. Um on model side we’ve been like we’ve had hiccups but it’s more or less been smooth but on model side especially cuz there’s a lot of uh I mean infra side there’s a lot of very specific uh operations that each of these basically ASICs these GPUs are are built for and when we roll out new products like when we pick up new products from Nvidia or whoever um not everything works so in some of the meetings that we had with him uh early last year, uh he would hear these and he would make a phone call and the software team would deliver a patch the next day and we would work like side by side until that was resolved. Um and then we could run a model uh or a training run uh on the hardware uh very very quickly where otherwise it would have taken weeks of back and forth. So those kind of blockers are usually very quickly resolved with one phone call um or just us bringing it up to him or him just offering like frequently when uh a meeting is ending or there’s a lull in in the conversation he’ll be like, “okay how can I help? how can I make this faster?” whatever and someone will come up with with an answer.Tyler: I know you guys are doing many different products in parallel and I get that it’s kind of like you have to do that but also it’s sometimes in most organizations it’s like very difficult to stay focused on a single thing and like a single objective. How does that kind of work uh for just executing on multiple different fronts at the same time?Sully: Very frequently we actually uh and this is increasing with scale. We don’t have a full picture until like the all hands or we just chat with people what everyone is doing and how far everyone is on these different projects. Like for example on on when we did our our our voice model and our voice deployment um we actually had a lot of the work built for extremely low latency uh extreme low latency end like uh packets to be sent to the client. It was already built out and um it was a matter of flipping the right switches and the right configs basically to cut our latency pretty significantly um like 2-3x uh end to end. Um this is actually the case a lot of the time is there is a stupid thing that uh exists somewhere in the software or the hardware and someone has come up with a solution um and you find it when you go to look for it in in our codebase somewhere or you ask around and someone’s like “oh yeah this XYZ person has done this you should talk to them and they will hook you up.” Um there’s not a lot of time spent syncing up with anyone or asking for permission or um waiting for anyone at all. Like the answer is like when you propose someone someone says a good idea. Like usually you propose something and the the answer is either “no that’s dumb” or “why isn’t it done already?” Like um and then you go and do it and then it’s done.Tyler: With Elon companies, you can kind of just ask for responsibility and then you basically just live by the sword, die by the sword, and if you get things done, then you can just ask for more responsibility and you can keep on doing that or you’re just like out. What’s been your experience like with that?Sully: Very much so. Yeah, like um I’ve jumped around a lot of different projects and mostly just because someone asked for my help and I kept helping and then I ended up owning some of the stack or a lot of the stack. Um and this is the case for everyone—like this is just how it is. Um if you have any particular experience or um can iterate on something very quickly within days, you own that component. Um yeah there’s no formal anything I think officially on our HR software I I’m on voice and iOS or something and our security software thinks I still work on our X integration and um which never updated. Yeah. No, no one ever updates this stuff like um it’s kind of ridiculous.Tyler: And has has your like journey at the company kind of been you show up there’s not exactly like a clear direction of what you’re going to work on and then you just start working on stuff and then you just kind of like hop from project to project by whoever asks for your help?Sully: Yeah, there’s quite a bit of like overlap and flowing. Um so like after onboarding I’m usually on two or three projects at once. Um, and whichever one is most pressing or I can help the most on ends up taking majority of my time and then that kind of overlaps and flows in like a waterfall way.Tyler: What’s been the journey from like the starting to to now? Like what what projects have you worked on?Sully: Yeah, so specifically I started um I first worked on like ASRock uh and our integration there and I worked with our backend team a bit on like reliability and scaling up because we were scaling up a lot at that time. Uh and then after that I took on solo building up our our desktop suite. Um and took that went to internal completion. Uh and then I got asked for help on our Imagine rollout and iOS which yeah our iOS team is small for like how many people use it. Like it’s ridiculous. You won’t guess the number. Um like five people for three. It was three and I was the third person at the time when we were rolling that out. It was like it was ridiculous and everyone’s like really really good. Um yeah, this is the first place where I’ve had to work very hard to keep up really with like the the speed and the talent.Tyler: What was the first uh experience that you had where you thought to yourself like you’re actually being kind of used to your full, you know, potential?Sully: And I think that Imagine rollout was definitely like it was a really good push cuz like we had this 24-hour iteration cycle. Um you all would get feedback every night on whatever we were doing. Um and yeah, we we would push out that night. Uh in the morning we would have all the feedback. We would immediately knock out all the bugs, um implement the new stuff that that people were asking for. Whatever model had come up with, we implemented that too. Like it was a very very fast cycle and it was uh I think it was the longest like continuous stretch of me being in the office like every day.Tyler: What was that like at the time?Sully: It was like two or three months. Two or three times. Yeah. Yeah. Okay. Um yeah, like there weren’t weekends for a while, which was uh it was good to know that I could do that and I was pretty happy doing that. Um, and after that I got pulled onto Macro Hard product which was just one other person at the time. So it was the two of us uh for a while and I’ve been on that since uh since that project kicked off basically.Tyler: I don’t know how much you like know about this but uh the like Colossus build and all the ridiculous stuff that the like early xAI team had to do to turn on Colossus and like get power and all the necessary inputs to making that work. And even today, I think like it’s just bottlenecks across the entire thing. You just want more you you want more like uh chips and GPUs and all the stuff working and faster. Um what was that like?Sully: There’s a lot of war stories um and a lot of bets. Um want to go into a few? Yeah. So I think Tyler took this bet uh with Elon like uh one we were setting up new racks I think of I forget what which GPUs we were rolling out at that time. Um, we took a bet. Uh, Elon’s like, “Okay, you get a Cybertruck tonight if you can get a training run on these GPUs uh in 24 hours.” Uh, and we were training that night. Um, did he get the Cybertruck? Yeah, he got it. I think it’s Yeah, I see it from our lunch window. Mhm. Cafeteria. Yeah, he’s cool. Um uh you know what the I so for power we actually have to collaborate very tightly with the like municipal uh and state power companies uh because when load goes high on their end we have to shut off and go fully on the like 80—or maybe it’s more than that, I think more than that—80 mobile generators we brought in on trucks um and go fully on those um just so that we don’t like impact power uh anywhere. Within—and we have to do that like seamlessly without interrupting anyone’s uh extremely volatile training runs uh on extremely volatile uh you know GPUs and and hardware which scales up and down by like megawatts in milliseconds. It’s it’s a lot. Um, is that also part of the logic of like basically putting massive battery packs right next to the uh data centers cuz then you can kind of like go up and down much faster?Sully: Batteries can scale up a lot uh scale up and down and uh balance that load a lot faster. Um cuz with a generator you’re literally asking a physical thing to speed up or or slow down like a spinning spinning physical thing that’s obviously just going to take a certain amount of time. The batteries can uh react to the load much much faster and then yeah it’s like actually from the physical standpoint I think there’s the uh local capacitors, the station like data hall side capacitors, the batteries, and then generators, and then the public municipalities, although we might have changed that infrastructure at this point. Things move very quickly especially on the cooling side.Tyler: Do you have any other really good like war stories that are just like uh I don’t know things that shouldn’t have been possible that became possible?Sully: Uh, so the the lease for the land itself was actually technically temporary. It was the fastest way to get the permitting through and actually start building things. Um, I assume that it’ll be permanent at some point, but yeah, it’s I think a very short-term lease at the moment technically for all the data centers. It’s the fastest way to get things done.Tyler: And how do they how do they do that?Sully: Um I think there’s basically a special exception uh within like the local and state government says, “okay if you want to just uh modify this ground temporarily”—I think it’s like for like uh carnivals and stuff—you can. xAI is actually just a carnival company currently and so that was the way to get done quickly. I mean it was done yeah 122 days for like internal planning. I know things are just going to keep on scaling up like crazy and Elon’s talked about energy being the biggest bottleneck and then you know just being able to get chips. Um how do you guys plan when it’s very difficult to like predict 12 to 24 months in the future exactly what projects you’re going to be working on or what their like resource requirements are going to be?Sully: We try we try very hard to work backwards from like what’s the highest leveraged thing we can be doing and then we determine the physical requirements later. So like if we want to get to 10 or 100 billion in revenue by this date, uh what are the highest leverage things we can do like from an economic perspective? How can we actually build systems to do that? And then what does it take on the physical and software side to roll that out and and get it done? Um just roll down roll backwards the whole way. So we don’t usually start with the with the physical requirement. That’s usually actually at the end.Tyler: Is there like a SpaceX-esque um like algorithm for making things happen? As in like the usual “delete”?Sully: Yeah. Yeah. I mean that’s the case all the time. Um and we do do the thing where yeah we delete something and then add it back later. Um what was the like last time that you did that? Today. Today. Um today. Yeah. So with Macro Hard we deploy on um a lot of like physical hardware that changes and um the testing harness for that is hard. Um so we try to minimize how many special cases are downstream of where it needs to be. And um for example like with display scaling um we need to be able to support displays that are you know 30 years old as well as the latest like 5K Apple whatever displays and that has to happen on the same stack. Um, turns out not all the systems are happy with that at all times. Like you have to you have to fiddle with the encoders at a certain level. Like uh video encoders um was the specific thing basically. We I didn’t know but uh as it turns out there are limits to the maximum amount of pixels that certain encoders can take. So we have to now have I removed this special case for multiple encoders and turns out we found a problem at at plus 5K resolution and so we added that back.Tyler: What are the most interesting things about xAI itself um that you think like would be really good stuff to talk about?Sully: There’s a lot of characters that work there and also we’re doing hiring in like interesting ways I guess. Um like things that I thought would be stupid are okayed and we just do them and we try them and it’s like we we’ll do a hackathon and if we get five people in as a result it’s worth it. Um because just their like expected return on on the company’s like revenue or valuation is higher than the cost of running this hackathon for 500 people. Um like the overhead value is actually very high which is like funny. We did the math um earlier this week. Uh right now we’re like I think at about $2.5 million per commit to the main repo. Um and I did five today.Tyler: So you added like 12 and a half million of value. Um light days. Light days. Exactly. It was a good day. Um it’s funny things like that. Um like the levers are are extremely strong. Like you you can get a lot a lot done with a lot less effort and time than you used to be able to for sure just because of who you work with, the internal tooling that we built up. Um, and my boss.Tyler: What’s like an example of the type of person that like wants to work here? Cuz I know when when you’re talking about it, you kind of show up and the first day you’re just like, “I want to work on the weekends. I want to work on, you know, during the night, all this stuff.” Uh, go all in on this. Um, what kind of special characters are are working there?Sully: People are definitely very enthusiastic when they come in. Like, um, very very enthusiastic. Uh just like like mission oriented. Um there’s I guess different types of ambition for sure. Some people want to move up like the leadership ladder and own more in terms of a managerial like how many people report to me sense. Some people want to own huge parts of the technical stack. So like right now we’re doing a big rebuild um of like our core uh production APIs. It’s being done by one person with like 20 agents. Um, and they’re they’re very good and they’re capable of doing it and um, like it’s working well. Uh, so you can own huge chunks of the code base, no problem.Tyler: It’s kind of like a X where like after the acquisition they like had, you know, much fewer people, but you just like never had a lot of people in the first place, so there’s one person like owning a huge part of the product. Absolutely. For hiring. Um what’s what unusual practices outside of just hackathons uh does xAI do?Sully: Uh so we’re pushing very hard on Macro Hard. Like for two or three weeks I was doing upwards of 20 interviews a week. So that’s like some of them are like quick 15 minutes. Some of them are full 1 hour technicals. So a lot of my time uh is dedicated towards bringing in new people and a lot of people are very good. So it’s it’s actually very hard to judge them. How do you uh I have a very specific problem that I have solved. I’m not going to reveal it because then people will use it. But I have solved a very specific computer vision problem a few years ago for one of my startups and I uh I give people half an hour to try to implement the solution. It’s actually very very simple. This deceptively simple solution. People always overthink it. Um and this is something I like to index for on my team especially is like can you not overthink it and come up with a simple solution? Um it helps a lot because we’re deploying on such a wide variety of like uh on a wide variety of hardware as a result of the wide variety of of customers—like literally 30 years, 40 years of uh different hardware, different operating systems, everything like that. You have to come up with simple solutions or you’re going to have a 10 million line code base uh next week. So you you this is like very important. Um and especially now relying more and more on on agents and and an AI and and such for writing code. Um an AI will happily train out 200 lines when a 10-line solution will do um and probably do better. So you have to look for that. Like I want people and I look and actively hire for people who can find the 10-line solution first. Um, we’re totally fine with people using AI to code things. Like you should you should use that as a force multiplier, but uh for now we’re smarter. We’ll see next year.Tyler: What other like force multipliers do you kind of like look for?Sully: I like people who will challenge uh challenge requirements and challenge me. So often uh I got this from uh Chester Zai German for he told me this and I thought it was great. He throws in usually um an incorrect requirement or question or an impossible like uh line in uh his challenges for people when he’s hiring like coding challenges and he expects people to come back and say like, “hey this is wrong, this is not possible, you made a mistake,” and if he doesn’t, then uh he doesn’t hire them. Same thing for me—I picked that up. It’s a great idea.Tyler: The pace is insanely fast and like you said you kind of have worked on a number of different things. How do you kind of come up to speed on something as quickly as possible when you’re on a new task or project?Sully: It depends on what thing it is. If there’s a lot of code to read, yeah, read the code by hand. Um like GD (Go to Definition) over and over again and you’ll find things out very quickly. Actually, it’s not that hard. Um for most things, the implementation is like less lines of code than you would otherwise see, which is nice. Um not all the time, but in most cases, if it’s something that’s in very active development, this is not the case. There’s going to be 20 different versions of it going at the same time, and it’s not obvious what is the current path. So, you just got to talk to people, and people are very open. Like, this is actually one of the things I was very surprised by, uh pleasantly surprised by when I joined is I thought people would be super smart and stuck up, but no, people are just super smart and very nice and helpful. Like, everyone’s on the same team, everyone’s rooting for each other, people are willing to like help you out um and answer your questions. So, which is good because we don’t like write a lot of docs. We write things. We do things too fast to write docs really. Um, actually, yeah, we’re trying to figure out some systems on on my team to like automatically generate docs as we like build stuff. Um, and with Grok, which is cool that we have unlimited access to uh very smart AI because then we can try a bunch of stupid things, see if it works, which otherwise, you know, at a startup would cost you maybe like $100 or a hundred thousand dollars or a million dollars in in credits or whatever. We do it for free. So experimentation—like failure—you can fail on a lot of things and it uh a lot more things you otherwise would um and as a result more experiments.