Who would say, well, one, I'm very flattered that I now come across in that way, and some of my mentors from the early days of my last company, which to say I appreciate their patience, because when you initially met us, we were the deepest of tech founders, and then a couple of mentors that would essentially just like verbally abuse us be like, No, the tech doesn't matter. And then they kind of like shove your head dunk your take figuratively in the water, like now the tech matters, like dunk your head and again, you're like, No, no, it doesn't matter anymore. They're like, Are you sure in the dunk even one more time? Like, yes, the tech doesn't matter. So I think that that would be just the lessons learned of I wish I could say that that was intuitive from the start. But like when you come from a like Carnegie Mellon, undergrad, MIT grad school, like, there's a tech oriented ecosystem. So let's just say that it took me seven years to get compliments like that. So thank you. So for other deep tech founders, what I find that's interesting is a lot of the books and literature out there. It's like there's a lot of deep tech that gets mentioned, but a lot of the like, lessons learned, like, oh, how do you do the lean startup? How do you quickly iterate get an MVP out there? Yes, that's great. But like for Airbnb, you could throw up a website, do specific, like search engine targeting, and people would go in there and you could have a spreadsheet on the back end. If you're like, on the extreme opposite end. If you're building a nuclear fusion startup, and you're like, hey, we want to do that. Okay, you're spending the next five to 10 years in like pure r&d. demoed before, like, you get a sign of is a customer vendor that or not. And I view, one of the key differences for deep tech founders is navigating the product need and making sure that you understand the product, maybe because you have a little bit of a larger leap than other startups from when you've identified the customer need. And then you need to build something to address that customer need. And they're kind of in a leap of faith in the between that, so I would say, if you failed, and it's like three months, three, four, or five, maybe even a year of you like correcting that. So putting extra emphasis on understanding every nuance of the customer. So then people say, Oh, okay, do customer discovery on there, I talked to 4050 people, for deep tech founders, you should be talking to like 200, make sure that you understand the market. So when you're building the system internally, you can figure out all the ways to cheat, and I guess would be like nugget number two, find every way that you can cheat as possible to kind of speed up the development, as long as the end customer can't tell the difference. So then, like I talked about, for us on the mapping side, okay, we're building a very robust mapping stack on there. But we're also building manual tooling that like if it ever does, if the map does blow up, okay, we can fix that and overnight, and then maybe like, one or two nights of lost sleep for an engineer on there. But when the store opens again, at 6am, the next morning, everything's fixed, and everything's good to go. So then you can have kind of these fall backs on there, which from a pure technical standpoint, or myself six, seven years ago, I'd be like, No, it's better to spend it up and build it all out. But it's finding what are ways to cheat that don't take on too much tech debt. So there's a constant balance on there of okay, how do you please is for the end customer. And I guess this even gets back to what we were talking about on the hardware side of Niantic, which for your audience, like hey, building AR classes, major deep tech problem, but simplifying it down to like, we could go and talk to the game designers of Pokemon Go of ingress of other Niantic games on there, be like, What do you want from there, they would give you the laundry list, and you'd have to interpret that and you can't please everyone, you need to get back to some of the fundamentals of physics. But if you can have that close iteration loop, now that can work better when you're inside one company. But when you're doing a startup, you need to mentally have the full kind of counter arguments, the points, what are the things that the your customers care about in your head as you're developing the product on there, because again, you have like a multi month technical development jump that you go in there. And that's where you can start to ignore the fusion reactor, because it's so obvious, hey, I put in one watt I get out to, okay, you're now the richest person in the world. But then as you're doing, say, kind of computer vision, deep AI, some of the like nerf stuff, then there you can be building for months, maybe even a year. And if you don't have the customer mindset in your head, for Batum, you can live breathe and drink it just by talking to them understanding and making sure you're actually listening, because that's one of the problems for some of the MIT students that I mentor, love them dearly. And I'm doing to them what I got done to me, it's like, okay, you know, the, the tech doesn't matter, slap, slap, slap, but just making sure that you're listening and being able to reiterate, why do they why do they Why do your customers care about this, because when you're developing it internally, you may only get two shots at building a deep tech solution for your customer base. So therefore, that was one the reasons for my company why I spent the first six months like I had, after about two months, I had a decent idea of going after. And then I just went through, went to different conferences, talk to a bunch of people brought on, I built up my advisory board before I even built out and I kind of engineering team to go after it, just to have that feedback in there. Because for any deep tech founder, you don't have as many shots on goal. But on the other side, it's a lot easier to hire for deep tech stuff. Like I actually have been very fortunate for the team that hired because there's a bunch of AR people that are like, hey, an actual application. And I can see and people get excited about it. Like the founder of boom, supersonic. They're just like, Okay, we want to make supersonic jet, we want you to go to San Francisco to Tokyo in three hours. Like that's something that a lot of like you can NERT snipe a lot of people that way, can get them really excited. So while there's extra risks in deep tech companies I view there are a lot more extra benefits can get people excited about it. People want to help you, you stand out. You're interesting, you don't have as much competition. Like if someone said that they wanted to go and build a full mapping computer vision stack to try and compete with me and run everything on the small pair of classes and doodle. I would say good luck, like I hope you succeed. Other players they're like, Okay, I have five, six different competitors doing almost the same thing. It's almost a marketing game in terms of who succeeds, which you do that to a certain degree, but I would say that's, those are probably the main In high level lessons, and then there's a lot of individual nuances. Sure. For it for particularly companies.