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Speaker 1: How'd you like to listen to dot NetRocks with no ads?

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Speaker 2: Easy?

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Speaker 1: Become a patron for just five dollars a month. You

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get access to a private RSS feed where all the

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shows have no ads. Twenty dollars a month. We'll get

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you that and a special dot NetRocks patron mug. Sign

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up now at Patreon dot dot NetRocks dot com. Hey

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guess what. It's dot net rocks from build. I'm Carl

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Franklin Capel. We've been here for three days. This is

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our third day, and I think it's our third day.

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It's our third day, yeah, yeah, and we're here where

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the Imagined Cup finalists. Representatives from each of the three

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teams are going to interview them in a minute. But

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first let's talk about better know a framework.

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Speaker 2: Roll the crazy music? All right? Man? What do you got?

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Speaker 1: I went searching again for things repos on GitHub that

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are trending, and the number one repo right now is

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WSL because.

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Speaker 3: Interesting just Windows Subsystem for Linux. Yeah, because they just

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open it, just open starts it.

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Speaker 1: But before that, this one has been at the top

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AI hedge fund.

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Speaker 2: Okay, what's that? Okay?

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Speaker 1: Absolutely I haven't run it, but it's a proof of

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concept for an AI powered hedge fund. The goal of

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the project is to explore the use of AI to

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make trading decisions. This project is for educational purposes only

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and is not intended for real trading or investment.

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Speaker 2: It seems like a great way to lose a lot

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of money fast.

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Speaker 1: Yeah, But here's the cool thing if you think about it. It

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employs several agents working together. So this is a great

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example of agentic programming. The Aswaff demo, Darin agent, the

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dean of valuation, focuses on story numbers and discipline value.

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All right, I'm not going to read them all, but

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one of them is Charlie Munger, right, Barren Buffert's partner

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only buys wonderful businesses at fair prices. Michael Burry agent

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the big short contrarian who hunts for deep value. Peter Lynch,

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Phil Fisher, Stanley Druckenmeiler. Now the question is what do

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they train this on their data? You know they're trading

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data apparently, and you know they have big disclaimers. But

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it's very popular, and that makes me wonder how good

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it is.

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Speaker 2: You know what they say, past results do not indicate

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future results.

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Speaker 1: No, you're right about that, but I think you know

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understanding what we do about AI. It's either that it's

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really good clickbait and got a lot of people to

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download it, or it's working for some people.

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Speaker 2: Yeah.

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Speaker 3: I don't know either one of those is true, but

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something's happening for sure. Okay, So that's it.

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Speaker 1: That's weird AI things. And this one's pretty popular, So

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who's talking to us? Gravity comment to Show nineteen forty eight,

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the one we did with Rob MenShing. We were talking

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about the open source maintenance fee and just trying to

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make open source more sustainable. Got a lot of comments

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on that show. Actually, this one comes from Rob King,

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who says, in my experience of using open source in

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a commercial setting, the biggest barrier is the financial apparatus

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to get money to pay for maintenance. We talked about

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this on the show.

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Speaker 3: There are often forms to fill out and people to

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convince to get that payment. I suspective companies just gave

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devs a small annual stipend to spend on open source

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project the money would flow more readily. After all, if

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I've taken a dependency on open source project, I've already

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done its cost benefit analysis that seems very optimistic. Actually, Rob,

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mostly I had a problem and I made that problem

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go away. The actual money change part should be as

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low frish as possible. I came at this a different

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way during the show, where it's because I, of course

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have worked with CFOs. It's like, Hey, if we had

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a mechanism so a CFO could put one chunk of

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money once a year and it would distribute into open

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source based on utilization, they'd probably do that. Just a

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question of how hard that mechanism is developed. But Rob,

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I appreciate your thoughts, and a copy of Music Cobey

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is on its way to you. And if you'd like

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a copy of Musico buy I write a comment on

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the website at dot NetRocks dot com or on the

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facebooks we publish every show there. And if you comment

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there and I read it on the show, was that

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your copy of music Kobe?

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Speaker 1: And for those who don't know, music to code by

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is music that you put on in the background while

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you're trying to focus. It's not distracting, it's not boring,

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and it is scientifically based in what I should I say,

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it's based in science in a study about the aspects

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of music that help people focus.

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Speaker 3: So yeah, there's after they did that show with Mark Semen.

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We were talking about flow and focus and you then

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made a thing.

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Speaker 2: I made a thing.

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Speaker 1: So there's twenty two tracks now they're twenty five minutes

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each to correspond with a Pomodora technique and you get

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them at musicdocodebuy dot net, or as Richard said, by

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sending us a comment, Okay, let us talk about the

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imagined cup we have, right, you know what, You're right,

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we should do nineteen fifty six, but I did not

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prepare anything. So this is episode nineteen fifty six. And

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what we've been doing lately is talking about what happened

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that year in world events. M So, I don't know

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you've got something up there.

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Speaker 3: We're beginning of the Suez crisis. Oh, that was the

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invasion of Britain and trying to control that the waterway there,

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which is very big deal. Yeah, and the first time

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Elvis Presley became famous, right, the first detection of a

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neutrinoy an undetectable particle.

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Speaker 1: An undetectable particle that can go through walls and lead

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and everything else. How did they even detect it with really?

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Once in a while the Trino's decay. It's very rare

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and by far most importantly, General Electric releases the first

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snoozeable alarm clock. Wow, there you go, but then you

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couldn't snooze.

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Speaker 2: Wow.

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Speaker 1: I wonder if it sounded like modern techno music.

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Speaker 2: I'm thinking that's the thing. The beat nineteen fifty six.

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Speaker 1: Yeah, so all right, well there's more stuff to talk

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about in nineteen fifty six, but we want to talk

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to the Imagined Cup finalists builds. So we have representatives

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of each team, just one representative from each team. So

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I'll start with Matt. Matt Steele is on the hair

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Match team. He's a Georgia Tech computer science grad with

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a concentration in AI and embedded systems. He has published

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more than five apps to the iOS app Store with

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over twenty thousand monthly active users. Matt's also a former

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US Olympic Trials qualifier in swimming and took fifth place

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at the US Open in swimming.

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Speaker 2: Wow. How about that?

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Speaker 1: And on top of that, you're in the Imagined Cup

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Finalists And so that's from the hair Match team. Guida

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Abdallah Omar is on the sign Verse team, and she

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is a passionate innovator and project associative Signverse that's sign

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v r S, where she leverages technology to address social challenges.

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Is the founder of Girls I Save, an initiative empowering

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girls in STEM and advocating against gender based violence. She

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brings a strong commitment to community driven tax solutions. A

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Guide is also a Generation Connect Youth envoy with the ITU,

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a judge for Young Scientists Kenya, and a recognized change

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maker in the Coastal region of Kenya. Her work combines

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information technology, advocacy and innovation to create inclusive, impactful change.

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Welcome and Daniel Kim is studying math in neuroscience at

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Stamford and seeing his grandmother struggle with her visual impairment,

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Daniel was inspired to pursue Argus, which is one of

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the teams in the finalists. In his free time, he

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loves spending time with animals in any capacity he can. Okay, well,

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welcome guys to dot ne Rocks and congratulations, thank you

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for having us. You bet, Matt, Let's start with you.

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Speaker 2: So here.

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Speaker 1: Match is the team that you are on and tell

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us a little bit about that.

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Speaker 4: Yeah sure.

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Speaker 2: So.

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Speaker 4: Hair Match is a mobile app which allows women with

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curly hair to take pictures of their hair, and then

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we use AI to be able to determine their hair type,

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and from there we've done the R and D to

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be able to curate products based on their specific hair

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type that work for them. And so there's a very

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big problem with curly hair care where people will spend

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a lot of money on products and then the products

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don't actually work for their hair because there's something really

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specific about that one product, you know, might be one

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chemical or one molecule that's often the formulation that doesn't

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work for that person's hair type. And then you end

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up with a bunch of products under yourself. And so

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my co founder, Relisha, has very very curly, very long hair,

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and this is a problem that she faced first.

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Speaker 3: I imagine just I've seen shelves like that just yeah,

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bottles that were used once.

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Speaker 4: Yeah, just you know, you use it once, but once

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you use it once, you can't return it. And so

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she just had this. She called it the shelf of shame.

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Speaker 2: So it's like our phones.

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Speaker 4: And so I've been friends with her since we interned

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together about two years ago. We were both college athletes.

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She cheered at University of North Carolina and I swam

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at Georgia Tech, and so we just became friends over that,

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and then eventually we both are really interested in building things,

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and so we decided to make hair match and that's

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kind of how it was born.

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Speaker 1: Now she's technological also, or was she just interested in

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the problem.

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Speaker 4: So she she is technical, she can code, but she

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primarily her strengths are in the media and the product

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side of things, and so primarily those two things were

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her focus on the team. And then she has a

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very very deep understanding of the problem because she is

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the customer.

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Speaker 2: Of her Perfect Perfect Domain Expert.

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Speaker 1: So people listening might be going, what you know, that

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seems kind of silly, But you know, I have two

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stepdaughters with curly hair, and they struggled all their growing

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up lives and into adulthood probably still do now with

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this problem. I'm you know, straightening hair was and just

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combing it could be a huge shoe, right, And some

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people want to straighten curly hair. Some people want to

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have smaller curls when they have big curls. Like there's

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a whole bunch of styling things and just maintenance.

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Speaker 4: Yeah, and there's a lot of societal pressure as well

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on people with curly hair and either straighten it or

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to keep it in very traditional hairstyles. They've been commonly

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accepted in the workplace for decades here especially, I guess

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primarily in the US because that's where we're based out of.

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But you know, one of the things that Alicia and

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I believe in is we want people to be able

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to have equal opportunity professionally and from a self confidence

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perspective as well, and so being able to build something

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that directly improves people's ability to style their hair and

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leave their confidence is great.

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Speaker 1: We can go to the app store and download hair

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Match right now.

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Speaker 4: Yeah, it's been live for six months now.

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Speaker 1: Actually, wow, what's the response been.

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Speaker 4: The response has been great. So in the app, we

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did for a while have my phone number in there.

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People could just text me if there was a problem,

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and I would just get these texts at random hours

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of the day like I love this, this is amazing, guys,

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you're doing great. We get emails like that as well,

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and so the response has been overwhelmingly positive, and that's

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something that we've really enjoyed.

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Speaker 2: Talk to me about the workflow, like, how does people

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use the app.

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Speaker 4: Yeah, So a user will download the app from the

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app Store and they go through onboarding. We ask them

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a few questions about their experience with hair and the

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sort of what they already know versus not, so we

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can tailor the experience a little bit to that. Then

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the primary magic is we have them take two pictures

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of their hair very up close in good lighting, so

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we're able to actually.

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Speaker 2: See that in the more in detail.

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Speaker 4: We process that using a visual transformer that we run

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in Azure ai foundry, and we're able to get the

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hair type. Once we get that hair type, we give

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them a nice little savable card that they can save

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to the chemeeral. It's like, oh, this is your hair analysis.

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It's really cool.

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Speaker 1: We it's like five or six different categories that you

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check exactly.

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Speaker 4: We have five different properties on that card that they

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can see. And then from there they're able to enter

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the app and they can go to the product recommendations list,

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which is curated directly for their specific match of those properties,

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and it goes, Okay, this is what this person needs,

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and it shows them only those products, and they're all

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sorted by the category as well, so whether it's gel,

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whether it's shampoo, whether it's conditioner, they can get easy

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access to those. And so instead of having to go

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to a store and you have, you know, a million

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products on the shelves, you don't know which ones actually

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work for you unless you, you know, look each one

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up one by one, and you have to go to

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some obscure web page to find that. We have that

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information all in front of the user, all at once,

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and it's very streamlined, very easy.

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Speaker 3: And of course my instinct is now you'll go to

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the store with the list and look at that. Was like, no,

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you won't, You'll go on Amazon in order.

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Speaker 4: And so the other thing we do is we have

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links to every single product inside the app, so we

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don't actually sell the products, but we connect the people

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to the source. And so what we've actually been able

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to do with that is we are able to offer

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discounts on those products because we reach out to the

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brand and manufacturers that we've found through our R and.

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Speaker 2: D to be a monetizing strategy.

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Speaker 4: Then, not necessarily for the modestation, we did it more

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for the customer. We do get the small affiliates, but

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it's been primarily good for us because we're able to

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not only you know, give the customer exactly what they want,

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but we can also save the money, which is really

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really cool.

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Speaker 1: Yeah, that is cool and congratulations, that's a great thing.

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Speaker 2: Thank you.

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Speaker 1: Just curious and any of you can answer this. How

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many other teams did you guys beat out to get

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in the top three?

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Speaker 2: A lot? But yeah, the match kept huge.

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Speaker 4: Yeah, I think it was. There were over fifteen thousand

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students that participated.

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Speaker 5: This year, one and fifty countries.

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Speaker 2: Wow.

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Speaker 1: So just being here and you guys were on stage

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right before the keynote, yes, on the first day of build.

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That must have been really exciting.

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Speaker 4: Oh yeah, yeah, it was. It was amazing, Just a

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great experience to be up there. You know, you look

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into the crowd and you know, the lights are a

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little bit blinding on stage, but you can see so

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many people and it really hits you in that moment

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that you know.

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Speaker 2: You're there thousands of people in them.

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Speaker 4: Yeah, it's like you've made something impact.

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Speaker 2: We have been hundreds of yers watching online.

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Speaker 4: Yeah, not the freaky out that it was like thinking,

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you know, there are almost as many people in there

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as we had users on hair Match in the first

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couple of months. Sure, that's insane.

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Speaker 2: Yeah. Well, also realization like that, how many people you

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attached to? Yeah?

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Speaker 3: Yeah, it seems like a niche product, but clearly for

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a certain group of folks it's really important, really important.

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Speaker 4: We estimate that there are probably about a billion people

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or more that could benefit from our product in this

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current state.

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Speaker 1: Wow, should we switch to you gata? Should we talk

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about sign Verse?

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Speaker 6: Of course?

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Speaker 2: Yeah? So what is the product and tell us about?

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Speaker 7: The sign Verse is an AI pod platform that chooses

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three D avatar to convert speech and text to real

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time sign language interpretation.

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Speaker 2: Wow. Wow, that's really cool.

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Speaker 7: So in basic what it's communication breaking the barriers between

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a person who's had of hearing and a person who

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can hear, because sometimes it becomes very challenging when people

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you know, communicate and someone cannot hear.

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Speaker 5: So with the app, you can be able to type

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in list hello, good morning.

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Speaker 7: So the avatar will be able to do the sign language,

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and then the person who is next will be able

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to see that through the application. So you can either

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use text or speech and making communications simless for both

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the hearing and the head of here.

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Speaker 2: So I could hold up my phone just talk and

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it's going to do the.

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Speaker 7: Sign language, Yes, absolutely, And we drove the inspiration because

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Brandis and I used to participate in Young Scientist Kenya

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That's Pack Home, and we used to do robotics training

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for young children and one time we found ourselves in

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a situation where the students that we're supposed to train

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were not able to hear, like the head of hearing

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and some of them were completely deaf. And you know,

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the amount of signed interpreters in the room like what

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to go to different classes, so there was already a gap.

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We had to you know, find ways to be able

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to bring that message right in terms of coding and

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talking to the student. But ever since then we realize

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there is a gap actually, and then we met Daniel

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who is one of our team who literally had to

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switch from being a computer student to being in the

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business because the school did not have sign language interpreters

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for that cause. So you can imagine the change he

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literally had to switch. And you know, it also comes

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with a lot of emotions to these people, they feel neglated.

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The community, you know, pushes them away because of the

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stigma of them not being able to hear. And that's

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why we were driven to, you know, create a technology

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that's going to focus more on providing real time sign

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language interpretation at different levels.

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Speaker 1: So you just mentioned sign language interpretations, so this app

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will also you can hold it up to somebody who's

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signing and it will say the words to you.

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Speaker 5: So that is like a future vision that we want

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to do.

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Speaker 7: For our first product, it's just converting speech to text

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and then to sign language. But in future we believe

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that we will be able to do the vice versa.

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And that's why we have signed verse so that we

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can go from sign to verse and back.

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Speaker 2: Yesh wow.

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Speaker 1: Because we used to have remember the Connect Microsoft Connect

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and it could track all of your joints in three

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D space. Yes, I think it was twenty twenty frames

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pertective like that, So there were applications that use that.

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But I think it wouldn't be more challenging on a

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phone to go from straight video to generative AI now right.

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Speaker 7: Yes, we're actually trying to incorporate the use of AI

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to be able to make it more seamless and more

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interactive because.

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Speaker 3: Even that Spelton tracking had problem with hands. Yeah, and

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sign of course it depends on the flavor signed too,

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doesn't it.

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Speaker 7: Yes, we have different sign language and because of the

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motion capture suits that we acquired and we incorporate the

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different data sets from Kenyan sign language to Ruandan Sign

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language to even SL So in future we're hoping to

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incorporate or different sign language based on the context of

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the country and the community that it.

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Speaker 2: Comes literally country to country, the sign language varias.

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Speaker 7: Yeah, even in Kenyan Sign language itself, it's narrowed down

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to different community sign language stylists.

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Speaker 2: Yes, yeah, regular language.

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Speaker 5: It's just a language.

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Speaker 7: It's just that it's now in form of you know,

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hand motion gestures and stuff.

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Speaker 1: When you have your every area, every dialect has its

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own idioms and things like that.

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Speaker 2: Yes, other people may not.

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Speaker 3: Understand just collecting all that data it's going to be

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really powerful, exactly, But I don't know that there's a

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good repository for.

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Speaker 2: All of the variations.

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Speaker 7: We're actually finding very hard in Africa to get like

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a very concrete data set when it comes to sign

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language interpretation. So far, we've gotten over twenty three hundred

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glosses in terms of the sign language interpretation, but we're

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looking forward to expanding to around two hundred and fifty

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thousand in the first phase and then five hundred thousand closses,

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so it's actually a lot.

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Speaker 2: A very definitive collection exactly.

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Speaker 1: Yes, so in as signed verse in the app stores

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and downloadable now.

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Speaker 7: So we've not been able to have it fully installed,

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but it's live. You can be able to access it,

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but it's not on the download for because we are

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still trying to ensure that it is accessible enough.

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Speaker 2: And yes, yeah, okay.

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Speaker 1: This is amazing and your cause, like I imagine after this,

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you've got another several things on your to do list.

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What's your plan for the future.

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Speaker 7: So just like I mentioned, we are planning to really

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expand in terms of the data because that is one

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of the most critical aspects we need to input in

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our platform. So we are looking at expanding beyond just

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Kenyan sign language to Rwandan and different African sign language

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and then also incorporate different you know, business model in

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terms of industries where communication is critical, just like in

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the healthcare just imagine you're going to the doctor and

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the doctor is not able to understand you. So tailoring

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our platform to you know, contextual to healthcare system, you know,

425
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transport system, like we're having communications with Kenya Airways, so

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that we ensure like whatever communication is there, our platform

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is also there to help people with the hairing close.

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Speaker 1: So I guess my question was, Okay, after this app

429
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is done, yes, then you'll probably move on the other

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thing because you're you know, the causes that you're talking

431
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about with girls and young girls.

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Speaker 5: Uh huh.

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Speaker 1: I imagine you know you've thought about what to do next,

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but it sounds like you're going to be working on

435
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this app for quite a long time.

436
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Speaker 2: Yeah, so much to do.

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Speaker 7: Yes, So what I always do with the young guys

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is you have a lot of outreach programs that I

439
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do back in my community, and it's always tailed back

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to the innovation that I do. So I introduce them

441
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to the aspect of STEM that is science, technology, engineering

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and mathematics. Since I was a young scientist, Kenya Winna,

443
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I have been in the innovation space for over three

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years now, so just trying to tailor what I do

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as a as a rider to what the community also

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needs to know. So I do outreach programs. I do

447
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you know, online talks. I give video conferences for youngers

448
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to be able to understand how it is to be

449
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in that tech field, especially in building solutions.

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Speaker 1: Yes, that is so wonderful. Wow, great, great, great stuff.

451
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Is the time take a break? Do you want to

452
00:21:02,920 --> 00:21:05,079
do that? Now? Let's do the break, all right, we'll

453
00:21:05,079 --> 00:21:08,079
be right back after these very very important messages to

454
00:21:08,160 --> 00:21:13,000
come around. Do you have a complex dot net monolith

455
00:21:13,039 --> 00:21:16,559
you'd like to refactor to a micro services architecture. The

456
00:21:16,640 --> 00:21:20,519
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457
00:21:20,680 --> 00:21:24,759
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458
00:21:24,759 --> 00:21:33,359
at aws dot Amazon dot com slash modernize. Now we're back.

459
00:21:33,400 --> 00:21:35,839
It's dot net Rocks. I'm Carl Franklin. That's my friend

460
00:21:35,920 --> 00:21:40,119
Richard Campbell. Hey, and we're here with the Imagined Cup finalists.

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And I guess that brings us to you, Daniel Daniel Kim.

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Of course it's probably no secret buy now, but your

463
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team Argus won the Imagine Cup. So pig around of

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applause for you.

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Speaker 8: Thank you, thank you so much.

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Speaker 2: Yeah.

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Speaker 1: And the thing, well, first of all, tell us what

468
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our argus is, and then I have so many questions.

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Speaker 8: Okay, right, happy to answer any questions you may have.

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Speaker 2: Okay.

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Speaker 8: So Argus was primarily spawned out of our dorm room

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where Arjene and I both met at Stanford three years

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ago and we realized that we both really enjoyed building,

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but we also realized we had a personal connection to

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visual impairments because both our grandparents were aging into them

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and just like seeing them no longer being able to

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do things that they were able to do prior. It

478
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was like we realized how much was taken for granted,

479
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and we really wanted to find a way to give

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them back that independence in their day to day lives

481
00:22:39,480 --> 00:22:43,759
because I know my grandma, she's definitely like she's got

482
00:22:43,759 --> 00:22:47,480
that pride and she enjoys being able to do things

483
00:22:47,480 --> 00:22:49,839
on her own and she dislikes asking for help. So

484
00:22:49,880 --> 00:22:53,799
it was definitely wanting to give that independence back. Kind

485
00:22:53,839 --> 00:22:56,400
of spawned arguson and I guess I could talk more

486
00:22:56,440 --> 00:23:00,640
about what argus is. So what we realize was that

487
00:23:00,680 --> 00:23:05,240
there were a lot of accessibility gaps in what's already

488
00:23:05,240 --> 00:23:06,720
out in the market. So I'm sure this is a

489
00:23:06,759 --> 00:23:09,440
tech audience. You've seen something like the meta ray bands

490
00:23:09,440 --> 00:23:12,640
and you're wondering, oh, why can't you just use that. Well,

491
00:23:12,759 --> 00:23:15,279
for a couple of different reasons, we realized our grandparents

492
00:23:15,359 --> 00:23:16,480
didn't really like them.

493
00:23:16,960 --> 00:23:17,400
Speaker 2: Sure.

494
00:23:17,519 --> 00:23:20,839
Speaker 8: For one, they don't really directly address the accessibility needs

495
00:23:21,279 --> 00:23:25,480
where one you're gonna have to like connect to Wi Fi.

496
00:23:26,000 --> 00:23:30,039
So something that we decided to pursue is building this

497
00:23:31,240 --> 00:23:34,960
hybrid system out where you have both edge compute, so

498
00:23:35,480 --> 00:23:38,920
Argus's two piece device. There's a camera module that goes

499
00:23:38,960 --> 00:23:40,440
on your glasses, frames.

500
00:23:40,160 --> 00:23:43,039
Speaker 1: On your own glasses. You don't have to buy new glasses.

501
00:23:43,200 --> 00:23:45,720
Speaker 8: It's very important, yeah, for sure. So just being able

502
00:23:45,799 --> 00:23:50,480
to easily integrate that into someone's already existing I guess,

503
00:23:50,519 --> 00:23:51,680
like habits in life.

504
00:23:52,000 --> 00:23:55,559
Speaker 1: Until it recognizes things in and tells you what it

505
00:23:55,559 --> 00:23:58,440
doesn't speak, does it put a thing.

506
00:23:58,440 --> 00:24:03,720
Speaker 8: On the screen, It gives audio feedback regarding whatever your

507
00:24:03,759 --> 00:24:09,759
question was, so o cr or just like spatial awareness.

508
00:24:10,720 --> 00:24:14,559
Speaker 1: Reading letters and bills and mail and yeah, I can imagine.

509
00:24:14,240 --> 00:24:16,319
Speaker 8: And just like figuring out where you are relative in

510
00:24:16,359 --> 00:24:19,119
a room. I think that's like incredibly helpful where it's

511
00:24:19,119 --> 00:24:20,839
like I need to find where the exit is, I

512
00:24:20,839 --> 00:24:23,400
don't really know how to get there. Yeah, So that's

513
00:24:23,960 --> 00:24:26,359
so we're able to do simple tasks like that all

514
00:24:26,400 --> 00:24:30,200
on the edge. So and we have a compute module

515
00:24:30,240 --> 00:24:33,279
that goes in your pocket. And these two systems are

516
00:24:33,279 --> 00:24:38,440
connected with the technology called hy Are, which is a

517
00:24:38,440 --> 00:24:41,599
wireless communication method that uses your skin as a conduit

518
00:24:41,799 --> 00:24:44,160
uses one hundred times less energy than something like that.

519
00:24:44,319 --> 00:24:46,640
Speaker 1: So this these are this is what I started looking

520
00:24:46,720 --> 00:24:50,359
up because when you and it just kind of wasn't

521
00:24:50,400 --> 00:24:54,119
an in passing comment during the you know, and I'm.

522
00:24:53,960 --> 00:24:56,839
Speaker 2: Like, wait, what what did you say? So, yeah, it

523
00:24:56,920 --> 00:24:57,440
used your skin.

524
00:24:57,519 --> 00:24:59,839
Speaker 1: So I looked it up and it has a range

525
00:24:59,880 --> 00:25:03,240
of like what five meters or something like that. It's

526
00:25:03,279 --> 00:25:08,319
not bad and it uses your skin lower power consumption.

527
00:25:08,799 --> 00:25:11,720
The one thing that and it's fascinating to me. The

528
00:25:11,759 --> 00:25:14,319
one thing that I didn't read in the white paper

529
00:25:14,440 --> 00:25:18,880
was latency. Like, you know, bluetooth has latency, right, and

530
00:25:18,920 --> 00:25:20,759
so you can't use that for real time, but just

531
00:25:20,799 --> 00:25:23,559
getting onto the YR thing, it probably doesn't matter all

532
00:25:23,599 --> 00:25:25,440
that much for you guys, but do you have any

533
00:25:25,480 --> 00:25:27,599
idea of what the latency is?

534
00:25:27,759 --> 00:25:30,400
Speaker 8: Yeah, so the latency for us personally has been like

535
00:25:30,599 --> 00:25:33,440
just a couple of seconds. So it's so what we've

536
00:25:33,480 --> 00:25:34,920
been able to do is like a couple of time

537
00:25:34,960 --> 00:25:38,200
saving strategies where it's like as soon as the user

538
00:25:38,240 --> 00:25:41,680
starts talking, we start communicating with the compute.

539
00:25:41,240 --> 00:25:42,319
Speaker 2: Module stream it.

540
00:25:42,519 --> 00:25:45,759
Speaker 8: Yeah, so it's just like immediately what's going on. It

541
00:25:45,799 --> 00:25:47,759
starts like figuring it out, and then by the time

542
00:25:47,799 --> 00:25:50,720
of the end of the query, there's just like watching

543
00:25:50,759 --> 00:25:52,680
out it. It's just like, yeah, catching up. I think

544
00:25:52,680 --> 00:25:53,480
that's a good way to put it.

545
00:25:53,559 --> 00:25:56,000
Speaker 3: Yeah, and then just trimming a little that little extra

546
00:25:56,039 --> 00:25:58,559
timeout so you can respond, you have more time to respond.

547
00:25:58,279 --> 00:25:59,839
Speaker 8: To make it much more I guess, like I've seen

548
00:26:00,160 --> 00:26:02,519
experience where you're not like, oh, now there's a big

549
00:26:02,599 --> 00:26:04,920
like pause, but you are traveling to the cloud and back,

550
00:26:04,960 --> 00:26:08,319
I imagine. So for simple tests, that's just all going

551
00:26:08,359 --> 00:26:10,720
to be on the device on the device, and then

552
00:26:11,000 --> 00:26:13,559
for more complex tests that's like, oh I want to

553
00:26:13,720 --> 00:26:15,720
help me like reason something out, it's going to be

554
00:26:15,799 --> 00:26:20,200
sending query to Azure ai foundry. So it's a hybrid model, right,

555
00:26:20,440 --> 00:26:23,160
and it's a Raspberry Pie based thing, is it. Yeah,

556
00:26:23,240 --> 00:26:26,319
so right, our prototype is using a Google Coral dev.

557
00:26:26,200 --> 00:26:29,319
Speaker 3: Board at the moment, right, So I've got a couple

558
00:26:29,319 --> 00:26:33,480
of those USB it's a it's a little NPU essentially, yeah, yeah,

559
00:26:33,799 --> 00:26:38,440
relatively expensive. Inexpensive at the time, but yeah for compute

560
00:26:38,440 --> 00:26:40,799
to render some a small model.

561
00:26:40,640 --> 00:26:41,279
Speaker 2: Yeah for sure.

562
00:26:41,359 --> 00:26:43,200
Speaker 8: So like three years ago, Archie and I were like,

563
00:26:43,279 --> 00:26:44,720
we don't have that much money, we got to like

564
00:26:44,720 --> 00:26:46,920
figure out how to So we're just using a lot

565
00:26:46,920 --> 00:26:51,599
of hobbyist grade we'rement built out our own camera module,

566
00:26:52,000 --> 00:26:53,759
and it was just like from there we started building

567
00:26:53,799 --> 00:26:54,599
out our prototypes.

568
00:26:54,720 --> 00:26:57,240
Speaker 1: And the prototype for the camera module is what how

569
00:26:57,400 --> 00:26:58,240
how big would you say?

570
00:26:58,240 --> 00:27:00,920
Speaker 2: It is? Like a inch and a half diameter or

571
00:27:00,920 --> 00:27:01,440
something like that?

572
00:27:01,960 --> 00:27:03,440
Speaker 8: Yeah, I would say like an inch and a half

573
00:27:03,519 --> 00:27:06,200
and we're definitely gonna be able to shrink that down. Yeah,

574
00:27:06,400 --> 00:27:08,279
So that's gonna be a next step as well, just

575
00:27:08,319 --> 00:27:10,240
like miniaturization of the device.

576
00:27:10,400 --> 00:27:12,759
Speaker 3: If you start thinking in terms of building purpose built

577
00:27:12,799 --> 00:27:16,000
hardware with current generation tech, like A, you're gonna get

578
00:27:16,039 --> 00:27:18,359
a lot more compute. B Yeah, you're gonna be able

579
00:27:18,359 --> 00:27:19,359
to make it really compact.

580
00:27:19,519 --> 00:27:21,559
Speaker 2: Yeah. You care about form factor a lot? Oh yeah

581
00:27:21,599 --> 00:27:22,240
for sure. Yeah.

582
00:27:22,519 --> 00:27:27,440
Speaker 1: Have you had anybody ask you about the health ramifications

583
00:27:27,480 --> 00:27:30,240
of the y R stuff? Is there is there any

584
00:27:30,240 --> 00:27:33,920
concern about that? I mean, does it use uh what kind.

585
00:27:33,759 --> 00:27:37,599
Speaker 8: Of I mean, there's no real concerns about health ramification.

586
00:27:37,799 --> 00:27:42,119
So we're not too concerned about that being a factor

587
00:27:42,200 --> 00:27:46,160
be considering Like it's like maybe if you're concerned about

588
00:27:46,200 --> 00:27:49,920
five G you could be considered because so it's.

589
00:27:49,759 --> 00:27:51,839
Speaker 1: Like five it's a five G transmitter.

590
00:27:52,079 --> 00:27:53,680
Speaker 8: Oh, I guess like the analogy I'm trying to make

591
00:27:53,759 --> 00:27:55,960
is like, Okay, if you're worried about five G, then

592
00:27:56,240 --> 00:27:57,759
I see, yeah.

593
00:27:57,440 --> 00:27:59,920
Speaker 2: Anybodys will be worried about turning on lights then yeah. Yeah.

594
00:28:00,920 --> 00:28:03,160
Speaker 3: That's the other thing about these close area networks is

595
00:28:03,160 --> 00:28:05,119
they're really low power. I mean, the whole goal here

596
00:28:05,200 --> 00:28:07,279
is to save energy. You need a lot of power

597
00:28:07,319 --> 00:28:07,680
to do that.

598
00:28:07,720 --> 00:28:10,359
Speaker 1: Oh it's like a hundred times less power than Wi Fi,

599
00:28:10,440 --> 00:28:10,680
is it.

600
00:28:10,920 --> 00:28:15,480
Speaker 3: Yeah, compare that to putting a cell phone up your head. Yeah,

601
00:28:15,680 --> 00:28:16,720
that's a lot more energy.

602
00:28:16,759 --> 00:28:22,480
Speaker 2: But either way, no measurable effect. It's not a thing.

603
00:28:22,839 --> 00:28:25,359
Speaker 1: So the thing, you know, the YR stuff is the

604
00:28:25,359 --> 00:28:27,839
most exciting thing for me. I mean, obviously it's a

605
00:28:27,839 --> 00:28:31,119
great product and it solves a problem. But this opens

606
00:28:31,200 --> 00:28:34,640
up like a world of possibilities. And you were the

607
00:28:34,720 --> 00:28:38,400
first one I've ever heard using this technology.

608
00:28:39,079 --> 00:28:41,480
Speaker 3: Handshaking and so forth for devices like that is a

609
00:28:41,519 --> 00:28:45,160
real nuisance for people who aren't technical or like I said,

610
00:28:45,200 --> 00:28:49,200
have side impairments so they can't see QR codes or

611
00:28:49,599 --> 00:28:52,000
enter long passwords and things like that. The idea that

612
00:28:52,039 --> 00:28:54,799
the close area network, the fact that you're close enough

613
00:28:54,799 --> 00:28:57,680
to it is the handshake and so they just connect.

614
00:28:57,920 --> 00:28:58,160
Speaker 2: Yeah.

615
00:28:58,279 --> 00:29:00,759
Speaker 8: I think the idea is like you wanted it to

616
00:29:00,799 --> 00:29:03,480
be something that just works right out of box. You

617
00:29:03,480 --> 00:29:05,920
put it in your pocket, put it slided on your glasses,

618
00:29:06,240 --> 00:29:07,920
and my grandma doesn't want to have to worry, Oh

619
00:29:07,920 --> 00:29:10,759
do I have to download X y Z features or

620
00:29:10,759 --> 00:29:12,920
do I have to set up Wi Fi? Yeah, these

621
00:29:12,920 --> 00:29:14,480
are all things we just wanted to be able to,

622
00:29:14,799 --> 00:29:18,119
you know, hand off to the elderly. Who are you know,

623
00:29:18,279 --> 00:29:22,400
our biggest demographic for this issue. So I think making

624
00:29:22,440 --> 00:29:24,000
it so that it just works out of the box

625
00:29:24,079 --> 00:29:25,640
is something that was really important to us.

626
00:29:25,519 --> 00:29:26,720
Speaker 2: Very important. Yeah.

627
00:29:26,839 --> 00:29:30,519
Speaker 3: And the security part is the perimeter. It's it's small. Yeah,

628
00:29:30,559 --> 00:29:32,039
it has to be close enough to you. Yeah, you

629
00:29:32,519 --> 00:29:34,839
have to be like able to touch the person. And

630
00:29:34,880 --> 00:29:38,240
then the second requirement is that you also have a

631
00:29:38,400 --> 00:29:41,400
YR device as well, where it's like you're able to

632
00:29:41,480 --> 00:29:44,599
receive some of that transmission, right, and yeah, that would

633
00:29:44,599 --> 00:29:47,799
be like a really random thing to have to Yeah,

634
00:29:47,839 --> 00:29:49,839
So we think that the security aspect of it is

635
00:29:50,400 --> 00:29:51,880
also a big sell factor for us.

636
00:29:52,039 --> 00:29:54,119
Speaker 2: Sure do you have a patent on this?

637
00:29:55,240 --> 00:29:59,720
Speaker 8: We are patent pending for the system. So yeah, we're

638
00:30:00,400 --> 00:30:03,720
looking forward to continue building on and iterating on our

639
00:30:03,720 --> 00:30:04,559
prototype scene.

640
00:30:04,480 --> 00:30:08,759
Speaker 1: Because I can see applications for this that go way

641
00:30:08,799 --> 00:30:10,160
beyond sort of vision.

642
00:30:11,359 --> 00:30:13,279
Speaker 8: Oh yeah, I think YR is going to be a

643
00:30:13,279 --> 00:30:17,359
really interesting paradigm shift in the wearable space as a whole.

644
00:30:18,000 --> 00:30:20,599
So definitely like really changes up the game in terms

645
00:30:20,640 --> 00:30:21,519
of like what's possible.

646
00:30:21,559 --> 00:30:24,640
Speaker 1: I think, right, have you had any interest from Apple

647
00:30:24,920 --> 00:30:28,400
or Android, Samsung or any of those guys.

648
00:30:29,319 --> 00:30:32,279
Speaker 8: Not yet, but if they'd like to reach out, definitely listening.

649
00:30:32,359 --> 00:30:36,240
Speaker 2: So I bet fantastic.

650
00:30:36,839 --> 00:30:39,480
Speaker 3: All right, you're all finalists. Congratulations on winning. But I

651
00:30:39,480 --> 00:30:41,319
guess you guys have all been on the ride, right,

652
00:30:41,400 --> 00:30:43,759
like you did you only meet when you got here?

653
00:30:43,880 --> 00:30:44,880
Did you already know each other?

654
00:30:45,039 --> 00:30:46,759
Speaker 4: We only met when we got here, right, So we

655
00:30:46,799 --> 00:30:48,720
had seen each other on video calls before.

656
00:30:51,000 --> 00:30:53,559
Speaker 3: Okay, So yeah, but you've been in contact just digitally

657
00:30:53,599 --> 00:30:55,960
because you're in different parts of the world too, and

658
00:30:56,000 --> 00:30:57,160
then now you've all been in.

659
00:30:57,079 --> 00:30:59,039
Speaker 2: Person for the first time coming here and to build

660
00:31:00,119 --> 00:31:00,559
all right.

661
00:31:00,519 --> 00:31:03,400
Speaker 3: He made it on I mean my experience running contests,

662
00:31:03,440 --> 00:31:05,759
not like exactly like this one. We've done a variety contest, Like,

663
00:31:05,759 --> 00:31:09,279
every finalist gets a lot of visibility. So yeah, winning

664
00:31:09,359 --> 00:31:12,079
is awesome, but it's not the only thing. You made

665
00:31:12,079 --> 00:31:14,400
it through a lot of players, a lot of different

666
00:31:14,400 --> 00:31:17,359
projects and got got got on the front page. Like

667
00:31:18,039 --> 00:31:19,640
to be interesting to see what happens you show an

668
00:31:19,640 --> 00:31:21,400
your projects. They're pretty powerful projects.

669
00:31:21,519 --> 00:31:25,279
Speaker 1: Very very So your first time in Seattle, any of you?

670
00:31:25,559 --> 00:31:27,759
Speaker 2: Yeah? In Seattle, all of us?

671
00:31:27,920 --> 00:31:28,400
Speaker 5: Yeah?

672
00:31:28,440 --> 00:31:28,680
Speaker 2: Wow.

673
00:31:29,079 --> 00:31:30,920
Speaker 1: Did you get to spend any time at Microsoft?

674
00:31:31,000 --> 00:31:32,400
Speaker 2: Did you? Guys? Did you take the campus?

675
00:31:32,480 --> 00:31:36,680
Speaker 4: So we went on Sunday, but then we are also

676
00:31:36,720 --> 00:31:38,839
going to go tomorrow for longer, so we'll be able

677
00:31:38,839 --> 00:31:39,640
to see more stuff.

678
00:31:39,720 --> 00:31:42,000
Speaker 3: Yeah, that's great. Make them take you to the treehouses.

679
00:31:43,319 --> 00:31:46,960
The tree houses are cool. Campus is huge, It's a

680
00:31:47,039 --> 00:31:48,680
lot to see, but definitely do the tree Do.

681
00:31:48,680 --> 00:31:51,200
Speaker 1: You have any appointments with you know, any of the

682
00:31:51,279 --> 00:31:52,160
higher ups that.

683
00:31:52,519 --> 00:31:53,519
Speaker 2: Get a little sat your time.

684
00:31:53,640 --> 00:31:57,720
Speaker 8: Oh yeah, I'm going to be getting a mentoring session

685
00:31:57,759 --> 00:32:01,039
with Saudia, So that would be really cool. Getting ready

686
00:32:01,039 --> 00:32:03,000
to figure out what kind of questions you want to

687
00:32:03,000 --> 00:32:04,960
ask and make sure the best use.

688
00:32:04,839 --> 00:32:06,200
Speaker 2: Of the time. Absolutely.

689
00:32:06,279 --> 00:32:07,680
Speaker 8: Yeah, so it's really exciting.

690
00:32:07,720 --> 00:32:08,799
Speaker 2: Yeah, no, very challenging.

691
00:32:09,000 --> 00:32:10,119
Speaker 3: So what is it like to run a troll your

692
00:32:10,160 --> 00:32:12,480
dollar company, because who knows the answer to that question.

693
00:32:12,359 --> 00:32:15,759
Speaker 2: Except that guy I'd be Chris Farley. You know, hey,

694
00:32:16,200 --> 00:32:22,160
you remember Windows? That was awesome. I don't know what

695
00:32:22,279 --> 00:32:25,079
to ask such a I guess I have questions.

696
00:32:25,799 --> 00:32:30,039
Speaker 1: So where do you guys see your careers?

697
00:32:30,359 --> 00:32:34,160
Speaker 7: So we will also be meeting the Microsoft chief executive

698
00:32:34,200 --> 00:32:38,240
on accessibility. Awesome, Yes, together with tim agers because our

699
00:32:38,359 --> 00:32:43,319
projects are tailor don't ensuring accessibility for people with disability.

700
00:32:43,519 --> 00:32:45,759
Speaker 2: Yeah, that's fantastic.

701
00:32:45,839 --> 00:32:50,480
Speaker 1: So after these projects are done, what are your aspirations

702
00:32:50,519 --> 00:32:54,680
for the future? I mean, do you want to continue

703
00:32:54,720 --> 00:32:58,039
to be innovating in your vertical space? Do you want

704
00:32:58,079 --> 00:33:01,000
to branch out? Do you want to do more general things?

705
00:33:01,200 --> 00:33:02,480
What are your aspirations?

706
00:33:03,240 --> 00:33:03,720
Speaker 2: O data?

707
00:33:04,039 --> 00:33:06,400
Speaker 7: Okay, So I think for me it would be with

708
00:33:06,519 --> 00:33:09,440
the team is still to pass you sign Verse because

709
00:33:09,440 --> 00:33:12,880
it has connected us in a way that we are

710
00:33:12,920 --> 00:33:16,200
also connected to the community because you're solving the problem

711
00:33:16,240 --> 00:33:18,640
that is directly to the community we serve.

712
00:33:18,759 --> 00:33:20,319
Speaker 5: So we're gonna passue sign Verse.

713
00:33:20,359 --> 00:33:23,640
Speaker 7: At the same time with the different I'm still studying,

714
00:33:23,759 --> 00:33:26,759
My teammates are also still studying, So it's gonna be

715
00:33:26,799 --> 00:33:29,640
in a juggling school at the same time passuing.

716
00:33:29,200 --> 00:33:32,599
Speaker 2: Sign verse but you think sign versus your career, Yes, yes.

717
00:33:32,480 --> 00:33:34,640
Speaker 5: It's gonna be part and passel off me.

718
00:33:35,119 --> 00:33:37,559
Speaker 3: It's so cool, lucky to work on the thing that

719
00:33:37,599 --> 00:33:39,200
matters most to you, of course, and then also end

720
00:33:39,240 --> 00:33:40,200
up in a magic cup with it.

721
00:33:40,519 --> 00:33:42,000
Speaker 2: Yeah, that's really cool.

722
00:33:42,079 --> 00:33:44,920
Speaker 4: How about you, Matt Oh Yeah, So I guess the

723
00:33:45,720 --> 00:33:47,640
big thing that has really emerged for me is that

724
00:33:47,680 --> 00:33:50,160
I realized that the only constant really has changed, and

725
00:33:50,200 --> 00:33:53,559
so I can't predict exactly what I'm going to be

726
00:33:53,599 --> 00:33:55,160
doing in the future. We're going to try and continue

727
00:33:55,200 --> 00:33:57,240
to scale hair Match. I've also made a bunch of

728
00:33:57,279 --> 00:34:00,680
other apps as well. Right the core theam that I

729
00:34:00,720 --> 00:34:02,880
try and hold myself too is I I really just

730
00:34:02,920 --> 00:34:05,359
love building products for people. And if I'm movable to

731
00:34:05,359 --> 00:34:07,599
build products for people that other people are not willing

732
00:34:07,640 --> 00:34:10,199
to make, I think that's an impact that I really

733
00:34:10,199 --> 00:34:12,679
believe in. And so, you know, I guess the next

734
00:34:12,679 --> 00:34:14,559
step for me is really I just want a million

735
00:34:14,559 --> 00:34:16,840
people to use something sure that I've made, and then

736
00:34:16,920 --> 00:34:18,199
you know, maybe someday a billion.

737
00:34:18,320 --> 00:34:19,599
Speaker 2: That's great.

738
00:34:19,719 --> 00:34:22,760
Speaker 1: So you're staying in the app development sphere, what you

739
00:34:22,800 --> 00:34:24,320
don't know exactly.

740
00:34:23,840 --> 00:34:25,760
Speaker 2: Where that's going to lead you, but that's where you're

741
00:34:25,800 --> 00:34:26,119
going to be.

742
00:34:26,159 --> 00:34:28,880
Speaker 4: Yeah, currently, app development could could move into something else.

743
00:34:28,880 --> 00:34:31,199
It really depends on whatever people are using as their

744
00:34:31,239 --> 00:34:34,840
primary device. I will either hopefully be that device, or

745
00:34:34,840 --> 00:34:36,400
I will be an application on it.

746
00:34:36,400 --> 00:34:38,199
Speaker 2: It might be an argist device for you know.

747
00:34:38,199 --> 00:34:41,280
Speaker 3: Yeah, exactly the moment. But you know, phone has been

748
00:34:41,280 --> 00:34:43,119
around for a while now. I think we're ready for

749
00:34:43,119 --> 00:34:47,039
a disruption there. Okay, Daniel, what's what's uh? What do

750
00:34:47,079 --> 00:34:47,880
you see in the future.

751
00:34:48,039 --> 00:34:50,039
Speaker 8: Yeah, I mean there's definitely a couple of different pathways

752
00:34:50,079 --> 00:34:50,840
forward from here.

753
00:34:51,039 --> 00:34:51,280
Speaker 2: M H.

754
00:34:52,239 --> 00:34:54,280
Speaker 8: I mean, in particular, a lot of doors have opened

755
00:34:54,320 --> 00:34:56,920
up for us and our team, and we want to

756
00:34:56,920 --> 00:35:00,000
continue building in the same vertical. So as we continue

757
00:35:00,119 --> 00:35:02,880
you to build out our iterations of prototypes and figure

758
00:35:02,920 --> 00:35:05,639
out what the final prototype is going to look like,

759
00:35:05,679 --> 00:35:08,559
we'd like to obviously expand that out to BEATA users.

760
00:35:08,800 --> 00:35:10,440
So that's going to be the next big step for us,

761
00:35:10,880 --> 00:35:14,840
and I think primarily as we continue figuring out I

762
00:35:14,840 --> 00:35:17,679
think I got some great advice from Han Yang from

763
00:35:17,719 --> 00:35:21,400
Microsoft or startups. He said that, you know, it's important

764
00:35:21,480 --> 00:35:24,320
to also be able to understand like you need to

765
00:35:24,320 --> 00:35:26,960
be able to understand like when you need to pivot

766
00:35:27,079 --> 00:35:30,119
and figure out if if this is like something the

767
00:35:30,159 --> 00:35:33,280
consumers actually need yea, rather than this just being like, oh,

768
00:35:33,360 --> 00:35:36,320
now you're being stubborn about it. So, while we're going

769
00:35:36,440 --> 00:35:41,400
to continue trying to solve the problem of accessibility, I

770
00:35:41,440 --> 00:35:43,559
think like the ways in the form factors in that

771
00:35:43,599 --> 00:35:46,800
comes may be shifting, but I think that's like, yeah,

772
00:35:46,840 --> 00:35:49,000
building it with empathy at its core is something that

773
00:35:49,039 --> 00:35:52,880
we're deeply passionate about and deeply connected to. So and

774
00:35:52,920 --> 00:35:54,960
we also just want to remind the world that, you know,

775
00:35:55,039 --> 00:35:57,440
accessibility shouldn't be treated as a feature, it should be

776
00:35:57,440 --> 00:35:59,719
treated as a human right. Yeah, So I think that's

777
00:35:59,760 --> 00:36:02,239
what we also would be want to continue reminding people

778
00:36:02,280 --> 00:36:05,480
as we continue building. So yeah, I think that's what

779
00:36:05,599 --> 00:36:06,800
Argus's future looks like.

780
00:36:06,840 --> 00:36:08,880
Speaker 1: I don't know about you, guys, but I've had this

781
00:36:08,960 --> 00:36:11,760
experience many times and I continue to have it where

782
00:36:11,880 --> 00:36:14,840
a friend that I haven't met or talked to in

783
00:36:14,960 --> 00:36:18,400
years says, hey, let's go out for a beer. I

784
00:36:18,440 --> 00:36:20,639
want to pick your brain about an app idea that

785
00:36:20,719 --> 00:36:24,679
I have, right, And it always ends up here's the

786
00:36:24,800 --> 00:36:28,159
idea and here's what we'll do. I'll design it, you

787
00:36:28,280 --> 00:36:29,880
build it, and we'll split.

788
00:36:29,559 --> 00:36:32,679
Speaker 2: The profits, right, right, So I know that.

789
00:36:33,480 --> 00:36:37,119
Speaker 1: The first question I ask is does this app already exist?

790
00:36:37,440 --> 00:36:39,199
Speaker 2: Like have you done your homework?

791
00:36:39,840 --> 00:36:43,320
Speaker 1: And you know most of the time they haven't, but

792
00:36:43,480 --> 00:36:45,960
they say, yeah, I don't think so. So I'm like,

793
00:36:46,000 --> 00:36:50,119
all right, well before we first of all, here's my rate. Right,

794
00:36:50,400 --> 00:36:52,840
it's going to cost you fifty grand to get a prototype,

795
00:36:53,199 --> 00:36:55,599
and you know, so are you still interested?

796
00:36:55,800 --> 00:36:58,920
Speaker 2: Right, I'm not splitting the profits with you on a spec.

797
00:36:59,119 --> 00:37:01,679
Speaker 1: So do you have people come up to you all

798
00:37:01,719 --> 00:37:04,440
the time and are any has any has that ever happened?

799
00:37:05,079 --> 00:37:05,280
Speaker 2: Yes?

800
00:37:05,880 --> 00:37:09,519
Speaker 4: I do get this quite a lot. Yeah, just generally.

801
00:37:09,840 --> 00:37:14,079
So for me, we really built hair Match in public,

802
00:37:14,159 --> 00:37:16,679
so we would post on Instagram reels you know, oh

803
00:37:16,760 --> 00:37:18,599
this is what we did today building it, this is

804
00:37:18,599 --> 00:37:21,280
what we did that day. I posted videos like that

805
00:37:21,360 --> 00:37:23,360
for one hundred and eighty one day straight.

806
00:37:23,559 --> 00:37:24,079
Speaker 2: Wow.

807
00:37:24,199 --> 00:37:27,159
Speaker 4: And through that I had a couple million impressions. I

808
00:37:27,199 --> 00:37:30,559
would get multiple dms per week of people saying, oh,

809
00:37:30,599 --> 00:37:32,159
I want to work with you, I want to work

810
00:37:32,159 --> 00:37:34,039
with you, or you'd get stuff like oh I want

811
00:37:34,079 --> 00:37:36,800
to work with you for free? Yeah, but really it's

812
00:37:36,840 --> 00:37:39,760
always that they want something more out of you, so

813
00:37:40,119 --> 00:37:42,559
like you will ever like get out of them. And

814
00:37:42,639 --> 00:37:45,119
so there are rare instances where you know, you might

815
00:37:45,599 --> 00:37:48,360
you might connect with somebody online who really will make

816
00:37:48,360 --> 00:37:51,159
an impact long term with you, but it's very rare,

817
00:37:51,360 --> 00:37:52,920
and so there's a lot of noise with that where

818
00:37:52,920 --> 00:37:55,320
you just have to figure out, Okay, does this person

819
00:37:55,400 --> 00:37:57,199
actually want to make a difference or are they just

820
00:37:57,239 --> 00:37:59,719
doing this because they they want to have a like

821
00:37:59,840 --> 00:38:01,480
a by not really putting into work.

822
00:38:02,159 --> 00:38:04,239
Speaker 3: We get the same thing with that. Most of the

823
00:38:04,239 --> 00:38:06,000
time people just don't understand what the work involves.

824
00:38:06,079 --> 00:38:06,239
Speaker 8: Right.

825
00:38:06,320 --> 00:38:08,320
Speaker 3: That's also like you've got the app out the door,

826
00:38:08,320 --> 00:38:10,159
it's in the store, you know, a bunch of things.

827
00:38:10,199 --> 00:38:12,599
A lot of people don't know because it's way harder

828
00:38:12,639 --> 00:38:15,079
than you think it is. It seems simple, but when

829
00:38:15,079 --> 00:38:16,920
you actually go through every one of those steps and

830
00:38:17,079 --> 00:38:19,400
finally see your app appear in the store, you've been

831
00:38:19,400 --> 00:38:23,840
through some stuff. And then it's hard to talk to

832
00:38:23,840 --> 00:38:26,400
someone who's just never done that and they think they

833
00:38:26,400 --> 00:38:27,639
trivialize all those efforts.

834
00:38:27,719 --> 00:38:28,679
Speaker 2: Oh yeah, and we.

835
00:38:28,639 --> 00:38:30,760
Speaker 1: Get the same thing with the podcast. You know, people

836
00:38:31,119 --> 00:38:33,480
want to be guests on our show, and usually they're

837
00:38:33,519 --> 00:38:36,639
just selling something or you know, the stuff that our

838
00:38:37,159 --> 00:38:38,239
listeners don't care.

839
00:38:38,119 --> 00:38:40,159
Speaker 2: About, so we just let a lot of it go.

840
00:38:41,159 --> 00:38:47,239
Speaker 1: Yeah, all right, yeah, so we know what's next for you.

841
00:38:47,599 --> 00:38:50,320
But that's usually a question I asked, but you've kind

842
00:38:50,320 --> 00:38:53,199
of already answered it. But you're you're going to continue

843
00:38:53,239 --> 00:38:54,639
working on this stuff. You're going to take it to

844
00:38:54,679 --> 00:38:55,159
the next job.

845
00:38:55,360 --> 00:38:56,960
Speaker 2: But none of you finished school yet either, like you

846
00:38:57,000 --> 00:38:57,840
still have school.

847
00:38:57,880 --> 00:39:01,480
Speaker 3: I did just gratch congratulation, Thank you, going back for more.

848
00:39:01,519 --> 00:39:02,320
Speaker 2: Are you going to go to work?

849
00:39:02,480 --> 00:39:03,360
Speaker 4: I'm going I'm just.

850
00:39:06,679 --> 00:39:08,719
Speaker 2: But that's you know, the crazy part for me is

851
00:39:08,760 --> 00:39:10,840
thinking you still got classes?

852
00:39:11,119 --> 00:39:15,480
Speaker 8: Yes, yeah, all these other things Tomorrow my team.

853
00:39:15,480 --> 00:39:18,880
Speaker 7: Joint classes at four in the morning before coming.

854
00:39:18,679 --> 00:39:21,960
Speaker 2: To on the other side of the world of course difference.

855
00:39:22,360 --> 00:39:24,440
Speaker 5: Yea, so it's a bit crazy, but.

856
00:39:24,639 --> 00:39:26,559
Speaker 2: Yeah, that's great. That's amazing.

857
00:39:26,920 --> 00:39:30,840
Speaker 1: Well, congratulations to you all again and amazing job. Congratulations

858
00:39:30,920 --> 00:39:34,920
Daniel on August. It's it's you guys are amazing.

859
00:39:35,079 --> 00:39:37,559
Speaker 8: Yeah, of course, it's been incredible honor meeting everyone here.

860
00:39:38,039 --> 00:39:40,719
Speaker 1: All right, Well, thanks for being awesome and thank you

861
00:39:40,760 --> 00:39:42,719
for listening, and we'll talk to you next time on

862
00:39:42,840 --> 00:40:06,159
dot net rocks. Dot net Rocks is brought to you

863
00:40:06,159 --> 00:40:10,199
by Franklin's Net and produced by Pop Studios, a full

864
00:40:10,280 --> 00:40:14,440
service audio, video and post production facility located physically in

865
00:40:14,519 --> 00:40:18,239
New London, Connecticut, and of course in the cloud online

866
00:40:18,280 --> 00:40:20,719
at pwop dot com.

867
00:40:20,880 --> 00:40:23,039
Speaker 6: Visit our website at d O T N E t

868
00:40:23,280 --> 00:40:27,280
R O c k S dot com for RSS feeds, downloads,

869
00:40:27,440 --> 00:40:31,119
mobile apps, comments, and access to the full archives going

870
00:40:31,159 --> 00:40:34,559
back to show number one, recorded in September two thousand

871
00:40:34,559 --> 00:40:37,239
and two. And make sure you check out our sponsors.

872
00:40:37,360 --> 00:40:40,400
They keep us in business. Now go write some code.

873
00:40:40,719 --> 00:40:41,519
Speaker 2: See you next time.

874
00:40:42,400 --> 00:40:48,000
Speaker 8: You got javans at