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Speaker 1: Welcome to Stuff You Should Know from House Stuff Works

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Speaker 1: dot com. Hey, and welcome to the podcast. I'm Josh Clark,

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Speaker 1: and there's Charles W. Chuck Bryant. Jerome's to my right, uh,

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Speaker 1: and this is stuff you should There's probably some people going,

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Speaker 1: did they replaced Jerry with Jerome? Named Jerome? Yeah, that's

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Speaker 1: just the nickname we have for Jerry. Uh. It's a

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Speaker 1: it's an arcane reference to the librarian at the beginning

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Speaker 1: of Ghostbusters whose uncle thought he was sat Jerome. No, really,

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Speaker 1: that's what I've always been referencing. I thought we were

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Speaker 1: on the same page about it. We also call her Jarre's.

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Speaker 1: That's a reference to nothing, right, Jerry, all right, that's

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Speaker 1: a that was a great Jerry Chuck um chuck. Yes,

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Speaker 1: have you ever been on the Internet of Things? M Well,

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Speaker 1: you can't really be on the Internet of things. I

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Speaker 1: don't understand. Explain. Well, the Internet of Things, my friend,

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Speaker 1: is a really just a collection of interconnected devices to

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Speaker 1: make your life simpler and more and less private, pretty

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Speaker 1: much in front with potential complications, but more convenient. Allegedly,

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Speaker 1: there's a lot of people who also say this is

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Speaker 1: all just a bunch of navel gazing in a lot

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Speaker 1: of ways, you know, Like, do we really need these apps?

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Speaker 1: I ran across a a vape for smoking weed as

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Speaker 1: they call it, that has an app. Uh, it's an

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Speaker 1: a vaping system for smoking. It's just a little pipe marijuana.

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Speaker 1: But but it's just a little pipe, and um, it

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Speaker 1: has an app that goes with it that remotely controls

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Speaker 1: it the heat settings and stuff. Oh does it like

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Speaker 1: things like you've smoked one ounce of weed this month? No,

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Speaker 1: which would be pretty intriguing. I guess that would be. Yeah,

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Speaker 1: And that's the point. There's a lot of there's a

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Speaker 1: lot of stuff that you can point to and say,

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Speaker 1: this is pretty neat. This thing feeds my cat while

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Speaker 1: I spend twenties three hours a day at work. Yeah,

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Speaker 1: that's it's nice that I can keep this cat alive

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Speaker 1: that I have no connection with any longer because I'm

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Speaker 1: at work all the time. But this machine feeds it

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Speaker 1: because I can control it remotely with an app. Right.

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Speaker 1: But if you have something tracking how much we do

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Speaker 1: you smoke? You're either smoking far too much or not

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Speaker 1: nearly enough, or you have too much money is another

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Speaker 1: way to put it. Yeah. Uh, you know what my

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Speaker 1: friend Clay. I don't know if you've met Clay, but

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Speaker 1: he told me something. The way, as far as being

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Speaker 1: concerned about security and privacy rights and things, is what

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Speaker 1: they do is they sell it to you first as

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Speaker 1: a convenience and then before you know it, you know,

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Speaker 1: dot dot dot Clay Clay never finished the sence he did,

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Speaker 1: but you know, I don't remember exactly he finished it.

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Speaker 1: Clay said this in Oh, yeah, I think i'd like

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Speaker 1: Clay like way before, like he was probably just talking

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Speaker 1: about geez, I don't even know, like a credit card

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Speaker 1: or something, and and he was just like, just be aware, man.

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Speaker 1: He was like, they sell it to he is a

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Speaker 1: convenience before you know what. Everyone is doing it that way,

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Speaker 1: and it's fraught with complications, was essentially what he was saying. Yeah,

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Speaker 1: I think that's very precient because that's exactly the point

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Speaker 1: that we're at right now. And we'll talk a little more,

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Speaker 1: well a lot more, I'm sure, about security and privacy

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Speaker 1: and all that stuff, but ultimately it's like you said,

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Speaker 1: the Internet of things. Is this A lot of other

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Speaker 1: people call it the internet, Like this is just the

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Speaker 1: next wave of the Internet. This is where the Internet

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Speaker 1: is going but the I guess the best description of

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Speaker 1: it is it's a series of interconnected UM machines devices that,

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Speaker 1: since the environment in a lot of k is, UM

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Speaker 1: can carry out some sort of function. Usually it is

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Speaker 1: a sensing function, and UM can communicate with central servers,

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Speaker 1: usually in the cloud, via the Internet. It's about as

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Speaker 1: simple as that. Like, that's the Internet of things. Yeah,

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Speaker 1: Like if you think, boy, this all sounds weird and

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Speaker 1: I don't use stuff like that, if you have anything

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Speaker 1: that has the word smart in front of it, then

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Speaker 1: you're probably using the Internet of things already, right if

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Speaker 1: you've got a smartphone that to a smart thermostat or

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Speaker 1: a smart smoke detector, or if you wear a exercise tracker.

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Speaker 1: This that's the Internet of things right exactly. And the

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Speaker 1: there's a lot of obvious steps that are right there

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Speaker 1: on the horizon coming after this is like the idea

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Speaker 1: that UM your refrigerator will be able to be like, oh,

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Speaker 1: these guys are almost out of cash. You milk, I'll

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Speaker 1: contact Amazon and have it delivered in two hours. And

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Speaker 1: you're like, I don't drink cash, you milk anymore? Fridge,

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Speaker 1: dupid fridge? How many times do I have to abuse

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Speaker 1: was you. But that's pretty neat. I mean I think

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Speaker 1: a lot of this. It's it's both good and a

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Speaker 1: little creepy. Like that'd be cool. If I showed up

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Speaker 1: at home one day and I was out of milk

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Speaker 1: and it was waiting on my doorstep, I of milk.

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Speaker 1: You wouldn't want waiting on your doorstep. O, cash your milk.

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Speaker 1: It doesn't matter. Oh you don't in refrigerate it no way,

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Speaker 1: because it's not milk, it's cash your juice. Yes, it

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Speaker 1: is what they should call it. Um the I don't

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Speaker 1: disagree with you, right, Like, I'm sure it is pretty neat.

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Speaker 1: It's pretty cool, and in five years it'll be totally

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Speaker 1: second nature to us. Right. But the in my experience,

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Speaker 1: the more mechanized, the more automated, the more convenient and

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Speaker 1: I just made air quotes, life gets, the more difficult

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Speaker 1: it is to keep up with, the less simple it is,

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Speaker 1: and the more horrific it is when something breaks down. Interesting,

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Speaker 1: So in within simplification, you think it becomes more complicated, Yeah,

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Speaker 1: because you you rely on machines that can break, and

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Speaker 1: when they break, you're like, I forgot how to order

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Speaker 1: cash your milk? Where you get that stuff. So you

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Speaker 1: think we're headed for idiocracy, uh, to an extent, But

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Speaker 1: I think it's more. It's more than that. In the

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Speaker 1: short term. I think it's just that it's it's so

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Speaker 1: much easier to walk to a grocery store and buy

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Speaker 1: cash you milk, and walk back home than it is

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Speaker 1: to um ensure that your fridge has all of the

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Speaker 1: updated firmware and make sure that it's it's ordering correctly

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Speaker 1: from Amazon, and to make sure Amazon gets there. And

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Speaker 1: you're just relying on all these other components rather than

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Speaker 1: your own two feet and the idea that the people

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Speaker 1: at the store are gonna have your cash you milk.

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Speaker 1: I see that in a way, but I also disagree

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Speaker 1: in a way. Like, for instance, I have I have

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Speaker 1: a few things in my life that I've set to

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Speaker 1: auto order, like air filters, baby formula, stuff that water

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Speaker 1: my fridge water filter like this stuff gets shipped to

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Speaker 1: me automatically, and it's wonderful because I don't have to

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Speaker 1: think about it at all. So does your fridge order

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Speaker 1: itself or you just put a time around like Amazon,

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Speaker 1: it's on a timer to these you know, not even Amazon,

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Speaker 1: like you know, the fridge filter company. You can just

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Speaker 1: set it to auto deliver like every sixty days. So

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Speaker 1: it's nice because I don't have to think about it.

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Speaker 1: The only thing is missing is the camera or the

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Speaker 1: device itself being hooked up telling the company, Hey, my

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Speaker 1: air filter is over or you know, spent um, which

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Speaker 1: would be pretty awesome to tell you the truth, if

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Speaker 1: your air filter could be like it's actually we're at

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Speaker 1: we need to go ahead and order. However, you know what,

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Speaker 1: that's a little I never really thought about that being

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Speaker 1: a little dicey, because you know, you need a new

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Speaker 1: water filter, trust me, Like really, but what if you

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Speaker 1: could get another month out of this one that you're

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Speaker 1: throwing away and you're ordering early, then they can sell

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Speaker 1: another to a year to you exactly. That's what I

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Speaker 1: assume when you're trusting them, like when they're like you

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Speaker 1: have to change your oil every three thousand miles or

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Speaker 1: or yeah, you need to change your water filter every

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Speaker 1: three months, Like I never changed my oil. Yeah, I forget.

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Speaker 1: That's for chumps. It's a scam. Uh anyway, all right,

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Speaker 1: so that's a little personal overview. The phrase Internet of

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Speaker 1: Things is actually coined, they think, in the late nineties

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Speaker 1: by guy named Kevin Ashton, who worked for PNG. I'll

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Speaker 1: bet he has a T shirt that says I coined

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Speaker 1: the term Internet of things. Well just so he can

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Speaker 1: invite people to punch him in the face or give

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Speaker 1: him a hug, depending on who you are. He worked

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Speaker 1: for Procter and Gamble, and he had a presentation, Uh,

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Speaker 1: they're at work where he said, you know what we

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Speaker 1: should do. We should put um radio frequency ID tags

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Speaker 1: r F I D s on lipstick in the store,

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Speaker 1: so and have that hooked up to a machine where

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Speaker 1: we could automatically send that information and say, hey, the

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Speaker 1: store is running low on lipstick, get a shipment over there.

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Speaker 1: And he coined the term Internet of things supposedly in

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Speaker 1: that meeting, and apparently though at this time, but prior

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Speaker 1: to that, the nineties were a big time for something

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Speaker 1: called ubiquitous computing, which is the basically the predecessor to

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Speaker 1: the idea of the Internet of things, where like computers

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Speaker 1: would just be integrated into our lives totally and completely um,

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Speaker 1: and the Internet of things is in that vein a

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Speaker 1: little similarly. But from stuff I've read, there's a lot

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Speaker 1: of people were like, I didn't quite feel fulfill the

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Speaker 1: promise of ubiquitous computing. This is just kind of like

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Speaker 1: life slightly more convenient now thanks to this, But that

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Speaker 1: that his original idea, Kevin Ashton's, makes total and complete sense,

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Speaker 1: you know, like the you could how many sales do

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Speaker 1: you miss when your lipstick thing is empty until you

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Speaker 1: get you find out it's empty, and then get to

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Speaker 1: refill it, Like if your lipstick the last one can

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Speaker 1: be like, hey, I'm the last one here, you guys,

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Speaker 1: better sense and replacements. It's great. That makes perfect, total sense,

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Speaker 1: and that was ultimately the original basis of the Internet

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Speaker 1: of Things. It's taking um dumb things and making them,

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Speaker 1: like you said, smart, by give then the ability to

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Speaker 1: sense their surroundings and communicate that data to a central

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Speaker 1: server where that's analyzed and then the proper people are alerted. Yeah,

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Speaker 1: and here's the thing that I also never considered is

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Speaker 1: they must have discovered that there was a need for that.

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Speaker 1: And the only need I can imagine would be that

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Speaker 1: the employees were so bad at realizing that they were

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Speaker 1: running out of stock that they would go several days

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Speaker 1: without having lipstick on hand. So they were like, these

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Speaker 1: people can't even do that. Well, what if let's say

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Speaker 1: that you have a person whose job it is to

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Speaker 1: restock lipstick, right, and they go one week and there's

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Speaker 1: like five million tubes of orange, so much orange, they

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Speaker 1: just take some handful and throw throw it away just

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Speaker 1: because they don't want their bosses to feel bad about

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Speaker 1: the orange lipstick that's not being sold. Tubes of orange

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Speaker 1: come back right the next week and it's all gone

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Speaker 1: sold out. And then it's erratic like that, you've got

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Speaker 1: an employee you're sending there, Um that's just hit or miss,

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Speaker 1: whether you just wasted a bunch of gas in the

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Speaker 1: employees time, rather than being alerted like now you guys

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Speaker 1: can come before it's too late. Sure, that's the thing

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Speaker 1: that makes the most sense to me. Kevin Ashton genius.

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Speaker 1: Maybe so I meant to look him up to see

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Speaker 1: what he was doing these days, if he was just

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Speaker 1: wearing the T shirt or he might be a professional

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Speaker 1: term coiner, you know, haven't worked out quite as well

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Speaker 1: to sit back in the fishnet of the future. Yeah,

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Speaker 1: that that was that kind of caught on a little

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Speaker 1: bit early in the two thousand's, but then it just yeah,

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Speaker 1: it went the way the dodo, which was coined by Yeah,

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Speaker 1: that's been around for a while. It's the Dodo. Um,

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Speaker 1: the Dodo died, all right, So let's talk about a

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Speaker 1: little bit what you What you've got essentially is a

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Speaker 1: is a step by step system that many times starts

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Speaker 1: with a smartphone this connected to the internet. Uh. Then

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Speaker 1: you have other pieces of hardware in your home that

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Speaker 1: are also connected to the internet, and there is most

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Speaker 1: likely an app for that hardware on your smartphone, and

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Speaker 1: then that's usually sent out to the cloud. It's not

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Speaker 1: some guy or some ladies sitting in a room in

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Speaker 1: looking at your data. It's eight thousand steps today, just

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Speaker 1: following behind you, counting count No, they're looking at the

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Speaker 1: data from your wearable. Yeah, so just in your guest room.

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Speaker 1: That become a neat I don't know. No, it depends

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Speaker 1: on whether they like get along with you, or if

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Speaker 1: they drink your cash you milk. Well, Todd drinks all

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Speaker 1: the cash you milk. So that's why Todd's not wanted.

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Speaker 1: He's being counterproductive. So then it usually goes to the cloud,

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Speaker 1: which is where we send our data these days, where

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Speaker 1: it's analyzed, and that's a big that's a big part

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Speaker 1: of the Internet things too, Chuck, is the cloud, because

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Speaker 1: that means that you don't have to analyze the day

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Speaker 1: in the little machine, in the little censer. All it

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Speaker 1: has to do is sense stuff and create that data

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Speaker 1: and then send it to the cloud, where you're basically

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Speaker 1: outsourcing all the analysis. That takes a lot more computing power.

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Speaker 1: So that was a big development that there's such a

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Speaker 1: thing as the cloud now. Yeah, that's it kind of

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Speaker 1: puts the smart into it all. Um. And if you're

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Speaker 1: wondering how big it is right now, that depends on

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Speaker 1: who you ask, but some say between fifteen and twenty

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Speaker 1: five billion devices already that are connected, and uh, some

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Speaker 1: people say by twenty it could be anywhere from fifty

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Speaker 1: billion to a trillion devices connected, depending on how much

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Speaker 1: it catches on, you know, how every day it becomes.

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Speaker 1: But it's at it that way for sure. I don't

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Speaker 1: think there's any going back from this. I think they're

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Speaker 1: going to stick a some sort of um computing hardware

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Speaker 1: that taps into the Internet on everything everything, okay, And

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Speaker 1: and it doesn't necessarily have to be a smart thermostat,

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Speaker 1: like they have devices now that you can tag to

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Speaker 1: everyday items to keep up with things. Yeah, or let's

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Speaker 1: say cameras that Let's say you have a security system

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Speaker 1: at your house that you can view from your smartphone

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Speaker 1: from anywhere in the world, and maybe it automatically calls

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Speaker 1: the police. That's the Internet of things, right you know. Yeah,

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Speaker 1: I mean there's a lot of great applications for it.

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Speaker 1: And again, like this is just we're in the the

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Speaker 1: nascent period of of this, like the stuff that's like, wow,

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Speaker 1: holy cow, I have a smart doorbell. That's amazing and

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Speaker 1: it's it's awesome and it works really well. But there's

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Speaker 1: you can't you basically can't apply your imagination to predict

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Speaker 1: what's even gonna be fifteen years into the future as

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Speaker 1: far as the Internet of things goes, Like just the

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Speaker 1: the change in uh, how we deal in inner act

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Speaker 1: with the Internet and our surroundings is it's it's inestimable. Yeah,

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Speaker 1: who wrote this one was a strickling No, this was

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Speaker 1: Bernadette Johnson. Well, she wrote a line in here that

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Speaker 1: I'm just gonna read because it really kind of hit

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Speaker 1: home for me. She said, we've essentially given common physical

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Speaker 1: objects both computing power and senses, and that explains it

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Speaker 1: to a t. Yeah, you did a good job with this.

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Speaker 1: There's a lot of information, Like you could do anything.

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Speaker 1: You can make anything smart that you wanted. You could

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Speaker 1: have a smart can't open her smart tuba lipstick, yeah exactly,

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Speaker 1: or smart tube of toothpaste that you know when you're

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Speaker 1: squeezing the end and your how do you get all

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Speaker 1: the toothpaste out? What's your method? Oh? You know they

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Speaker 1: make a little um remover. I can't remember what it's called.

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Speaker 1: You gotta you got a machine? No, well, yeah, I

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Speaker 1: have a smart toothpaste remover. It's like a little it

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Speaker 1: looks like a remember those candy lips, the wax lips.

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Speaker 1: It looks like those, but it's pocket right now. Okay,

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Speaker 1: so pull us out. Um. If you had a like

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Speaker 1: a slit in the lips, you put the tube of

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Speaker 1: toothpaste in the like the end of it in there,

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Speaker 1: and you just kind of tilted at an angle a

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Speaker 1: little bit and puts pressure and you just slide it

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Speaker 1: along and it pushes it to the to the front. Yeah.

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Speaker 1: I use my toothbrush to do the same thing. How

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Speaker 1: Oh you slide the tooth trust the toothpaste tube on

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Speaker 1: the sink and just you know, use it as a

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Speaker 1: uh flattener. Squeezer, squeezer. Yeah, that's a good one. Interesting. Yeah,

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Speaker 1: I never realized you just hacked your tooth brush. What

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Speaker 1: do you? Uh, what do you pay for something like

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Speaker 1: what you got? Like a dollar something like that? Yeah, yeah, nothing,

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Speaker 1: nothing too much. Well, boy, I guess I got away

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Speaker 1: with it. See now, if if I if there was

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Speaker 1: some sort of computing chip on this thing that calculated

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Speaker 1: how much toothpaste was left and sent that information to

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Speaker 1: my app, that would be a smart toothpaste squeezer, that's it.

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Speaker 1: Or you come home and open the mailbox and there's

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Speaker 1: a new toothpaste and you're like, I didn't even know

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Speaker 1: I was out because I haven't been using my my

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Speaker 1: lippy device. You know, Yeah, I forgot to brush my

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Speaker 1: teeth for three weeks. Let's take a break. Yeah, it's

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Speaker 1: getting a little silly. Yeah, And you go brush your

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Speaker 1: teeth and we'll meet back in here. And how long

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Speaker 1: does it take you to brush your teeth? Like? Seven?

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Speaker 1: Eight seconds? Great, we'll be back here in eight seconds.

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Speaker 1: By the way, seven or eight seconds is not nearly

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Speaker 1: enough time. I just know. That's washing your hands. You're

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Speaker 1: supposed to do the alphabet whie for brushing your teeth.

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Speaker 1: Either for washing your hands, or brushing your teeth, you know,

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Speaker 1: if you're supposed to do like three minutes for brushing

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Speaker 1: your teeth. I've got the we won't name check here

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Speaker 1: and buzz market, but I've got a mechanical toothbrush and

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Speaker 1: it um electric toothbrush and you know it has it

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Speaker 1: beeps and you divide your mouth up into four zones.

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Speaker 1: Oh yeah, I think I have the same top left,

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Speaker 1: top right, bottom left, bottom right, and it just beeps

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Speaker 1: and deep like vibrates. Oh really, it's already vibrating though.

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Speaker 1: How can you tell the difference? It changes its vibration really? Yeah,

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Speaker 1: how weird. I guess maybe it's a pause in the

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Speaker 1: vibration that I think about it. I just assigned to

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Speaker 1: a vibration. Yeah, the same deal though. Yeah. And they

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Speaker 1: again there's smart toothbrushes that can keep up with how

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Speaker 1: much you brush your teeth and that kind of stuff.

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Speaker 1: Are they really uh? Their WiFi connected toothbrushes that connect

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Speaker 1: to an app. You know, my brother in law, the

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Speaker 1: Marine Corps general um he. I used to laugh at

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Speaker 1: him because I was in his bathroom once and opened

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Speaker 1: the drawer and he had a log of his razor

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Speaker 1: and shaving log and like how many times he'd used

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Speaker 1: the raser And it's basically pre smart Internet of Things

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Speaker 1: smart razor, because I'm sure they have those now to

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Speaker 1: alert you, like when you should change your your razor blades.

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Speaker 1: I haven't heard of that one, but I wouldn't be

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Speaker 1: surprised if there was. I just thought it was very funny.

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Speaker 1: I mean, I said a lot about who he was.

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Speaker 1: Oh yeah, you know, and no surprised that he's a

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Speaker 1: Marine Corps general. If he's keeping up with stuff like that,

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Speaker 1: I can I can see him, like, um, I met him.

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Speaker 1: He's a great guy, just sitting at the edge of

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Speaker 1: the bed right before bedtime, petting his cat fifty times,

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Speaker 1: no more, no less, and than putting it in the

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Speaker 1: foot locker at the end of the bed for the night,

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Speaker 1: and like tucking himself in. Yeah, he's the one that

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Speaker 1: trere me on to peeing sitting down too. So oh yeah,

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Speaker 1: a great debt to him. Nice hats off. Is he

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Speaker 1: coming to our DC show again? Uh? No, man, they're

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Speaker 1: transferred over overseas for the first time ever. Are they

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Speaker 1: coming to our UK shows? No, not that overseas. I'm

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Speaker 1: not allowed to say where he's going. Okay, that's cool.

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Speaker 1: I'm with you top secret, huh. I can tell you

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Speaker 1: off the airs. That says, I see what you're saying.

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Speaker 1: It probably doesn't matter. I'm just respecting this privacy by

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Speaker 1: telling everyone that he taught me to be sitting down.

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Speaker 1: You really got starting that national combo? Why don't you

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Speaker 1: I just think it's important, you know, I need to

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Speaker 1: be talking about it. No mistakes is the motto of

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Speaker 1: the tagline. No no drips, no runs, no errors. Nice. Um,

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Speaker 1: did we just take a break and we came back

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Speaker 1: with this garbage? Maybe we should start over again, Jerry,

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Speaker 1: all right, well let's talk about the chech how about that? Yeah,

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Speaker 1: telemetry nothing new, No apparently, this says. And again, the whole,

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Speaker 1: the whole basis of the Internet of Things is what's

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Speaker 1: called machine to machine um communication. Right, you have like

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Speaker 1: your smart lipstick just sitting there sensing that it's the

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Speaker 1: last tube it can sense all day long, and it's

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Speaker 1: still a dumb stick, a lips lipstick. It can't tell you,

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Speaker 1: right unless it can communicate that data to the people

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Speaker 1: who need to know that stuff. And they do that

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Speaker 1: through machine to machine communication. And like you're saying, telemetry

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Speaker 1: was the original version of that, which apparently dates back

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Speaker 1: the Yeah, it's comes from the Greek um tele means

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Speaker 1: remote and uh metro metron means measure. And that's where

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Speaker 1: you're basically in a remote area, you would measure something

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Speaker 1: and then send that uh via back then telephone line, right,

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Speaker 1: like an Arctic station or something set up to watch animals,

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Speaker 1: like deep in the jungle or something. It's like, there's

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Speaker 1: a world the beast, don't we go there's a wild beast.

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Speaker 1: That was early telemetry exactly. And that's essentially just an

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Speaker 1: extension of what we're doing now. Yeah, now it's an

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Speaker 1: extension of from then. Well we've we've built upon that.

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Speaker 1: I mean think like the first dial up stuff that

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Speaker 1: was I would guess probably telemetry, you know, the series

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Speaker 1: of like the and all that. I mean, you're sending

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Speaker 1: signals from one machine to another saying and let me online.

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Speaker 1: Yeah it's your problem. Why are you so slow? Uh?

389
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Speaker 1: And what's allowed the Internet of Things to take root? Um?

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00:22:08,320 --> 00:22:12,320
Speaker 1: Very simply it gets more complicated, but the invention of

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Speaker 1: the Worldwide Web by Mr Tim Burners Lee off Man

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Speaker 1: h Then the ubiquitous nous is that a word? Ubiquity ubiquitousness,

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Speaker 1: ubiquitousness of WiFi. Although yeah, I think ubiquity is yeah,

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Speaker 1: is that right? Yeah, WiFi, I do the porky pig

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Speaker 1: thing where I just skip it. Um, the the widespread

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Speaker 1: nature of WiFi all of a sudden, where um, you

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Speaker 1: don't have to be physically connected to something that really

398
00:22:44,600 --> 00:22:49,080
Speaker 1: advanced things speed. And then like we already said, the cloud, Yeah,

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Speaker 1: I think the cloud is the thing that really kind

400
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Speaker 1: of allows it more than anything else. It's just if

401
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Speaker 1: you had to have that kind of computing power right

402
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Speaker 1: there in the sensor, then it's just would be very limiting.

403
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Speaker 1: You couldn't put it on just anything, and it would

404
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Speaker 1: be a lot more expensive. To these things that they're

405
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Speaker 1: adding to to you know, normal inanimate objects to make

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00:23:10,280 --> 00:23:15,400
Speaker 1: them smart are very cheap to produce. They just need

407
00:23:15,440 --> 00:23:19,600
Speaker 1: a few components. They need computing hardware, they need sensors,

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00:23:19,920 --> 00:23:22,760
Speaker 1: they need communication hardware, and then they need some sort

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Speaker 1: of power source, which you can get that from the

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Speaker 1: machine itself that you plug in, right, So like if

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Speaker 1: it's a smart coffee maker, it can draw power from

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Speaker 1: the the plug that the coffee maker runs off of.

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Speaker 1: And then it needs Internet access, which if you have

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Speaker 1: a smart coffee maker, but you don't have internet at

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Speaker 1: your house. You made a poorer decision in your coffee

416
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Speaker 1: maker purchase. Like pretty much everything comes with internet access

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Speaker 1: at this point right in the Western world. Sure. Uh.

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Speaker 1: The other thing we've kind of been talking about is

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Speaker 1: UM your own devices in your home. But it just

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00:23:55,400 --> 00:23:57,200
Speaker 1: you don't have to be just hooked up to things

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Speaker 1: that you own. You can hook up to other like

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Speaker 1: SIST devices. Like let's say your town has UM devices

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Speaker 1: that monitor traffic conditions, you can tap into that. I

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00:24:08,119 --> 00:24:10,560
Speaker 1: guess that's what WAYS is, right or is that all

425
00:24:10,600 --> 00:24:13,159
Speaker 1: self reported? I looked up and UM, it seems to

426
00:24:13,160 --> 00:24:16,280
Speaker 1: be all self reported. But there's something called like WAYS

427
00:24:16,400 --> 00:24:19,560
Speaker 1: Citizen or something like that, and it it appears to

428
00:24:19,600 --> 00:24:22,440
Speaker 1: be WAYS trying to get smart cities to let them

429
00:24:22,440 --> 00:24:26,080
Speaker 1: tap into their information like traffic cams and stuff like that. Yeah,

430
00:24:26,320 --> 00:24:28,840
Speaker 1: and apparently that's already a thing like if you just

431
00:24:28,920 --> 00:24:31,720
Speaker 1: leave your phone open or like the Bluetooth on, if

432
00:24:31,720 --> 00:24:35,440
Speaker 1: you're driving through a smart city with UM traffic sensors,

433
00:24:35,760 --> 00:24:39,560
Speaker 1: it basically uses your phone's information while you're in traffic

434
00:24:39,880 --> 00:24:42,919
Speaker 1: as real time traffic information because your phone has something

435
00:24:43,000 --> 00:24:46,000
Speaker 1: like a an accelerometer in it, so it knows how

436
00:24:46,040 --> 00:24:49,320
Speaker 1: fast you're moving at any given point. And if it's

437
00:24:49,800 --> 00:24:52,960
Speaker 1: giving that information to just a panel on the side

438
00:24:52,960 --> 00:24:55,160
Speaker 1: of the street, that panel can put all that info

439
00:24:55,240 --> 00:24:58,359
Speaker 1: together and be like oh, peach trees like super backed

440
00:24:58,440 --> 00:25:01,080
Speaker 1: up right now, and then if Ways can get their

441
00:25:01,080 --> 00:25:03,919
Speaker 1: hands on their information, they can send that out to

442
00:25:04,000 --> 00:25:06,719
Speaker 1: their users. But for right now. Ways, as far as

443
00:25:06,760 --> 00:25:10,040
Speaker 1: I know, is is it's a it's a social apps.

444
00:25:10,680 --> 00:25:14,720
Speaker 1: It relies on its users to update conditions, which, by

445
00:25:14,720 --> 00:25:17,480
Speaker 1: the way, Ways I think might be the best app

446
00:25:17,520 --> 00:25:20,080
Speaker 1: of the twenty one century so far. I didn't start

447
00:25:20,200 --> 00:25:22,000
Speaker 1: using it. I had it on my phone for a while,

448
00:25:22,040 --> 00:25:27,400
Speaker 1: but I never used it, but have a little bit recently. Um,

449
00:25:27,840 --> 00:25:29,920
Speaker 1: I don't I don't like interact with it much. I'll

450
00:25:29,960 --> 00:25:32,040
Speaker 1: just set it to tell me where to go, like

451
00:25:32,080 --> 00:25:35,120
Speaker 1: I don't report accidents and things. Does that mean I'm

452
00:25:35,119 --> 00:25:38,840
Speaker 1: a bad user? I mean you're you're you're using like

453
00:25:38,880 --> 00:25:42,119
Speaker 1: the efforts of other people without contributing. I mean the

454
00:25:42,160 --> 00:25:44,280
Speaker 1: point is is for everybody to contribute. It makes it

455
00:25:44,359 --> 00:25:46,480
Speaker 1: more robust. But it's not like you're going to show

456
00:25:46,560 --> 00:25:50,560
Speaker 1: up at your house, you pull over on the highway,

457
00:25:50,600 --> 00:25:55,480
Speaker 1: that's That's probably the one big thing about about ways

458
00:25:55,560 --> 00:25:57,480
Speaker 1: is that like you're not supposed to be using it

459
00:25:57,560 --> 00:25:59,920
Speaker 1: in that situation. If you're the driver, you're supposed to

460
00:25:59,920 --> 00:26:02,040
Speaker 1: be the passengers. But they're also kind of telling you

461
00:26:02,080 --> 00:26:04,240
Speaker 1: to you know well, I mean there's a thing that

462
00:26:04,440 --> 00:26:07,200
Speaker 1: when when it comes up or when you try to start,

463
00:26:07,280 --> 00:26:10,719
Speaker 1: it'll say like are you a passenger? And they just

464
00:26:10,760 --> 00:26:13,800
Speaker 1: assume that you're going to just be truthful about that. Yeah.

465
00:26:13,840 --> 00:26:15,640
Speaker 1: That's like the website to say, like, tell us your

466
00:26:15,640 --> 00:26:18,480
Speaker 1: twenty one by clicking here exactly and then welcome to

467
00:26:18,520 --> 00:26:21,280
Speaker 1: the party. Yeah, and the twenty year olds like, no, shoot,

468
00:26:21,119 --> 00:26:24,080
Speaker 1: you can click on this close two weeks, I'll be

469
00:26:24,080 --> 00:26:28,680
Speaker 1: back in two weeks um. The other thing I was

470
00:26:28,720 --> 00:26:31,400
Speaker 1: wondering too, like what if the lipstick as an example,

471
00:26:32,000 --> 00:26:36,520
Speaker 1: what if they that's open uh to where you can

472
00:26:36,560 --> 00:26:38,159
Speaker 1: look at your app and say like, well, no, this

473
00:26:38,200 --> 00:26:41,000
Speaker 1: store is out of lipstick. That'd be cool, you know. Yeah,

474
00:26:41,160 --> 00:26:42,480
Speaker 1: I said, of having to call and talk to a

475
00:26:42,560 --> 00:26:45,040
Speaker 1: dumb person, I know it's awful wait for them to

476
00:26:45,040 --> 00:26:47,600
Speaker 1: go look with their eyes. Well, but you know, I

477
00:26:47,840 --> 00:26:49,399
Speaker 1: get it though, because now the days you call and

478
00:26:49,440 --> 00:26:50,760
Speaker 1: say hey, I want to check and see if you

479
00:26:50,760 --> 00:26:54,040
Speaker 1: have something stock, and you usually met with all right,

480
00:26:54,920 --> 00:26:58,119
Speaker 1: hold on and not like sure, I'd be happy to

481
00:26:58,119 --> 00:27:02,320
Speaker 1: go check before user. They're like, oh man, I can't

482
00:27:02,359 --> 00:27:05,199
Speaker 1: wait to get outsourced to a robot, right. But then

483
00:27:05,240 --> 00:27:07,040
Speaker 1: you get to the store and they don't have it

484
00:27:07,040 --> 00:27:08,720
Speaker 1: in stock, and you're like, I called and asked someone.

485
00:27:08,760 --> 00:27:12,520
Speaker 1: They're like, who'd you talk to? There's no one by

486
00:27:12,600 --> 00:27:14,880
Speaker 1: their name of works here. Sorry, and he's like got

487
00:27:14,960 --> 00:27:18,840
Speaker 1: covering his name back with his hand. Um and Chuck.

488
00:27:19,960 --> 00:27:22,960
Speaker 1: Speaking of smart cities, you know, traffic info is a

489
00:27:22,960 --> 00:27:28,240
Speaker 1: big one, so was um smart traffic lights, which I

490
00:27:28,240 --> 00:27:32,280
Speaker 1: wish they's been around starting when I was sixteen, because

491
00:27:32,359 --> 00:27:34,320
Speaker 1: there is there are a few things to me that

492
00:27:34,359 --> 00:27:36,359
Speaker 1: are more of a waste of time than sitting in

493
00:27:36,440 --> 00:27:39,520
Speaker 1: a traffic light when there's no traffic going through Decatur

494
00:27:39,640 --> 00:27:42,119
Speaker 1: where near a while it is famous for its lights

495
00:27:42,119 --> 00:27:45,400
Speaker 1: not being timed or trip tripped or whatever, and it's

496
00:27:45,560 --> 00:27:48,720
Speaker 1: there forever it's horrible. Yeah, Decator does have really long

497
00:27:48,800 --> 00:27:51,880
Speaker 1: lights and yeah, they don't even have the We found

498
00:27:51,920 --> 00:27:54,280
Speaker 1: out it's either a metal detector or a weight sensor

499
00:27:54,920 --> 00:27:57,520
Speaker 1: that where there's like the lines that where they obviously

500
00:27:57,520 --> 00:28:01,680
Speaker 1: cut out the the um a hard top in front

501
00:28:01,680 --> 00:28:04,760
Speaker 1: of you in front of a light. If you don't

502
00:28:04,760 --> 00:28:08,480
Speaker 1: even have that, that's a problem. But even those ones

503
00:28:08,520 --> 00:28:11,240
Speaker 1: that have sensors don't always like to it immediately. This

504
00:28:11,280 --> 00:28:13,800
Speaker 1: should be like, it should be a lot smarter than that.

505
00:28:13,840 --> 00:28:16,000
Speaker 1: And that's part of what the revenure engine. You're that

506
00:28:16,040 --> 00:28:19,000
Speaker 1: guy have to stop right? You know Decatur's motto when

507
00:28:19,000 --> 00:28:22,880
Speaker 1: you drive in it says Decator, what's your hurry? Really? No,

508
00:28:23,320 --> 00:28:26,440
Speaker 1: I can kind of see it actually slow down, Decatur.

509
00:28:26,560 --> 00:28:29,440
Speaker 1: What's with all the baby strollers to be another one

510
00:28:30,119 --> 00:28:34,360
Speaker 1: road baby strollers? Why they have this big off road

511
00:28:34,400 --> 00:28:37,320
Speaker 1: like jogging baby stroller with the huge tires and yet

512
00:28:37,359 --> 00:28:41,320
Speaker 1: one of those Yeah, but that's uh, well no, I

513
00:28:41,320 --> 00:28:43,040
Speaker 1: mean that I don't really go off road, But that's

514
00:28:43,040 --> 00:28:47,520
Speaker 1: because the sidewalks in my neighborhood are awful there. They suck.

515
00:28:47,600 --> 00:28:49,440
Speaker 1: Ye is off road might as well be like tree

516
00:28:49,480 --> 00:28:52,160
Speaker 1: roots growing up everywhere, that kind of thing. Yeah, they're like, O,

517
00:28:52,200 --> 00:28:54,120
Speaker 1: this tree's never gonna grow, Let's put a sidewalk up

518
00:28:54,200 --> 00:28:58,960
Speaker 1: right next to it. Um, well, you're talking about smart cities.

519
00:28:59,000 --> 00:29:01,840
Speaker 1: The other cool thing part actually that they could do

520
00:29:01,920 --> 00:29:05,680
Speaker 1: They might actually be doing this is infrastructure, like embedding

521
00:29:05,960 --> 00:29:10,160
Speaker 1: sensors into sidewalks. Well, that's a good example. Like a

522
00:29:10,200 --> 00:29:14,000
Speaker 1: sidewalk that becomes cracked or broken, or bridge that becomes

523
00:29:14,160 --> 00:29:16,880
Speaker 1: weak in one point, they can send the signal and say, hey,

524
00:29:17,320 --> 00:29:20,000
Speaker 1: maybe you should come check out this bridge, and then

525
00:29:20,000 --> 00:29:24,520
Speaker 1: eventually they will send a signal to the robots sidewalk crew,

526
00:29:25,200 --> 00:29:27,760
Speaker 1: who will come out and prepare the sidewalk and everything

527
00:29:27,800 --> 00:29:30,720
Speaker 1: will look perfect all the time thanks to the robots. Right,

528
00:29:30,840 --> 00:29:33,680
Speaker 1: but there's forty robots and like thirty of them are

529
00:29:33,720 --> 00:29:38,360
Speaker 1: just standing around working And where did they learn to

530
00:29:38,400 --> 00:29:42,880
Speaker 1: smoke cigarettes? It seems weird to get so rubbed at

531
00:29:42,880 --> 00:29:45,840
Speaker 1: cigarette breakes because I didn't smoke. Yeah, and I was

532
00:29:45,880 --> 00:29:48,520
Speaker 1: always like, you know, I'm just gonna go stand outside

533
00:29:48,520 --> 00:29:51,400
Speaker 1: and the boss will be like, you can't do that. Yeah,

534
00:29:51,440 --> 00:29:53,760
Speaker 1: I mean you could take a break, but cigarette brakes

535
00:29:53,760 --> 00:29:55,680
Speaker 1: weren't even real breaks. Someone's just like a new smoke,

536
00:29:55,960 --> 00:29:57,520
Speaker 1: but you can never go you know, I'm just gonna

537
00:29:57,520 --> 00:30:00,200
Speaker 1: go stand outside for five minutes. Like you have to

538
00:30:00,200 --> 00:30:03,200
Speaker 1: be killing yourself to make that allowed. Yeah. No, I

539
00:30:03,400 --> 00:30:05,440
Speaker 1: that dawned on me when I was smoker, to like

540
00:30:05,720 --> 00:30:08,160
Speaker 1: that's when I was young and like, uh, you know,

541
00:30:08,280 --> 00:30:12,920
Speaker 1: I was more angry back then about justice. Yeah. So again,

542
00:30:13,000 --> 00:30:15,840
Speaker 1: we can sit here basically all day and talk about

543
00:30:16,000 --> 00:30:18,800
Speaker 1: you know, devices and applications for this kind of thing.

544
00:30:19,240 --> 00:30:22,000
Speaker 1: But there's some hurdles that are coming up that need

545
00:30:22,040 --> 00:30:25,840
Speaker 1: to be addressed, um pretty soon, and we'll talk about

546
00:30:25,880 --> 00:30:45,120
Speaker 1: those right after this. Alright, chuckers, we're back hurdles. So

547
00:30:45,240 --> 00:30:49,760
Speaker 1: right now, there's some immediate hurdles, including the idea that

548
00:30:50,240 --> 00:30:56,560
Speaker 1: a lot of smart technology operates on using totally different languages,

549
00:30:56,640 --> 00:31:00,320
Speaker 1: different protocols, different everything, um, so that if you have

550
00:31:00,360 --> 00:31:03,400
Speaker 1: a house full of different smart gadgets, you probably have

551
00:31:03,480 --> 00:31:05,960
Speaker 1: an app for every single one of them, rather than

552
00:31:06,000 --> 00:31:10,120
Speaker 1: one integrated gap or app. And that's that's a it's

553
00:31:10,120 --> 00:31:12,480
Speaker 1: not a hurdle, like you can have that many apps,

554
00:31:12,880 --> 00:31:16,080
Speaker 1: but the idea of it being seamlessly integrated into just

555
00:31:16,360 --> 00:31:19,800
Speaker 1: one part of your phone would be great. And if

556
00:31:19,840 --> 00:31:21,960
Speaker 1: they could talk to one another without you having to

557
00:31:22,040 --> 00:31:28,200
Speaker 1: control it. You know, like you're, um, you're the light

558
00:31:28,280 --> 00:31:32,800
Speaker 1: sensor on your light shade notices that the sun story

559
00:31:32,840 --> 00:31:35,000
Speaker 1: and you go down, so it opens your blinds a

560
00:31:35,000 --> 00:31:38,680
Speaker 1: little bit, right, and when that happens that you're you're smart.

561
00:31:38,720 --> 00:31:41,160
Speaker 1: Candle up slicer knows that you like a slice the

562
00:31:41,200 --> 00:31:44,120
Speaker 1: candle up before dinner, so it slices up the candle up.

563
00:31:44,120 --> 00:31:46,520
Speaker 1: And they're all talking to one another. So it's not

564
00:31:46,600 --> 00:31:48,880
Speaker 1: like everything's on a timer and things happen at once.

565
00:31:49,160 --> 00:31:52,280
Speaker 1: It's happening because one thing is sensing this and it's

566
00:31:52,320 --> 00:31:54,720
Speaker 1: relaying that information to the other devices in your house

567
00:31:54,800 --> 00:31:58,280
Speaker 1: is as well, that's not happening right now. Yeah, what

568
00:31:58,360 --> 00:32:04,200
Speaker 1: I need is I need between seven and nine am.

569
00:32:04,280 --> 00:32:09,080
Speaker 1: I need my toilet, toilet to flush about every eight minutes. Yeah,

570
00:32:09,600 --> 00:32:13,720
Speaker 1: man alive. It would be my smart house. Or as

571
00:32:13,800 --> 00:32:17,040
Speaker 1: soon as the coffee starts brewing seven minutes later, the

572
00:32:17,040 --> 00:32:20,360
Speaker 1: toilet flushes. That happens. See, coffee is good for that.

573
00:32:20,440 --> 00:32:22,440
Speaker 1: It's great for that. But that's another thing that's coming

574
00:32:22,520 --> 00:32:24,560
Speaker 1: very soon too. Smart toilets. I can tell you, like,

575
00:32:25,480 --> 00:32:27,760
Speaker 1: you've got a lot of Billy Rubens in here, Yeah,

576
00:32:27,880 --> 00:32:31,120
Speaker 1: what's up with that? It'll say, what's up with that?

577
00:32:32,160 --> 00:32:34,640
Speaker 1: Uh So, basically, what you're talking about is systems that

578
00:32:34,640 --> 00:32:36,680
Speaker 1: aren't integrated because it's a bunch of different companies with

579
00:32:36,680 --> 00:32:40,600
Speaker 1: all their own devices. But there are companies trying to

580
00:32:40,640 --> 00:32:46,720
Speaker 1: come together to um join up with open source platforms uh,

581
00:32:46,800 --> 00:32:49,400
Speaker 1: and one of them is created by Qualcom called the

582
00:32:49,480 --> 00:32:54,920
Speaker 1: All Scene Alliance, which is when like it sounds something

583
00:32:54,960 --> 00:32:58,760
Speaker 1: like from a future horror movie, sounds really creepy, the

584
00:32:58,800 --> 00:33:02,200
Speaker 1: All Scene Alliance or a new speak like we might

585
00:33:02,200 --> 00:33:04,160
Speaker 1: as you might as well just say, like, we want

586
00:33:04,160 --> 00:33:07,160
Speaker 1: a camera in every room of your home so we

587
00:33:07,200 --> 00:33:08,600
Speaker 1: can all just talk to each other and make your

588
00:33:08,640 --> 00:33:15,120
Speaker 1: life simpler, just relax, laid back. Apple's Home Kit always

589
00:33:15,320 --> 00:33:18,160
Speaker 1: make it sound cute and not creepy, and it's probably

590
00:33:18,200 --> 00:33:21,400
Speaker 1: creepier than the All Seeing the Lions. Yeah, a bunch

591
00:33:21,400 --> 00:33:24,280
Speaker 1: of people have one. Google, as M, Samsung, there's one

592
00:33:24,320 --> 00:33:27,000
Speaker 1: called everything That's missing a couple of vowels. Wink is

593
00:33:27,040 --> 00:33:29,840
Speaker 1: a big one. Um, it's a big one right now.

594
00:33:30,400 --> 00:33:32,800
Speaker 1: It controls some stuff like I think Phillips lights, and

595
00:33:32,840 --> 00:33:35,240
Speaker 1: it works with Nest maybe or something like that. It

596
00:33:35,280 --> 00:33:37,720
Speaker 1: does like two things, so it's like cutting manage right now.

597
00:33:38,080 --> 00:33:41,200
Speaker 1: But um, as the author of this article, Bernadett Johnson

598
00:33:41,240 --> 00:33:44,640
Speaker 1: puts out, none are all encompassing, which I saw that

599
00:33:44,680 --> 00:33:46,800
Speaker 1: and I was like, Mitch Hedberg would have like that sentence,

600
00:33:47,400 --> 00:33:49,840
Speaker 1: none are all encompassing. Yeah, he said he was like

601
00:33:49,880 --> 00:33:53,320
Speaker 1: trying new words and um rather than like totally he

602
00:33:53,360 --> 00:33:56,000
Speaker 1: said he was eating totally too much. They'd be like

603
00:33:56,400 --> 00:33:59,560
Speaker 1: Mitch to you like s'mores, and'd be like all encompassingly.

604
00:34:02,080 --> 00:34:05,000
Speaker 1: So another hurdle, UM that we are already getting around

605
00:34:05,240 --> 00:34:09,040
Speaker 1: was um. Back in the nineties, we started to realize

606
00:34:09,040 --> 00:34:12,640
Speaker 1: we were running out of IP addresses. The standard IP

607
00:34:12,800 --> 00:34:17,320
Speaker 1: address was the UH, well it still is in some ways,

608
00:34:17,400 --> 00:34:21,480
Speaker 1: the I p V four. Yes, I p V four,

609
00:34:21,920 --> 00:34:24,080
Speaker 1: And in the nineties they got smart. It wasn't like

610
00:34:24,200 --> 00:34:27,360
Speaker 1: the Y two K bug. We're like, oh my gosh,

611
00:34:27,520 --> 00:34:30,520
Speaker 1: things are going to be different in a month. Uh.

612
00:34:30,560 --> 00:34:32,839
Speaker 1: They got on this a while ago and created the

613
00:34:33,000 --> 00:34:40,839
Speaker 1: I p v six and started UH basically created uh

614
00:34:41,040 --> 00:34:46,279
Speaker 1: potentially what's the number, an undisillion number of at three

615
00:34:46,800 --> 00:34:52,520
Speaker 1: forty undisillian addresses that is one with thirty six zeros

616
00:34:52,600 --> 00:34:57,239
Speaker 1: behind it and enough to give uh IP addresses to

617
00:34:57,440 --> 00:35:00,120
Speaker 1: everyone on the planet times ten to the tw the

618
00:35:00,200 --> 00:35:03,879
Speaker 1: eighth power right, So basically they said, we don't want

619
00:35:03,880 --> 00:35:06,600
Speaker 1: to run out ever again. Well, the funny thing is,

620
00:35:06,719 --> 00:35:08,920
Speaker 1: Chuck is in one when they came up with the

621
00:35:09,000 --> 00:35:11,920
Speaker 1: I p v four that came up with four point

622
00:35:11,960 --> 00:35:16,440
Speaker 1: to nine five billion possible addresses. Yeah, they're like, that's

623
00:35:16,760 --> 00:35:20,440
Speaker 1: we're doing. And then what within like thirty thirty years,

624
00:35:20,880 --> 00:35:24,719
Speaker 1: thirty five years, they started really run out. And apparently

625
00:35:24,760 --> 00:35:26,680
Speaker 1: there was a prediction that in two thousand and fifteen

626
00:35:26,719 --> 00:35:28,680
Speaker 1: we were gonna straight up run out of I p

627
00:35:28,880 --> 00:35:33,240
Speaker 1: v four Internet addresses or IP addresses UM, and apparently

628
00:35:33,280 --> 00:35:36,360
Speaker 1: that was a cliff we avoided, obviously, because that's still

629
00:35:36,480 --> 00:35:39,600
Speaker 1: we're still making things that have their own IP addresses.

630
00:35:39,960 --> 00:35:42,239
Speaker 1: I thought they did run out, No, they used They

631
00:35:42,400 --> 00:35:45,720
Speaker 1: used different things to mitigate it, including this network address

632
00:35:45,760 --> 00:35:49,440
Speaker 1: translation really kind of open things up, and that's where

633
00:35:49,480 --> 00:35:53,720
Speaker 1: a server identifies a network as a single IP address

634
00:35:53,960 --> 00:35:57,000
Speaker 1: and then leads it to the local network to decide

635
00:35:57,320 --> 00:35:59,719
Speaker 1: where the information that's supposed to be going to. One

636
00:35:59,760 --> 00:36:02,480
Speaker 1: can you around the network goes see what I'm saying,

637
00:36:02,560 --> 00:36:04,480
Speaker 1: but to the server, to the rest of the Internet,

638
00:36:04,520 --> 00:36:07,560
Speaker 1: that whole network, which can be a ton of computers

639
00:36:08,080 --> 00:36:11,600
Speaker 1: um is just one IP address. So you just reduced

640
00:36:11,680 --> 00:36:14,320
Speaker 1: it by that many computers that are on that local network.

641
00:36:15,000 --> 00:36:17,919
Speaker 1: That was a big one. But then also building um

642
00:36:18,120 --> 00:36:21,399
Speaker 1: new things on the i p v six platform has

643
00:36:21,440 --> 00:36:23,520
Speaker 1: helped mitigate it. A little bit too. So I think

644
00:36:23,560 --> 00:36:25,920
Speaker 1: it's a cliff that we came very close to but

645
00:36:25,960 --> 00:36:28,920
Speaker 1: avoided going over. Well, it doesn't matter now because I

646
00:36:29,040 --> 00:36:31,520
Speaker 1: p V six is the new way forward. It is,

647
00:36:31,520 --> 00:36:34,480
Speaker 1: but there's a lot of stuff still in use that's

648
00:36:34,520 --> 00:36:37,680
Speaker 1: plenty good for the next couple of years that have

649
00:36:37,840 --> 00:36:40,880
Speaker 1: like I p V before p I p V four

650
00:36:40,960 --> 00:36:44,400
Speaker 1: addresses that still need to like they're like me too, Well,

651
00:36:44,480 --> 00:36:47,359
Speaker 1: that doesn't matter. They can they're compatible now. Well, they're

652
00:36:47,360 --> 00:36:49,520
Speaker 1: working to make them compatible. I think they're already a

653
00:36:49,560 --> 00:36:54,040
Speaker 1: long way down that road. They're using both seamlessly pretty much. Um,

654
00:36:54,480 --> 00:36:57,080
Speaker 1: there's a great wire state, well I think so. There's

655
00:36:57,120 --> 00:36:59,960
Speaker 1: a great Wired article about it. Uh and they basically say,

656
00:37:00,000 --> 00:37:04,759
Speaker 1: at first they weren't entirely compatible. Uh, you had to

657
00:37:04,800 --> 00:37:06,760
Speaker 1: have some sort of layer in between to make them

658
00:37:06,800 --> 00:37:10,840
Speaker 1: basically be friends. And um, they're still working on it.

659
00:37:10,840 --> 00:37:14,080
Speaker 1: It's not like finished, but it said so far the

660
00:37:14,080 --> 00:37:17,760
Speaker 1: transition has been pretty seamless. Like you're using you're interacting

661
00:37:17,800 --> 00:37:20,160
Speaker 1: with I PB six right now. You don't even know it. Yeah,

662
00:37:20,200 --> 00:37:22,279
Speaker 1: I would assume like if you have something that was

663
00:37:22,320 --> 00:37:24,480
Speaker 1: made in the last couple of years, it's probably I

664
00:37:24,600 --> 00:37:28,520
Speaker 1: p V six, That's what I would guess, So that's

665
00:37:28,520 --> 00:37:31,880
Speaker 1: pretty neat Undistillian didn't even know there was a thing.

666
00:37:32,000 --> 00:37:33,759
Speaker 1: I didn't either, I had to look it up. It's like,

667
00:37:33,760 --> 00:37:39,160
Speaker 1: what the heck is that a typo on? So I

668
00:37:39,239 --> 00:37:41,200
Speaker 1: don't think we can put it off any longer, Chuck.

669
00:37:41,800 --> 00:37:46,120
Speaker 1: There's a lot of security and privacy concerns that crop

670
00:37:46,200 --> 00:37:49,839
Speaker 1: up from the just the presence of the Internet of things. Right,

671
00:37:50,120 --> 00:37:52,120
Speaker 1: if you have a bunch of sensors in your house

672
00:37:52,200 --> 00:37:56,280
Speaker 1: collecting data and everything from you know how how many

673
00:37:56,320 --> 00:38:00,400
Speaker 1: times you toss and turn in your sleep to um,

674
00:38:00,440 --> 00:38:03,480
Speaker 1: you know how many minutes the toilet needs to be

675
00:38:03,520 --> 00:38:07,520
Speaker 1: flushed in intervals to whether you're moving around your house

676
00:38:07,600 --> 00:38:10,520
Speaker 1: or not, whether you're home. There's a lot of sensors

677
00:38:10,560 --> 00:38:15,840
Speaker 1: in in even now the standard home in the United States, UM,

678
00:38:15,920 --> 00:38:19,640
Speaker 1: that can that are collecting data and there's not a

679
00:38:19,719 --> 00:38:23,759
Speaker 1: lot of regulation on what happens to that data, who

680
00:38:23,760 --> 00:38:26,239
Speaker 1: has access to that data, how safe that data has

681
00:38:26,280 --> 00:38:31,600
Speaker 1: to be, and um, it's just wide open for government surveillance, hackers,

682
00:38:32,440 --> 00:38:36,840
Speaker 1: targeted ads. I mean, if you're paranoid about government stuff,

683
00:38:36,920 --> 00:38:39,880
Speaker 1: then this probably worries you. Hacking as a whole different

684
00:38:40,320 --> 00:38:43,480
Speaker 1: can of worms, like everyone should worry about that. Yeah,

685
00:38:43,640 --> 00:38:46,560
Speaker 1: I feel like everyone should worry about governments aveillance as well,

686
00:38:47,560 --> 00:38:50,960
Speaker 1: big time some people. You know, I think that's bunk.

687
00:38:51,680 --> 00:38:57,920
Speaker 1: But governments aveillance well some people. Yeah, sure they are fools.

688
00:38:58,400 --> 00:39:00,920
Speaker 1: Maybe people are utter fool and the world is full

689
00:39:00,960 --> 00:39:03,879
Speaker 1: of fools. That's crazy to me. How could you not?

690
00:39:05,480 --> 00:39:09,040
Speaker 1: I mean, like, there's there's testimony from the head of

691
00:39:09,080 --> 00:39:12,960
Speaker 1: the n s A. There was Snowdon releasing the prison files. Like,

692
00:39:12,960 --> 00:39:15,520
Speaker 1: how could anyone just say, no, that's not the case

693
00:39:15,560 --> 00:39:17,960
Speaker 1: any Well, what you're saying is everyone should be as

694
00:39:18,040 --> 00:39:20,440
Speaker 1: up on this as I am. And that's that's the

695
00:39:20,480 --> 00:39:25,360
Speaker 1: foolish statement. Your people on the street today, fifty of

696
00:39:25,400 --> 00:39:29,960
Speaker 1: them would say that's just that's just conspiracy stuff that

697
00:39:30,080 --> 00:39:32,280
Speaker 1: I see. I thought you were saying like people knew

698
00:39:32,520 --> 00:39:34,560
Speaker 1: and and they were saying like, no, this isn't a

699
00:39:34,600 --> 00:39:37,120
Speaker 1: real thing. No, I think I got most people probably

700
00:39:37,120 --> 00:39:39,759
Speaker 1: have their head in the sand of totally agree. I

701
00:39:39,760 --> 00:39:44,160
Speaker 1: see what you mean. I still think they're foolish and

702
00:39:44,160 --> 00:39:48,560
Speaker 1: and sad because there's the one thing that would press

703
00:39:49,320 --> 00:39:53,000
Speaker 1: security into the Internet of things. There isn't any right now,

704
00:39:53,160 --> 00:39:55,840
Speaker 1: there's virtually none, but I mean, it's all got to

705
00:39:55,880 --> 00:40:00,319
Speaker 1: be self installed, not by the person but by the company, like, hey,

706
00:40:00,320 --> 00:40:02,000
Speaker 1: we know you're probably worried, so we've done this, this

707
00:40:02,080 --> 00:40:04,760
Speaker 1: and this right. And the way that that will happen

708
00:40:04,920 --> 00:40:07,920
Speaker 1: is if people say, oh, that brand is not very secure,

709
00:40:08,000 --> 00:40:10,400
Speaker 1: I'm going to go to their competitor, which is super secure,

710
00:40:10,760 --> 00:40:13,080
Speaker 1: or if they get sued, sure, and that will cause

711
00:40:13,160 --> 00:40:15,640
Speaker 1: brands to which are self regulating right now as far

712
00:40:15,680 --> 00:40:19,080
Speaker 1: as security goes, to become more secure. But if people

713
00:40:19,160 --> 00:40:21,399
Speaker 1: are unaware of it or just don't think that kind

714
00:40:21,400 --> 00:40:23,480
Speaker 1: of stuff is going on, then there's not gonna be

715
00:40:23,520 --> 00:40:25,400
Speaker 1: any call for that, and they'll be able to continue

716
00:40:25,440 --> 00:40:28,120
Speaker 1: to put sensors, sensors in our home, devices in our

717
00:40:28,120 --> 00:40:31,160
Speaker 1: home that can you drop on us, that can detect

718
00:40:31,239 --> 00:40:34,680
Speaker 1: all sorts of different things about us without any thought

719
00:40:34,760 --> 00:40:37,880
Speaker 1: for security whatsoever. Yeah, well, they have a great example

720
00:40:37,920 --> 00:40:40,120
Speaker 1: in here, and it's not just the Internet of Things

721
00:40:40,239 --> 00:40:44,319
Speaker 1: is already happening. Um with target His dad in two

722
00:40:44,400 --> 00:40:49,520
Speaker 1: thousand twelve got mad because his teenage daughter was getting

723
00:40:50,080 --> 00:40:54,239
Speaker 1: baby ads targeted toward her, and he was like, why

724
00:40:54,239 --> 00:40:56,120
Speaker 1: are you trying to get my teenage daughter to have

725
00:40:56,160 --> 00:40:57,840
Speaker 1: a baby? Why do you keep center to this stuff.

726
00:40:58,840 --> 00:41:02,279
Speaker 1: And he found out she's pregnant and was like he

727
00:41:02,320 --> 00:41:05,920
Speaker 1: actually apologized to them, but I still think like he

728
00:41:06,000 --> 00:41:09,200
Speaker 1: had a beef because what I thought is they were

729
00:41:09,239 --> 00:41:14,560
Speaker 1: just using her search uh information to target ads like,

730
00:41:14,800 --> 00:41:17,680
Speaker 1: which is what goes on all the time. But she wasn't.

731
00:41:17,800 --> 00:41:23,120
Speaker 1: What Target does is they have every customer. Every time

732
00:41:23,160 --> 00:41:25,840
Speaker 1: you shop at Target with a credit card, you have

733
00:41:25,880 --> 00:41:29,360
Speaker 1: a guest I D number that says, oh, here's that

734
00:41:29,360 --> 00:41:32,320
Speaker 1: credit card from Josh Clark again, he's back in my store.

735
00:41:32,719 --> 00:41:37,319
Speaker 1: Here's all the things that he's bought. Uh. So let's

736
00:41:37,360 --> 00:41:42,000
Speaker 1: target ads at him simply by shopping there without using cash,

737
00:41:42,040 --> 00:41:44,560
Speaker 1: which I didn't know that happened. Well, yeah, and it

738
00:41:44,680 --> 00:41:46,200
Speaker 1: used to be you had to sign up for like

739
00:41:46,239 --> 00:41:49,719
Speaker 1: a rewards program like a Kroger cards that I understand.

740
00:41:50,480 --> 00:41:53,480
Speaker 1: This is the same thing without you opting into it.

741
00:41:53,960 --> 00:41:56,200
Speaker 1: You know. Yeah, it's just tracking your credit card. And

742
00:41:56,960 --> 00:41:58,720
Speaker 1: I think it's New York Times. I read one article

743
00:41:58,760 --> 00:42:01,840
Speaker 1: where eventually they quit talking to the New York Times,

744
00:42:01,960 --> 00:42:05,400
Speaker 1: but he got a little information at first, uh, And

745
00:42:05,640 --> 00:42:07,479
Speaker 1: he said he talked to a Target employee that said,

746
00:42:07,680 --> 00:42:11,000
Speaker 1: here's the hypothetical example. Let's say this. There's a girl

747
00:42:11,360 --> 00:42:14,600
Speaker 1: in Atlanta shopping here. She buys a cocoa butter lotion.

748
00:42:15,200 --> 00:42:17,720
Speaker 1: She buys a big purse that could be a diaper bag.

749
00:42:18,320 --> 00:42:23,000
Speaker 1: She buys magnesium supplements, uh, and a bright blue rug.

750
00:42:23,680 --> 00:42:26,520
Speaker 1: They might just surmise, Hey, I bet this lady's pregnant,

751
00:42:26,640 --> 00:42:29,359
Speaker 1: but she's gonna have a boy because she's gonna buy

752
00:42:29,480 --> 00:42:32,200
Speaker 1: that bright blue rug. He's gonna smell like cocoa butter.

753
00:42:32,640 --> 00:42:34,759
Speaker 1: And yeah, and so you know what, I bet you

754
00:42:34,880 --> 00:42:38,640
Speaker 1: she's doing August too, uh, determined to buy what she's purchasing.

755
00:42:38,920 --> 00:42:42,600
Speaker 1: So let's start bombarding her, bombarding her with ads. And

756
00:42:42,640 --> 00:42:44,240
Speaker 1: that just seems a little creepy if you're not opting

757
00:42:44,320 --> 00:42:46,839
Speaker 1: in with I mean, it's a little creepy anyway if

758
00:42:46,840 --> 00:42:51,360
Speaker 1: you get like the shopper's card, but you're you're saying like, sure,

759
00:42:51,480 --> 00:42:53,840
Speaker 1: I'll take a little bit of a discount and exchange

760
00:42:53,840 --> 00:42:57,520
Speaker 1: for you keeping checking my spending habits, or you willingly

761
00:42:57,600 --> 00:43:00,640
Speaker 1: check the box terms and conditions with reading it, Well,

762
00:43:00,719 --> 00:43:03,240
Speaker 1: that's I think terms and conditions should be a whole

763
00:43:03,280 --> 00:43:06,920
Speaker 1: other episode. Man, there's a documentary about that. Yeah, that's

764
00:43:07,200 --> 00:43:11,520
Speaker 1: I think it's called terms and Conditions apply and it's maybe,

765
00:43:11,520 --> 00:43:13,879
Speaker 1: but boy it is creepy. Yeah, you know, I think

766
00:43:15,400 --> 00:43:17,440
Speaker 1: no way, and they make it that long so that

767
00:43:17,560 --> 00:43:20,600
Speaker 1: no one would but it's yeah, And there's actually we've

768
00:43:20,600 --> 00:43:23,480
Speaker 1: read this uh Guardian article. Did you check that out

769
00:43:23,520 --> 00:43:26,520
Speaker 1: that I sent you? So in the Guardian article there's

770
00:43:26,560 --> 00:43:31,799
Speaker 1: a mention of Samsung which had, uh, they had terms

771
00:43:31,800 --> 00:43:33,800
Speaker 1: and conditions for their TV. I think it was in

772
00:43:33,840 --> 00:43:37,120
Speaker 1: two fourteen or fifteen that it came out and it says,

773
00:43:37,200 --> 00:43:40,160
Speaker 1: in the terms and conditions for the TV that you

774
00:43:40,239 --> 00:43:43,560
Speaker 1: bring into your home, please be aware that if your

775
00:43:43,640 --> 00:43:48,040
Speaker 1: spoken words include personal or other sensitive information, that information

776
00:43:48,080 --> 00:43:51,000
Speaker 1: will be among the data captured and transmitted to a

777
00:43:51,080 --> 00:43:55,000
Speaker 1: third party through your use of voice recognition, which means

778
00:43:55,200 --> 00:43:58,880
Speaker 1: your TV is listening to you and transmitting your conversations.

779
00:43:59,200 --> 00:44:01,400
Speaker 1: Are at the very least keywords from your conversations in

780
00:44:01,440 --> 00:44:04,319
Speaker 1: your voice to somebody else who can figure out how

781
00:44:04,360 --> 00:44:06,720
Speaker 1: to target ads, who can put you on a government

782
00:44:06,760 --> 00:44:09,040
Speaker 1: watch list, who can do anything, Which means that you're

783
00:44:09,080 --> 00:44:12,279
Speaker 1: talking normally in your own home and your TV's eavesdropping

784
00:44:12,280 --> 00:44:15,719
Speaker 1: on you. Yeah, and you uh, you think you say

785
00:44:15,760 --> 00:44:18,959
Speaker 1: that's okay, not you, but one says that's fine. Because

786
00:44:19,000 --> 00:44:20,719
Speaker 1: I don't want to touch my remote. I just want

787
00:44:20,760 --> 00:44:25,160
Speaker 1: to say, turn up volume. You know, yeah, I can't

788
00:44:25,160 --> 00:44:29,200
Speaker 1: be bothered to use my finger. Fine, Nicolas Cage movies

789
00:44:29,520 --> 00:44:35,560
Speaker 1: come a bad one. He didn't make bad movies, dude, kidding.

790
00:44:36,840 --> 00:44:39,760
Speaker 1: I used to love Andy Sandberg's Nick Cage on SNL.

791
00:44:40,440 --> 00:44:42,120
Speaker 1: I don't think I ever saw that one. So funny.

792
00:44:42,280 --> 00:44:45,799
Speaker 1: I like Nick Cage's tiny elvis. Oh yeah, that was good.

793
00:44:45,880 --> 00:44:49,799
Speaker 1: He was so bizarre. He's I love Nicholas Cage because

794
00:44:49,840 --> 00:44:53,040
Speaker 1: he's unabashed. He will do some really great smaller movies

795
00:44:53,080 --> 00:44:55,640
Speaker 1: where you're like, man, this dude is an amazing actor,

796
00:44:56,120 --> 00:44:58,640
Speaker 1: and then he'll do the worst garbage you can imagine

797
00:44:59,080 --> 00:45:02,279
Speaker 1: for money, yes, and just it's like, yeah, I wanna

798
00:45:02,560 --> 00:45:04,919
Speaker 1: buy eight new motorcycles. But I think he's a great

799
00:45:04,960 --> 00:45:07,719
Speaker 1: example of what a good director can do with an

800
00:45:07,760 --> 00:45:10,200
Speaker 1: actor if they know what they're doing with them, because

801
00:45:10,239 --> 00:45:13,000
Speaker 1: he does virtually the same thing in all all movies.

802
00:45:13,080 --> 00:45:15,440
Speaker 1: It's just how much more he's doing it and how

803
00:45:15,520 --> 00:45:17,880
Speaker 1: much he's reined in, or how good the script is.

804
00:45:18,120 --> 00:45:21,439
Speaker 1: He comes in, he's like, you want Cage or Age.

805
00:45:22,360 --> 00:45:24,280
Speaker 1: I mean, you're right. He has made some great movies.

806
00:45:24,320 --> 00:45:29,680
Speaker 1: But man, he has made some bad ones. Wow. Oh wait,

807
00:45:29,680 --> 00:45:31,480
Speaker 1: hold on, yeah, let's get back on track. There's one

808
00:45:31,480 --> 00:45:34,040
Speaker 1: other thing too. There's a big debate going on right now, Chuck,

809
00:45:34,080 --> 00:45:38,319
Speaker 1: about whether your phone is eaves dropping on you for

810
00:45:38,520 --> 00:45:41,439
Speaker 1: at the very least targeted ads. Again, if you think

811
00:45:41,480 --> 00:45:46,040
Speaker 1: that your phone is not eaves dropping on you, you're

812
00:45:46,040 --> 00:45:52,319
Speaker 1: you're deluding yourself. Your phone, your TV, your laptop, everything

813
00:45:52,400 --> 00:45:54,680
Speaker 1: around you that is connected to the Internet and has

814
00:45:54,719 --> 00:45:58,480
Speaker 1: a microphone and or a video camera is eaves dropping

815
00:45:58,800 --> 00:46:02,560
Speaker 1: on you. And you don't care, right, I do care,

816
00:46:02,719 --> 00:46:04,600
Speaker 1: But I also have a feeling like what you have

817
00:46:04,600 --> 00:46:07,200
Speaker 1: a smart what can I do? I know you cannot

818
00:46:07,200 --> 00:46:09,120
Speaker 1: have a smart phone. That's part of it. And there's

819
00:46:09,160 --> 00:46:11,120
Speaker 1: there's a that's a big thing. There's a there's a

820
00:46:11,160 --> 00:46:13,279
Speaker 1: trade off. It's like, Okay, I want to be able

821
00:46:13,320 --> 00:46:16,520
Speaker 1: to read Twitter every thirty seconds and just be like

822
00:46:16,760 --> 00:46:22,160
Speaker 1: I was boring and then do it again thirty seconds later. Um,

823
00:46:22,160 --> 00:46:25,240
Speaker 1: And I'm willing to trade that ability for the idea

824
00:46:25,280 --> 00:46:27,200
Speaker 1: that yeah, I'm being listened to in the in the

825
00:46:27,239 --> 00:46:30,120
Speaker 1: gamble that well, I mean, I guess I'm not saying

826
00:46:30,120 --> 00:46:33,480
Speaker 1: anything that important, you know, But I mean, like that's

827
00:46:33,640 --> 00:46:36,799
Speaker 1: that's that's wrong, Like that's wrong. Well, or the people

828
00:46:36,840 --> 00:46:38,319
Speaker 1: say like, well, if you don't have nothing to hide,

829
00:46:38,320 --> 00:46:40,920
Speaker 1: then you know, that's that's a fallacy. That's a logical

830
00:46:40,960 --> 00:46:44,200
Speaker 1: fallacy that a lot of the people collecting that data

831
00:46:44,280 --> 00:46:47,400
Speaker 1: bank on it has it still has a chilling effect

832
00:46:47,400 --> 00:46:49,640
Speaker 1: on on society at large. And if they ever do

833
00:46:49,760 --> 00:46:54,800
Speaker 1: want something on you, brother, they got it. Man, I'm sorry,

834
00:46:54,800 --> 00:47:00,600
Speaker 1: I'm worked up. I was driving. I mean, I'm gonna

835
00:47:00,680 --> 00:47:05,719
Speaker 1: be cool. Then that light didn't change green. It all

836
00:47:05,760 --> 00:47:08,360
Speaker 1: went south. It's a dumb traffic light, all right. So

837
00:47:08,360 --> 00:47:11,319
Speaker 1: we talked a little bit about hackers. Um, and we're

838
00:47:11,320 --> 00:47:14,000
Speaker 1: not just talking about stealing your information or tapping into

839
00:47:14,000 --> 00:47:18,359
Speaker 1: your bank. Um. What about if you if you're if

840
00:47:18,360 --> 00:47:22,040
Speaker 1: your grandmother who is a shut in, has this great

841
00:47:22,160 --> 00:47:25,680
Speaker 1: new smart health system that is hooked up to her

842
00:47:25,719 --> 00:47:30,239
Speaker 1: body and alerts her doctor if something's wrong, she's low

843
00:47:30,280 --> 00:47:32,799
Speaker 1: on meds. Uh. These are all great things, but what

844
00:47:32,840 --> 00:47:36,680
Speaker 1: if someone can hack into that and tell you know,

845
00:47:36,840 --> 00:47:39,640
Speaker 1: and hacking the grandma systems where it doesn't alert, then

846
00:47:39,640 --> 00:47:42,400
Speaker 1: her life is literally at stake. Or what if you

847
00:47:42,440 --> 00:47:45,319
Speaker 1: have and this has happened too, what if you go

848
00:47:45,360 --> 00:47:48,440
Speaker 1: into your baby's room and your baby monitor you hear

849
00:47:48,520 --> 00:47:50,760
Speaker 1: some guy's voice on the end of their end, yelling

850
00:47:50,800 --> 00:47:54,320
Speaker 1: and screaming curse words in Russian. I think that's happened

851
00:47:54,320 --> 00:47:58,280
Speaker 1: to your baby? Is just like, what's this guy's problem? Yeah,

852
00:47:58,320 --> 00:48:03,359
Speaker 1: he usually tells me nice stories. Where's Sergey wants back?

853
00:48:04,600 --> 00:48:08,120
Speaker 1: It's all really creepy, man. You know one of my heroes,

854
00:48:08,239 --> 00:48:12,480
Speaker 1: Charles c Man, who I've never heard of it. He

855
00:48:12,600 --> 00:48:15,239
Speaker 1: also he wrote an article in Vanity Fair called look

856
00:48:15,280 --> 00:48:17,720
Speaker 1: Out He's Got a Phone, and it was all about

857
00:48:17,800 --> 00:48:20,400
Speaker 1: the ways that the Internet of Things could be hacked

858
00:48:20,400 --> 00:48:23,080
Speaker 1: to like basically really threatened somebody. Like if you've got

859
00:48:23,080 --> 00:48:26,600
Speaker 1: a smart pacemaker or smart insulin pump, those things could

860
00:48:26,600 --> 00:48:29,200
Speaker 1: be hacked, you know, and that's a that's a something

861
00:48:29,200 --> 00:48:31,880
Speaker 1: that we're gonna have to deal with, or we're dealing

862
00:48:31,880 --> 00:48:34,360
Speaker 1: with now as it as it stands. Well, one of

863
00:48:34,400 --> 00:48:36,799
Speaker 1: the things that could help and what should be going

864
00:48:36,880 --> 00:48:40,279
Speaker 1: on is these devices at the very least should be

865
00:48:40,320 --> 00:48:44,399
Speaker 1: giving you options on how much data they get their

866
00:48:44,400 --> 00:48:48,680
Speaker 1: hands on, how it's stored, uh, and what the expiration

867
00:48:48,800 --> 00:48:52,280
Speaker 1: date on that is, Like if you quit using this device,

868
00:48:52,480 --> 00:48:56,160
Speaker 1: they still have your information or might still be collecting

869
00:48:56,200 --> 00:49:00,160
Speaker 1: it too. Yeah, absolutely, or uh, when I didn't think

870
00:49:00,160 --> 00:49:04,880
Speaker 1: about when these systems are no longer supported, like you know,

871
00:49:04,920 --> 00:49:07,920
Speaker 1: the company shuts down or something, it needs to have

872
00:49:08,000 --> 00:49:13,360
Speaker 1: a suicide uh measure programmed in to where it like

873
00:49:13,400 --> 00:49:16,680
Speaker 1: it kills itself after it's not supported anymore, and it

874
00:49:16,719 --> 00:49:23,080
Speaker 1: should do it gruesomely. What about economics, Well, as you

875
00:49:23,080 --> 00:49:26,520
Speaker 1: can imagine, if there's hundreds of billions of devices on

876
00:49:26,560 --> 00:49:30,280
Speaker 1: the horizon being connected, it's going to have a pretty

877
00:49:30,280 --> 00:49:34,040
Speaker 1: big economic impact. And they're talking about something on the

878
00:49:34,160 --> 00:49:37,799
Speaker 1: order of what was it four point three trillion dollars

879
00:49:37,840 --> 00:49:44,400
Speaker 1: in value up from nine billion? It seems low to me. Yeah,

880
00:49:44,640 --> 00:49:47,160
Speaker 1: you know, like think about just in cash you milk

881
00:49:47,200 --> 00:49:54,640
Speaker 1: alone trillion dollars. Yeah. Well, it's also costing some companies. Um.

882
00:49:54,760 --> 00:49:58,359
Speaker 1: How so, well, if you've heard of square, yeah, you'd

883
00:49:58,400 --> 00:50:00,200
Speaker 1: probably pay for a lot of things with ware of

884
00:50:00,239 --> 00:50:02,840
Speaker 1: these days. It's a great thing because it allows a

885
00:50:02,880 --> 00:50:06,080
Speaker 1: small business. Previously there was only one way to make

886
00:50:06,080 --> 00:50:10,440
Speaker 1: credit card transactions. You had to get a fairly expensive

887
00:50:10,480 --> 00:50:15,200
Speaker 1: system that uh or a cash register that you know

888
00:50:15,239 --> 00:50:17,279
Speaker 1: made it all possible, and you had to They kind

889
00:50:17,280 --> 00:50:19,080
Speaker 1: of had you over a barrel a little bit. Then

890
00:50:19,080 --> 00:50:20,920
Speaker 1: Square came along and said, now you know what, you

891
00:50:20,960 --> 00:50:23,319
Speaker 1: don't need that stuff. Let's democratize this. Yeah, we have

892
00:50:23,320 --> 00:50:25,680
Speaker 1: the Internet. Now here's some competition. All you need this

893
00:50:25,760 --> 00:50:28,920
Speaker 1: little thing to plug in to your tablet and you

894
00:50:28,960 --> 00:50:31,080
Speaker 1: can swipe it right there in the cab or in

895
00:50:31,120 --> 00:50:35,359
Speaker 1: the place of business and avoid the middlemen and use

896
00:50:35,719 --> 00:50:38,680
Speaker 1: or use PayPal and basically skirt these companies that have

897
00:50:38,760 --> 00:50:42,000
Speaker 1: kind of been ripping you off, um, as a business,

898
00:50:42,000 --> 00:50:44,160
Speaker 1: and then that business passes the cost on to you

899
00:50:44,200 --> 00:50:47,080
Speaker 1: as a customer so in a way, or they're like,

900
00:50:47,160 --> 00:50:49,120
Speaker 1: we don't take that credit card there, fees are too

901
00:50:49,239 --> 00:50:54,520
Speaker 1: high or whatever. Yeah, exactly. Um, but you're right, it's

902
00:50:54,719 --> 00:50:58,239
Speaker 1: the democratization, which is good. I mean it's great. It's

903
00:50:58,280 --> 00:51:01,520
Speaker 1: opened up a lot of a lot. It's taken etsy

904
00:51:01,600 --> 00:51:05,959
Speaker 1: into the real world. That's right, you know. And isn't

905
00:51:05,960 --> 00:51:09,120
Speaker 1: that awesome? Well yeah, Um, there's also worry that it

906
00:51:09,120 --> 00:51:11,280
Speaker 1: could cost jobs. Like you said, what if the lipstick

907
00:51:11,560 --> 00:51:16,040
Speaker 1: um stocker gets fired? Um because he threw away all

908
00:51:16,080 --> 00:51:18,480
Speaker 1: those orange tubes of lipstick. Yeah, he deserves that. He

909
00:51:18,520 --> 00:51:20,920
Speaker 1: didn't do his job good enough. Um. Well that's it.

910
00:51:21,120 --> 00:51:24,160
Speaker 1: Like this, I think this article kind of just kind

911
00:51:24,160 --> 00:51:26,680
Speaker 1: of glosses over that issue, and it's a big issue

912
00:51:26,680 --> 00:51:28,719
Speaker 1: in and of itself. I don't know, but I don't

913
00:51:28,719 --> 00:51:30,520
Speaker 1: think it glossed over it so much as there's a

914
00:51:30,840 --> 00:51:34,480
Speaker 1: school of thought, a very like prominent school of thought

915
00:51:34,560 --> 00:51:38,560
Speaker 1: that says, no, that's not what happens. People get different

916
00:51:38,640 --> 00:51:41,719
Speaker 1: jobs and learn new things. And the one example they

917
00:51:41,840 --> 00:51:44,560
Speaker 1: used in here, which I think makes sense is, um,

918
00:51:44,719 --> 00:51:47,440
Speaker 1: a t M s A TMS popped up everywhere and

919
00:51:47,480 --> 00:51:49,359
Speaker 1: people like, oh, well, there's not gonna be any more

920
00:51:49,360 --> 00:51:51,359
Speaker 1: bank tellers. No one needs to go to a bank,

921
00:51:51,480 --> 00:51:55,239
Speaker 1: And well, yeah, but they actually increased in number, right, Yeah,

922
00:51:55,239 --> 00:51:57,440
Speaker 1: they did, uh. And they think part of that reason

923
00:51:57,520 --> 00:52:02,120
Speaker 1: is because banks could open more branches because they didn't

924
00:52:02,120 --> 00:52:05,160
Speaker 1: need to staff it with fourteen bankers, they just needed

925
00:52:05,200 --> 00:52:08,760
Speaker 1: a couple. Yeah, but more branches meant ultimately more tellers,

926
00:52:09,360 --> 00:52:11,600
Speaker 1: just not in one place. The thing is is, I

927
00:52:11,360 --> 00:52:14,279
Speaker 1: I would be very curious to know whether that was

928
00:52:14,480 --> 00:52:18,440
Speaker 1: an anomaly like that. You know, if if typically in

929
00:52:18,480 --> 00:52:22,239
Speaker 1: an industry that gets replaced by a machine, a good

930
00:52:22,239 --> 00:52:25,600
Speaker 1: one like an a t M works pretty well, Um,

931
00:52:25,640 --> 00:52:28,440
Speaker 1: if they if they actually if jobs actually go up,

932
00:52:28,520 --> 00:52:30,600
Speaker 1: or if that was just like one of the very

933
00:52:30,680 --> 00:52:33,480
Speaker 1: rare examples of it. Well, I think it's it depends

934
00:52:33,520 --> 00:52:36,319
Speaker 1: on your industry. If you're one of the people that

935
00:52:36,400 --> 00:52:38,680
Speaker 1: did that thing, you're like, well, I lost my job

936
00:52:38,719 --> 00:52:42,160
Speaker 1: to a robot. If you build the robots, you're like,

937
00:52:42,239 --> 00:52:45,120
Speaker 1: I got a job because I'm now building robots. Right.

938
00:52:45,120 --> 00:52:47,000
Speaker 1: And again, I think we talked about this, I don't

939
00:52:47,040 --> 00:52:51,960
Speaker 1: remember in what episode, but if you are getting rid

940
00:52:51,960 --> 00:52:54,960
Speaker 1: of an industry and and putting a lot of people

941
00:52:54,960 --> 00:53:00,440
Speaker 1: out of their their employment your careers. Um, I'm not

942
00:53:00,520 --> 00:53:04,200
Speaker 1: against automating stuff like that, but I think part and

943
00:53:04,280 --> 00:53:07,040
Speaker 1: parcel with that is to figure out a way to

944
00:53:07,120 --> 00:53:10,719
Speaker 1: take those out of work people and train them to

945
00:53:10,800 --> 00:53:14,680
Speaker 1: go into new fields or just to to um build

946
00:53:14,680 --> 00:53:17,960
Speaker 1: the stuff that that took over their jobs or whatever.

947
00:53:18,360 --> 00:53:20,640
Speaker 1: But you can't just be like, best of luck, we

948
00:53:20,719 --> 00:53:22,560
Speaker 1: figured out a way for a robot to do what

949
00:53:22,600 --> 00:53:26,279
Speaker 1: you're doing. Go uh, go get hooked on oxyconton and

950
00:53:26,320 --> 00:53:29,520
Speaker 1: go die. Wait, we gotta what was that because we

951
00:53:29,600 --> 00:53:31,960
Speaker 1: got a great listener mail about that. Yeah, that's that's

952
00:53:32,000 --> 00:53:35,160
Speaker 1: what I was talking about. A certain amount of people

953
00:53:35,200 --> 00:53:39,320
Speaker 1: from this industry. It was a Kentucky coal coal industry

954
00:53:39,480 --> 00:53:41,560
Speaker 1: like that were then cross trained to do computer work.

955
00:53:42,600 --> 00:53:44,480
Speaker 1: Wasn't even that long ago, but I can't remember exactly

956
00:53:44,520 --> 00:53:46,200
Speaker 1: what it was, but that's exactly what I mean. Like,

957
00:53:46,280 --> 00:53:48,920
Speaker 1: that's number one. That's a role of government. In my opinion,

958
00:53:49,280 --> 00:53:51,040
Speaker 1: it's one of the it's one of the clear things

959
00:53:51,040 --> 00:53:52,760
Speaker 1: that you can look at and be like, oh, yeah,

960
00:53:52,800 --> 00:53:55,959
Speaker 1: that's what government's for. They're supposed to invest in infrastructure

961
00:53:55,960 --> 00:54:00,279
Speaker 1: and education to to um keep people employed so that

962
00:54:00,400 --> 00:54:05,160
Speaker 1: everyone can earn a decent wage. That's that's my soapbox.

963
00:54:05,200 --> 00:54:07,840
Speaker 1: This has basically been one long soapbox, hasn't it. I

964
00:54:07,880 --> 00:54:10,800
Speaker 1: don't think so. Well, it's the Internet of Things. There's

965
00:54:10,880 --> 00:54:16,280
Speaker 1: literally nothing more to speak about it. Okay, no more. Uh,

966
00:54:16,320 --> 00:54:18,359
Speaker 1: I'm just kidding. And if you want to know more

967
00:54:18,360 --> 00:54:19,879
Speaker 1: about that kind of stuff, you should go check out

968
00:54:19,880 --> 00:54:24,480
Speaker 1: our compadre, John Strickland's podcast, tech Stuff. I guarantee talks

969
00:54:24,520 --> 00:54:28,000
Speaker 1: about the Internet of Things every other week, I would imagine. Um.

970
00:54:28,080 --> 00:54:30,480
Speaker 1: If you want to know more about it. In the meantime,

971
00:54:30,560 --> 00:54:33,520
Speaker 1: you can look up this article on how stuff works

972
00:54:33,520 --> 00:54:36,200
Speaker 1: dot com by typing Internet of Things on the search bar.

973
00:54:36,760 --> 00:54:39,680
Speaker 1: And since I said things, it's time for a listener

974
00:54:39,719 --> 00:54:42,200
Speaker 1: mail No, sir, Oh yeah, we already did listener mail.

975
00:54:42,360 --> 00:54:44,960
Speaker 1: Well that's right, but we have a bonus because now

976
00:54:44,960 --> 00:54:57,600
Speaker 1: we're gonna finish up with part two of Administered. Okay,

977
00:54:58,000 --> 00:55:00,239
Speaker 1: alright again, if you're new to the show, this is

978
00:55:00,239 --> 00:55:02,480
Speaker 1: when we thank people for the nice things they send us.

979
00:55:03,000 --> 00:55:07,080
Speaker 1: And it goes a little something like this. Uh. Peter,

980
00:55:08,440 --> 00:55:12,279
Speaker 1: the organization People for the Ethical Treatment of Animals, they

981
00:55:12,320 --> 00:55:15,880
Speaker 1: send us a cat care package after our cat podcast,

982
00:55:16,600 --> 00:55:19,919
Speaker 1: and I think probably partially because of my soapbox on

983
00:55:20,520 --> 00:55:24,000
Speaker 1: declining and outdoor cats, They're like this guy, yeah, send

984
00:55:24,040 --> 00:55:26,520
Speaker 1: him some cats stuff. Give him some cat stuff stat.

985
00:55:26,800 --> 00:55:28,439
Speaker 1: So thanks for that. Put a cat in a box

986
00:55:28,480 --> 00:55:32,600
Speaker 1: and mail it to him. That's Peter's way. We got

987
00:55:32,640 --> 00:55:38,840
Speaker 1: a postcard from China from Mary Kate Mueller. Thanks a

988
00:55:38,840 --> 00:55:42,280
Speaker 1: lot for that, Mary Kate. We appreciate it. Beautiful. Lisa

989
00:55:42,360 --> 00:55:46,040
Speaker 1: of black Bow Sweets, send us some candied pecans. Dude,

990
00:55:46,680 --> 00:55:49,759
Speaker 1: those are dangerous. Oh yeah, they did not last long

991
00:55:49,760 --> 00:55:52,920
Speaker 1: in the Clarkhouse. No, almost didn't make it. On the

992
00:55:53,000 --> 00:55:59,040
Speaker 1: ride home, I had to be like, I, man, they're good. Uh.

993
00:55:59,080 --> 00:56:02,840
Speaker 1: Aaron Supper send us the bottle of Sonoma County Distilling

994
00:56:02,880 --> 00:56:06,279
Speaker 1: Companies west of Kentucky Bourbon number one. I haven't tried

995
00:56:06,320 --> 00:56:08,080
Speaker 1: it as a good I have not tried it yet either,

996
00:56:08,120 --> 00:56:09,919
Speaker 1: but I'm very much looking forward to it. So thanks

997
00:56:09,920 --> 00:56:14,040
Speaker 1: a lot eron. And speaking of whiskey, uh, thirty three Books,

998
00:56:14,760 --> 00:56:18,280
Speaker 1: Dave from thirty three Books sent us a whiskey tasting

999
00:56:18,320 --> 00:56:23,160
Speaker 1: set which is a little uh I think imported from Ireland.

1000
00:56:23,160 --> 00:56:26,640
Speaker 1: Even a little whiskey tasting glass and book for note

1001
00:56:27,080 --> 00:56:29,800
Speaker 1: and a pen even. Yeah, it's everything you need, everything

1002
00:56:29,840 --> 00:56:33,560
Speaker 1: you need to taste whiskey. So thanks Dave for that. Um,

1003
00:56:33,680 --> 00:56:37,560
Speaker 1: we got a postcard from Caitlin and her fiance from

1004
00:56:37,600 --> 00:56:41,839
Speaker 1: the Mayo Clinic, remember the Helen Branch mentioned and I think,

1005
00:56:41,920 --> 00:56:46,240
Speaker 1: like I think some Unsolved Mysteries. We heard the episode

1006
00:56:46,239 --> 00:56:49,920
Speaker 1: on Unsolved Mysteries long ago. Yeah, but we got a

1007
00:56:49,960 --> 00:56:54,719
Speaker 1: Mayo Clinic postcard. Yeah, why not? Robin and Arran sent

1008
00:56:54,840 --> 00:56:58,960
Speaker 1: us some coasters, some Detroit coasters because they know, even

1009
00:56:58,960 --> 00:57:01,640
Speaker 1: though we poked on a Detroit, we secretly love Detroit. Yeah,

1010
00:57:02,280 --> 00:57:05,319
Speaker 1: thanks Robin and Aaron for that. Mark Singleton over at

1011
00:57:05,400 --> 00:57:09,040
Speaker 1: Rudolph Food sent us a ton of pork crimes and

1012
00:57:09,200 --> 00:57:10,960
Speaker 1: a bunch of great gear to go with it. So

1013
00:57:11,120 --> 00:57:14,600
Speaker 1: we can wear camouflaged hat while we eat our pork

1014
00:57:14,600 --> 00:57:19,680
Speaker 1: crimes right as it should be. Sam Meckling of Jeffson's

1015
00:57:19,760 --> 00:57:24,000
Speaker 1: Millard of Chicago, Yeah, sent us bottles of Milord, And

1016
00:57:24,040 --> 00:57:27,240
Speaker 1: if you've never heard of Milord, it is Chicago's own

1017
00:57:28,040 --> 00:57:35,600
Speaker 1: special uh liqueur, it's something it is. It's um known

1018
00:57:35,760 --> 00:57:44,040
Speaker 1: for its ah harsh aftertaste. Guess it's a good way

1019
00:57:44,040 --> 00:57:45,840
Speaker 1: to put it. Yeah. The great thing about Mallord, though,

1020
00:57:45,880 --> 00:57:49,200
Speaker 1: is they know the deal. They're not like, this is

1021
00:57:49,240 --> 00:57:53,400
Speaker 1: so delicious, You're just never gonna have anything better here.

1022
00:57:53,960 --> 00:57:57,120
Speaker 1: You seem to be having a good day. Let's change that. Um. Yeah,

1023
00:57:57,160 --> 00:57:59,960
Speaker 1: but Mlord actually have gotten this is the best thing

1024
00:58:00,000 --> 00:58:02,720
Speaker 1: you can say. I've gotten used to it, and um,

1025
00:58:02,760 --> 00:58:05,400
Speaker 1: it's an interesting taste. You should try it out. Well.

1026
00:58:05,440 --> 00:58:07,480
Speaker 1: Thanks to the dudes who sent us that, we appreciate.

1027
00:58:07,560 --> 00:58:10,480
Speaker 1: Thank you, Sam Um. Speaking of Booze, I think we

1028
00:58:10,560 --> 00:58:13,120
Speaker 1: mentioned it the other day, but also again, thank you

1029
00:58:13,160 --> 00:58:15,760
Speaker 1: to the people at Spring forty four, which is a

1030
00:58:15,800 --> 00:58:18,960
Speaker 1: Colorado distillery for the old tom Jin that they sent us. Yeah,

1031
00:58:19,000 --> 00:58:22,960
Speaker 1: that was just beautiful yea and actually just ran out Yeah,

1032
00:58:23,120 --> 00:58:28,160
Speaker 1: so just fire just thrown that out there. Badger body

1033
00:58:28,360 --> 00:58:31,440
Speaker 1: body products. Um, a competitive of my own wife. Even.

1034
00:58:31,560 --> 00:58:34,440
Speaker 1: That was Dave who sent us those Dave from Badger

1035
00:58:34,480 --> 00:58:38,000
Speaker 1: Body Products for outam New Hampshire sentence shaving stuff and

1036
00:58:38,080 --> 00:58:41,720
Speaker 1: sunscreen and beared oils and such. That was Dave Morrell.

1037
00:58:41,960 --> 00:58:44,640
Speaker 1: He was the beer guy. Yeah, he worked at Sweetwater. Yeah,

1038
00:58:44,680 --> 00:58:46,840
Speaker 1: so he used to bring a sweetwater. He's a great

1039
00:58:46,880 --> 00:58:48,640
Speaker 1: guy and like all beer guys, he ended up in

1040
00:58:48,640 --> 00:58:53,200
Speaker 1: New Hampshire. Um. But Emily, actually my own wife, who

1041
00:58:53,200 --> 00:58:56,400
Speaker 1: has her natural body product company, went and she usually

1042
00:58:56,400 --> 00:58:59,600
Speaker 1: poop boo's because people say they're natural in art. She went, oh,

1043
00:58:59,720 --> 00:59:02,440
Speaker 1: she's like Badger's good. Actually, yeah, she's like they make

1044
00:59:02,480 --> 00:59:05,040
Speaker 1: good stuff. So I used their beard oil. Now, well

1045
00:59:05,040 --> 00:59:08,400
Speaker 1: thanks a lot, David. I've been using the um the

1046
00:59:08,400 --> 00:59:12,640
Speaker 1: They have a bug repellent sun block that works, smells awesome,

1047
00:59:12,880 --> 00:59:15,040
Speaker 1: smells like citron now that works like a charm. So

1048
00:59:15,280 --> 00:59:18,439
Speaker 1: thank you for that. Pie Lady and Son, oh yeah,

1049
00:59:18,440 --> 00:59:20,840
Speaker 1: out of New York. They sent us Pie Dude and

1050
00:59:20,920 --> 00:59:23,840
Speaker 1: they were just getting started with their shipping program and

1051
00:59:24,000 --> 00:59:26,080
Speaker 1: Pie Lady and Son I have to tell you it

1052
00:59:26,160 --> 00:59:30,000
Speaker 1: worked great. They showed up fresh and delicious, and by

1053
00:59:30,040 --> 00:59:32,680
Speaker 1: delicious I mean really really delicious. Yeah, thanks a lot

1054
00:59:32,720 --> 00:59:34,680
Speaker 1: for that guy. Yeah, so you can support them as well.

1055
00:59:34,760 --> 00:59:38,000
Speaker 1: Pie Lady and Son out of New York City. UM

1056
00:59:38,120 --> 00:59:41,320
Speaker 1: Zach debt More sent us some beautiful cherry, walnut and

1057
00:59:41,400 --> 00:59:44,680
Speaker 1: maplewood boxes. Those are great. Yeah, you got mine on

1058
00:59:44,760 --> 00:59:49,120
Speaker 1: my desk. Matt Dent sent us his He's a he's

1059
00:59:49,120 --> 00:59:51,880
Speaker 1: a comic strip guy who's created the Willie who comics

1060
00:59:52,360 --> 00:59:54,320
Speaker 1: around for twenty five years. I know, I saw that book.

1061
00:59:54,320 --> 00:59:57,000
Speaker 1: It's amazing. Yeah. He sent us a big collection collector's edition.

1062
00:59:57,200 --> 01:00:01,840
Speaker 1: Congratulations Matt and Chuck. Some buddy, Uh made us a

1063
01:00:01,920 --> 01:00:05,000
Speaker 1: longboard that I've got stuff. You should know, a longboard.

1064
01:00:05,000 --> 01:00:08,840
Speaker 1: It's amazing. I don't know. We lost the correspondence. Who

1065
01:00:08,840 --> 01:00:10,720
Speaker 1: don't know who made it? Yes, so if you made it,

1066
01:00:10,880 --> 01:00:12,920
Speaker 1: send that in and we'll reach your name. Yeah, well,

1067
01:00:13,240 --> 01:00:15,520
Speaker 1: thank you, but thank you very much for the longboard.

1068
01:00:15,800 --> 01:00:20,320
Speaker 1: Our buttery butter buddy, Tyler Murphy, he's there, butter, He's

1069
01:00:20,320 --> 01:00:23,920
Speaker 1: our butter, our bread and butter. Uh from South Dakota

1070
01:00:24,000 --> 01:00:28,800
Speaker 1: sinis Um Instant Empire shirts and records, which is really cool.

1071
01:00:29,400 --> 01:00:31,360
Speaker 1: And I just realized that Tyler's emails has been going

1072
01:00:31,400 --> 01:00:34,400
Speaker 1: to my spam folder. I emailed them today because I

1073
01:00:34,440 --> 01:00:37,000
Speaker 1: never look in there and I happened to for something else.

1074
01:00:37,320 --> 01:00:39,200
Speaker 1: That's a bunch of emails from Tyler. I'm like, dude,

1075
01:00:39,320 --> 01:00:45,720
Speaker 1: so sorry. Yeah. Um, Hillary lows Are and Mike Dude.

1076
01:00:45,960 --> 01:00:47,520
Speaker 1: I don't know if Mike's a lows Are or not,

1077
01:00:49,120 --> 01:00:51,600
Speaker 1: but Hillary Mike have been with us for years. They

1078
01:00:51,600 --> 01:00:54,720
Speaker 1: are also from the Dakotas and travel to see our

1079
01:00:54,760 --> 01:00:57,640
Speaker 1: shows in Seattle and they're wonderful people. Hillary is a

1080
01:00:57,680 --> 01:01:00,200
Speaker 1: teacher and they as always sent us to list just

1081
01:01:00,280 --> 01:01:04,360
Speaker 1: delicious flathead like cheese. Dude, that is the best cheese

1082
01:01:04,400 --> 01:01:06,680
Speaker 1: on the planet. I think, who you got better cheese

1083
01:01:06,720 --> 01:01:09,160
Speaker 1: and flathead like? Send it in? Let us be the

1084
01:01:09,240 --> 01:01:14,480
Speaker 1: judge Rachel Stone, who is artist from Australia's East Coast.

1085
01:01:14,560 --> 01:01:17,960
Speaker 1: She has a site called Land of Wonderful dot com.

1086
01:01:18,280 --> 01:01:20,360
Speaker 1: She sent us a lovely handmade cards and letters. So

1087
01:01:20,400 --> 01:01:23,000
Speaker 1: thanks a lot, Rachel. Uh. And then finally you got

1088
01:01:23,040 --> 01:01:26,400
Speaker 1: any more? Nope? Last one. Emily and the crew at

1089
01:01:26,440 --> 01:01:30,040
Speaker 1: Kickapoo Joy Drinks. Oh yeah, they have their Kickapoo Joy

1090
01:01:30,080 --> 01:01:35,280
Speaker 1: Juice and their Atlanta base and they make all natural drinks, juice,

1091
01:01:35,400 --> 01:01:38,920
Speaker 1: juices and sodasn't things Kickapoo Juice and they sent us

1092
01:01:38,960 --> 01:01:41,280
Speaker 1: a box and that was super nice. Thanks a lot, guys.

1093
01:01:41,360 --> 01:01:44,040
Speaker 1: Thanks to everybody who sent us stuff. We appreciate it

1094
01:01:44,160 --> 01:01:47,080
Speaker 1: every time, so thank you. Uh. If you want to

1095
01:01:47,160 --> 01:01:49,320
Speaker 1: hang out with us on social media, you can go

1096
01:01:49,520 --> 01:01:53,400
Speaker 1: to s Y s K podcast on Instagram and Twitter.

1097
01:01:53,720 --> 01:01:55,680
Speaker 1: You can go to Facebook dot com slash stuff you

1098
01:01:55,720 --> 01:01:58,080
Speaker 1: Should Know. You can send us an email to Stuff

1099
01:01:58,120 --> 01:02:00,360
Speaker 1: podcast at how stuff works dot com and is always

1100
01:02:00,360 --> 01:02:02,000
Speaker 1: trying to start a home on the web. Stuff you

1101
01:02:02,000 --> 01:02:10,120
Speaker 1: Should Know dot com. For more on this and thousands

1102
01:02:10,200 --> 01:02:12,520
Speaker 1: of other topics, is it how stuff Works dot com