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Speaker 1: Hello the Internet, and welcome to this episode of the

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Speaker 1: Weekly Zeitgeist. Uh. These are some of our favorite segments

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Speaker 1: from this week, all edited together into one NonStop infotainment

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Speaker 1: laugh stravaganza. Uh yeah, So, without further ado, here is

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Speaker 1: the Weekly Zeitgeist. Chris, what is something from your search

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Speaker 1: history that's revealing about who you are? Well? I feel

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Speaker 1: like I've already used all the good, really good ones,

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Speaker 1: because mostly I do search for just metal detecting videos

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Speaker 1: and magnet fishing videos and mud larking videos. But I

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Speaker 1: do also like to um look up anything about Bigfoot,

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Speaker 1: and once again like I don't really care if it's

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Speaker 1: a percent true, I like to cost you know, I

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Speaker 1: don't care if it's a hundred percent. I think. Okay,

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Speaker 1: now these guys, you guys have actually heard me talked

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Speaker 1: about this in private, because this is something I talked

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Speaker 1: about a lot. Which is this thing that I've found

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Speaker 1: out that like just doing my usual just seeing if

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Speaker 1: there's anything new on the bigfoot news front, like on YouTube,

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Speaker 1: which is best probably for bigfoot news, I find. Um,

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Speaker 1: it turns out that when Mount St. Helen's erupted in

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Speaker 1: night like already it's I've lost your haul. Do you

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Speaker 1: remember Mount St. Helens when it blew up in the

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Speaker 1: It's like the biggest volcanic eruption in the contiguous United States,

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Speaker 1: so apparently. So I'm like watching this thing like Mount St.

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Speaker 1: Helen's big Foot Bodies was like the thing on YouTube,

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Speaker 1: and I was like, what what that sounds good? So

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Speaker 1: I I don't care. Maybe there was only one. It's

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Speaker 1: one of those documentaries made by some nuts, you know,

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Speaker 1: like just like but but once again, like I'm made

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Speaker 1: by a nut. Surprise, it's a Bigfoot. But anyway, I

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Speaker 1: think Bigfoot's real, but it's a separate thing. Wait. Wait,

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Speaker 1: this guy made a little documentary and says, okay, when

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Speaker 1: Mole St. Helen's erupted, the Army Corps of Engineers did

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Speaker 1: what they always do, which they went up and they

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Speaker 1: collected all the animal bodies because they didn't want disease

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Speaker 1: to spread. So they went up there with a helicopter

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Speaker 1: and I guess like a bag or whatever, and they

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Speaker 1: and they filled it up with all the animals that

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Speaker 1: were dead on Mount. Yeah. I was already like when

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Speaker 1: I heard that, I was like kind of like, I

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Speaker 1: was already like, wow, that's kind of I didn't know

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Speaker 1: they did that, because like the first thing I thought was,

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Speaker 1: I didn't know that they did that after every disaster

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Speaker 1: they collect all the dead animals. I had no idea

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Speaker 1: that happened. So I was that did kind of make

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Speaker 1: me think, huh, it's weird and know they do that.

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Speaker 1: They do that, Okay, So anyway, then this is when

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Speaker 1: it got Also I was still I got a little

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Speaker 1: more skeptical when they said they put all the animals

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Speaker 1: in separate stacks at the bottom of the mountain. So

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Speaker 1: like there was a stack of like elk, and like

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Speaker 1: I mean, I just said, they identified by family members.

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Speaker 1: So it's a stack of elk, I guess, and a

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Speaker 1: stack of like deer, and then a stack of like

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Speaker 1: raccoons or whatever, and then they have one stack under

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Speaker 1: a tarp. Relate is true. This is not a joke.

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Speaker 1: This so after the Armory Corps of Engineers gets all

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Speaker 1: the animals like they do and put them on an arc.

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Speaker 1: Just kidding, but that's what it sounds like they're doing

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Speaker 1: underneath the tarp. One of the one of the Somebody's

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Speaker 1: uncle who was in the Army Corps of Engineers talked

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Speaker 1: to this documentary filmmaker and told him that he went

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Speaker 1: and looked under the tarp and that was a pile

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Speaker 1: of bigfoots. And I'm going to assume that was the

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Speaker 1: shortest pile, right, you know what I mean, there probably

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Speaker 1: wasn't that. I mean, I'm sure when they're dead, they're

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Speaker 1: just laying down, and there aren't that many, That's what

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Speaker 1: I'm thinking. Apparently there was a tremendous bigfoot population on

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Speaker 1: Mountain North and so they were very negatively impact by

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Speaker 1: this explosion. Well, I mean, so this is this gets further,

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Speaker 1: so then that somebody else's uncle, always somebody's uncle also

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Speaker 1: looked under the tarp. No, he was working. Not only

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Speaker 1: under the tarp was there a bunch of big foots.

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Speaker 1: But then there was a medical tent where big foots

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Speaker 1: were being or big feet, and no one knows. That's

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Speaker 1: another that's another mystery. And I don't care if only

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Speaker 1: a big foot knows, and you can never get him

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Speaker 1: to say, so, do you guys go by big foots

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Speaker 1: or feet? Yeah, but you need medical care. This this

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Speaker 1: guy who worked for the Army Corps of Engineers, his

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Speaker 1: uncle or whatever, said that he stood by this medical

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Speaker 1: tent and he thought that just these tall guys in

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Speaker 1: big overcoats were going into the medical tent and and

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Speaker 1: and getting bandaged up. But then he noticed one of

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Speaker 1: them had like a huge amount of hair on his

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Speaker 1: wrist or something, and he was like, oh my god,

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Speaker 1: these are big foots they're treating that didn't die in

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Speaker 1: the thing. Then this other guy another person's uncle, uh

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Speaker 1: or stepdad or somebody who worked for the or whatever

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Speaker 1: it was. Check these are all checked out. Whatever uncle

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Speaker 1: all official titles step mentioning that there's somebody's uncle. I

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Speaker 1: don't know, just just to prove that it's not some

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Speaker 1: random guy. So somebody's uncle to do a family it's

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Speaker 1: a legit person. This guy knows people, he's got you know,

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Speaker 1: he's I mean, this guy's yeah. So so anyway, he

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Speaker 1: said that there This is where I really I doubt

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Speaker 1: this is true. But I don't know that there was

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Speaker 1: an army corps of engineers. Guy was talking big foot

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Speaker 1: to a big foot, and that the ideas that government

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Speaker 1: knows about big Foot's they talked to him. I don't

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Speaker 1: know what they talk to them about, because like good

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Speaker 1: tips on child rearing. But if they can talk to him,

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Speaker 1: why didn't the tell him ahead of time to clear

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Speaker 1: out of the mountain if there's gonna I mean, everybody

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Speaker 1: knew that mount was going to blow up that because

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Speaker 1: you know, I don't know. I think of what color

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Speaker 1: the big feet are. If they were white, they maybe

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Speaker 1: would have got the men because they're brown. So you

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Speaker 1: think yetti are treated better. Yes, yeties are treated Yes,

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Speaker 1: you all know about yetis and all. YETI lives matter.

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Speaker 1: Looks they got for that ship. But big feet, I

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Speaker 1: mean misrepresented, you know what I mean. And but whenever

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Speaker 1: I mentioned this story to people, they always are like,

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Speaker 1: first of all, what are you talking about? The Army

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Speaker 1: Corps of Engineers collects all the animals after disasters, and

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Speaker 1: that's when it's everybody else for for them, it falls apart.

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Speaker 1: I'm easy to just I'm easy. I'll skip right over.

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Speaker 1: And then the next thing is I'm like, yeah, maybe

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Speaker 1: they don't do that, but maybe they do. I don't know.

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Speaker 1: I don't work for I don't have an uncle in

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Speaker 1: the Army Corps of Engineers. Like, sure, sir, again, this

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Speaker 1: is a Wendy's so what is your order? Uh? Do

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Speaker 1: they does the Army corp of Engineer go out into

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Speaker 1: the woods when like on an ordinary day and collect

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Speaker 1: any animals just in case. I mean, I'm only agreeing

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Speaker 1: with this because I saw a chernobyl and I know

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Speaker 1: that the dudes are shooting animals, and I'm like that

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Speaker 1: because they wander from sure, but again, you know, maybe

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Speaker 1: they were worried about zombie animals. Anyway, it's just go ahead.

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Speaker 1: I'm just gonna say, like a tie in for this

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Speaker 1: is that that just got me going into deeper stuff

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Speaker 1: that I didn't know about. Like in group of gold prospectors, uh, um,

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Speaker 1: we're attacked by big Foot's with boulders and uh the

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Speaker 1: canyon that they that happened in is called Ape Canyon

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Speaker 1: now for real, for real, that's on the map. So yeah, exactly,

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Speaker 1: So I don't care if it so anyway, I think

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Speaker 1: it's fun. I don't know if it's true. But you know,

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Speaker 1: I've heard that story before about the tarp uh and

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Speaker 1: I don't know, it feels like to me, it feels

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Speaker 1: like to me, they would do probably do something more elaborate.

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Speaker 1: I do feel like that's probably why why not just

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Speaker 1: drag them elsewhere? Like they put the tarps? How securely

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Speaker 1: they put them on there? Why were they letting. Why

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Speaker 1: were they letting people peak? Dude, if you if you

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Speaker 1: need medical, go to the tent. But put hadn't trying

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Speaker 1: to play it low key? What did they think? Apparently

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Speaker 1: there must have been a basketball team when overcoats up

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Speaker 1: on the mountain. Oh wait a minute, they're extremely harry.

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Speaker 1: Oh my god, like that. They it's like, it's not

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Speaker 1: a basketball team. That's big foots and overcoats or big

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Speaker 1: feet total cartoon logic. Yeah, over coats. But it wasn't

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Speaker 1: till the wrist hair is sir, do you have the time?

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Speaker 1: And then he pulls back. Great, you don't even know.

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Speaker 1: You don't even have a watch. Wait a minute, I

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Speaker 1: have to get back to my kids. You know, what

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Speaker 1: is a myth? What's something people think is true you

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Speaker 1: know to be false? Well I don't. I don't know

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Speaker 1: so much of this is a myth. But there's this

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Speaker 1: sense when you nine of ten people, if you ask

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Speaker 1: them what AI is, it's the same people where you

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Speaker 1: ask what gluten is, they're like bread. AI is the

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Speaker 1: same way where I think most people think of robots

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Speaker 1: or like artificially intelligent robots or people who are going

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Speaker 1: to take your job, or robots are gonna take your jobs,

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Speaker 1: whereas AI is really like what allows you to order

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Speaker 1: a Starbucks coffee when you're in your car you're still

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Speaker 1: on the train, Like, I mean, that's not the best example,

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Speaker 1: but like AI is basically part of and parcel everything

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Speaker 1: that we now do on our parcel something that yact

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Speaker 1: you're building. I mean, it is my dream to do

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Speaker 1: a short form web series where I visit all of

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Speaker 1: the post offices of America. Would that be possible? Um? No,

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Speaker 1: not when when they when? When? Development people are like,

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Speaker 1: what's the audience? That would be a hard question, old

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Speaker 1: people who will be dead by the end of this media.

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Speaker 1: That's right, But AI is sort of finding ways to

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Speaker 1: mimic like neuron connections, right. Yeah. Well it's also I

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Speaker 1: mean you're talking about elections. I mean it's it's really

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Speaker 1: an amazing way to make predictions about things using data.

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Speaker 1: So the human mind can only compute so much um

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Speaker 1: and take into account so much data, whereas algorithms can

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Speaker 1: look at vast amounts of data, whether it be pictures, um,

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Speaker 1: well code, I guess uh, and and then make predictions

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Speaker 1: based on those things. Right. We're better at catching like

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Speaker 1: tumors and we're better at catching all things just from

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Speaker 1: like loading scans into or into brain. I think, yeah,

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Speaker 1: that's right, that's right. Yeah, But like you know, they

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Speaker 1: the raptors used AI to help better there's a drastic

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Speaker 1: market open door. The drake raptors. Raptors, they change the

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Speaker 1: I'm so sorry, Toronto, Uh how did they use A?

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Speaker 1: So basically you can use AI to make better again,

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Speaker 1: use for predictions, sake, to look at your shot history

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Speaker 1: and sort of better understand maybe where you should be

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Speaker 1: placing yourself on the court right, where you should be

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Speaker 1: taking more shop. That's right. Yeah, So there's machine learning.

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Speaker 1: I mean, this is machine learning. Machine learning is basically

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Speaker 1: now being harnessed in like every possible field you can imagine.

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Speaker 1: And that's artificial intelligence. It's using computers to to make decisions. Now,

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Speaker 1: sleepwalkers would imply that title would imply that we're unaware

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Speaker 1: of something. I would say, yeah, and what are we

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Speaker 1: unaware of? Well, I think people don't think about their

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Speaker 1: data enough generally. I think maybe in the past two

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Speaker 1: years maybe, I think also post election tampering people started

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Speaker 1: and the Cambridge An scandal, people started thinking more about

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Speaker 1: oh my god, like Facebook has a lot of information

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Speaker 1: about me, and it's being given to other companies that

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Speaker 1: I don't want to have my information. So on the

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Speaker 1: most basic level, not thinking about that enough is sleepwalking.

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Speaker 1: You know the idea now that certain states I mean

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Speaker 1: now there's two only, but certain states are you know,

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Speaker 1: making uh the government use official recognition technology illegal? Is

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Speaker 1: a good? Is a step in the right direction, I

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Speaker 1: personally think, but I don't think people have an appreciation

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Speaker 1: for Okay, if like, is the d m V using

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Speaker 1: my face two make predictions about other things legislatively. So

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Speaker 1: Oz took the phase. My co host took the phrase

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Speaker 1: from a British history book, but I or exact um.

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Speaker 1: But but I think you know. For him also, when

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Speaker 1: he was thinking of the show, which he asked me

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Speaker 1: to do a little later on, but when he was

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Speaker 1: thinking of this show, you know, he he started to

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Speaker 1: think about, well, what are all the things that I

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Speaker 1: don't know that aren't going to be happening in the

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Speaker 1: future but actually are happening right now, And how can

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Speaker 1: I talk to the people who have built these tools,

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Speaker 1: who a lot of them are like sounding the alarm.

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Speaker 1: You know, people who worked at Facebook who were like, yeah,

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Speaker 1: well they trained people created the light button were like

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Speaker 1: trained on the same tools that they used to build casinos, right, yeah, exactly.

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Speaker 1: I mean it's you're you're losing your free will because

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Speaker 1: you know, they are all these ways that companies are

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Speaker 1: manipulating your mind to make the decisions they want you

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Speaker 1: to make. And so you were like reframing it as

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Speaker 1: from a thing about like, well, I don't care what

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Speaker 1: they know about me because I have nothing to hide,

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Speaker 1: nothing to hide, and I'm a boy scout to know,

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Speaker 1: but you are not in control of your own life

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Speaker 1: because they know so much about you, and then they're

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Speaker 1: you know, manipulating your life in the background, and some

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Speaker 1: people don't. I mean, I think a lot of people don't,

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Speaker 1: like you know, I spoke to these parents on the

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Speaker 1: podcast who were like, well, if Amazon knows when I

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Speaker 1: need diapers, that makes my life a lot easier. But

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Speaker 1: it's also are you okay with the fact that Amazon

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Speaker 1: also sells like consumer facial recognition technology to government contracts. Yeah, Like,

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Speaker 1: my face is very hot, so people to see it,

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Speaker 1: don't say hot because they're they're also tracing biometrics, honey,

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Speaker 1: your actual body temperature? What? Well, I mean, I am

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Speaker 1: a vampire and advantage my body is actually very cool. Well,

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Speaker 1: if a hot face actually implies that you are nervous

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Speaker 1: about something and might be carrying a bomb, so yeah,

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Speaker 1: that's what they You never heard the term hot faces? Yeah, okay,

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Speaker 1: what I mean, it's not that cool. Good to know,

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Speaker 1: but yes, all that by like anything that can be

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Speaker 1: scanned and then compared to other things that have been scanned,

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Speaker 1: is using AI to detect or like white list or

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Speaker 1: blacklist someone. So now they're doing it with a laser

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Speaker 1: called jetson where they're actually using your heartbeat as a

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Speaker 1: biometric monitor, so like, oh, that's Jack's heart. Like if

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Speaker 1: they can't see your face because you're compromised, they're starting

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Speaker 1: to be able to read people in other ways. There.

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Speaker 1: When I say there, it's like DARPA is developing these things. God,

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Speaker 1: so if you have like an increased heartbeat, that will

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Speaker 1: be telling of oh that person. Well, just how your

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Speaker 1: heartbeats is a very particular signature that only you have. Yeah,

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Speaker 1: I didn't realize. Apparently it's exactly like your heartbeat is

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Speaker 1: like a fingerprint. Oh really I didn't yet, Yeah, exactly.

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Speaker 1: Mine is like a hip hop Yeah, it's like it's

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Speaker 1: like in an acapella group. Yea, So those are all

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Speaker 1: ai E things. Like when you go on jet Blue

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Speaker 1: and you're using your face to board a plane, you

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Speaker 1: should just ask people who work at jet Blue where's

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Speaker 1: that data going? Is it a hard delete? Are you

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Speaker 1: storing that data and then selling it to the US government?

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Speaker 1: Are you like? And I think the answer a lot

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Speaker 1: of times know. And there are a lot of people

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Speaker 1: who are working in this sort of ethical surveillance, if

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Speaker 1: such a thing as possible, who are really like creating

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Speaker 1: tools to also sound the alarm. Same with people who

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Speaker 1: are developing technology to detect deep fake. They have to

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Speaker 1: create deep fakes in order to detect them. So you know,

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Speaker 1: it's the kind of thing to just keep your eyes

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Speaker 1: open too, don't walk into it. I'm listening to sleep.

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Speaker 1: But if your eyes are open, then they're going to

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Speaker 1: use retinal skirters. They now, of course a kickstarter respectacles.

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Speaker 1: I think they're called something like that. I want to say,

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Speaker 1: I'm be saying it's something spect it's something ecticals and

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Speaker 1: it turns your eye into that like feeds it back

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Speaker 1: right into the camera. Well fund I was thinking well

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Speaker 1: funded kickstarters and then you're like malaria, Like, what in

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Speaker 1: the funk are you creating be from malaria? Crazy? Whatever. Yeah,

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Speaker 1: we also have a search for extraterrestrial intelligence. Also, don't

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Speaker 1: keep your eyes open and just keep your ears open

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Speaker 1: to podcast. They'll tell you. Yeah, that's right. Audio is

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Speaker 1: very safe. Walk around with your eyes closed and podcasts

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Speaker 1: in your ears and you will be safe. Um, no

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Speaker 1: danger there. So basically be a blind hipster for the

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Speaker 1: rest of your life. All right, we're gonna take a

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Speaker 1: quick break. We'll be right back, and we're back real quick.

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Speaker 1: I wonder I want to check in with the crowdfunded

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Speaker 1: wall because so we talked before about how um you know,

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Speaker 1: there there were rumors that the dude who got all

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Speaker 1: the money, Cole Page, was spending it, misspending it. But

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Speaker 1: then in May the wall was built by a privately

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Speaker 1: owned brick company in New Mexico. Uh, and not long

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Speaker 1: after the city served them a season desist because they

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Speaker 1: didn't actually get the proper permissions to build the wall.

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Speaker 1: Oh my god. So he gave the money to a

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Speaker 1: brick contractor somebody who like build stuff out of brick,

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Speaker 1: and they tried to do it over Memorial Day weekend

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Speaker 1: because he was like, then the city won't be paying

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Speaker 1: attention and we'll be able to like do this real

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Speaker 1: quick like And the city learned about it on maye

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Speaker 1: and visited the site where they were told to leave

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Speaker 1: wait by the people building the wall. I'll get the

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Speaker 1: funk out of here, city inspector. Uh. So the landowner,

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Speaker 1: the CEO of the brick company, George could I. He

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Speaker 1: went to city hall the next day to like, you know,

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Speaker 1: half as trying to get all these permits after the

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Speaker 1: wall was already being built, submitted the applications, but they

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Speaker 1: were incomplete, so no permit. Uh and colth Edge went

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Speaker 1: on Twitter and straight up bragged that the government wouldn't

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Speaker 1: notice them building the wall because it was a long weekend.

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Speaker 1: I'm sorry, this isn't like graffiti on your junior high

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Speaker 1: said at we build the wall planned for a battle.

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Speaker 1: That's why we finished the wall in three days when

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Speaker 1: the corrupt city was partying over the holiday. The only

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Speaker 1: thing left is to pave the road for border patrol. Boom.

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Speaker 1: Do they ever talked about how long the stretch this

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Speaker 1: uh wall was? So we'll get to that. But so

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Speaker 1: the break executive is facing up to ninety days in

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Speaker 1: jail for building a wall with no permit. Uh, and

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Speaker 1: building part of the wall has led people entering the

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Speaker 1: US and other parts of town were simply walking around

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Speaker 1: the wall because it's not very it's not very long. Uh.

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Speaker 1: It was purely symbolic. And some of the ways people

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Speaker 1: have chosen to enter now are you know, more dangerous

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Speaker 1: for them, which I'm sure he doesn't care about, or

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Speaker 1: you know, the rich people of this town who probably

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Speaker 1: were on board with some of these decisions, didn't care about.

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Speaker 1: But they're also causing people to cross in uh, residential neighborhoods,

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Speaker 1: So people are having to cross into uh, you know

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Speaker 1: where people live instead of this industrial part of town

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Speaker 1: where they built the wall. And they built some of

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Speaker 1: their wall on federal land and in doing so cut

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Speaker 1: off access to waterways and a public monument. Uh. So

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Speaker 1: the International Boundary and Water Commission forced the wall people,

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Speaker 1: the wall builders to prop open a gate in the

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Speaker 1: wall during business hours so they can do their jobs.

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Speaker 1: So on top of everything else, there's literally an open

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Speaker 1: door in their wall. Oh god, it's a man like

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Speaker 1: part of me. Uh. Fuck. How much money was this?

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Speaker 1: It was twenty million, twenty millions, but their goal was

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Speaker 1: one billion. They got to twenty million, which is a lot.

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Speaker 1: Bunch people took their money back when they were like, oh,

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Speaker 1: there aren't that many people like us. Okay, took their

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Speaker 1: money back, and he used the remainder to build a

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Speaker 1: small brick wall that UH has done nothing but embarrass him,

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Speaker 1: get his business partner into legal problems, and we build

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Speaker 1: the wall, which incorporated as a nonprofit in Florida, is

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Speaker 1: currently being investigated by the state Suite UH, and he's

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Speaker 1: currently trying to get more funding. He did a telethon

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Speaker 1: to his money to do the same thing in other cities.

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Speaker 1: UH recently build a Fall and Steve Bannon was there.

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Speaker 1: That's where Steve Bannon's at. That's that's the circles he's

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Speaker 1: traveling in. He's got to work, you know, yeah right,

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Speaker 1: uh wow, he's just at these fundraisers just like, hey,

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Speaker 1: remember me, the guy who thought this up? Then the

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Speaker 1: president literally exactly Uh. Even a year ago, people were like,

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Speaker 1: oh bannons over in Europe, he's like creating the populist

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Speaker 1: wave over there, and now yeah, shit is you know,

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Speaker 1: but there's still people who are definitely receptive for that message. Though,

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Speaker 1: as you'll see it's because there's a lot of money. Yeah,

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Speaker 1: I can't imagine. Oh man, some of these people who

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Speaker 1: just who don't know any better and really think like

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Speaker 1: they're hard earned money is going to something like that.

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Speaker 1: That's what's crazy about it is just like seventy it's

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Speaker 1: some statistic like seventy percent of Americans can't cover a

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Speaker 1: thousand dollar emergency. And when you think about that, I

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Speaker 1: imagine that the majority of these people that contributed to

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Speaker 1: this twenty million dollars are in that pool, possibly or

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Speaker 1: boomers who are just are so scared that you're like

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Speaker 1: facing real financial peril and you're going to contribute to

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Speaker 1: some bullshit that isn't even sanctioned. Yeah right, it's this

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Speaker 1: is it's anarchy outside. But I needed my racist snake oil. Right,

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Speaker 1: it cures, it cures immigrants, an our key out there.

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Speaker 1: It's yeah, regulations, it's not a cool thing to be

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Speaker 1: the guy who's pro regulations, but they're helpful. I like

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Speaker 1: how they even still like with his tweets like, yeah, man,

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Speaker 1: we're getting doing it for the government finds out it's

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Speaker 1: like the best solution. If we were really serious about

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Speaker 1: your problem, you want the government on board. I love this.

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Speaker 1: I love regulations. Like I'm from Haiti, I was born

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Speaker 1: in Haiti, and there's no rules, there's no laws. You know,

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Speaker 1: my husband's from India. People bribe the police on a

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Speaker 1: day to day basis, Like the police take bribes from people,

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Speaker 1: like small vendors on the streets. Street vendors have to

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Speaker 1: pay the police, Like like that's just an everyday occurrence

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Speaker 1: in India. I love laws, I love rules and order,

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Speaker 1: and I'm glad that we have them here. Yeah, sometimes

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Speaker 1: they work. Takes a permit to build your business monument

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Speaker 1: so that you don't block water weights, even fails as

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Speaker 1: a fucking fence because you're like, here's a or right

404
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Speaker 1: to walk the funk through right and you must must

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Speaker 1: leave it. Also, we have refrigerated water there, there's spa

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Speaker 1: water all right. At that look at a law of work,

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Speaker 1: Like if there wasn't a lots that means that random

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Speaker 1: people could just build brick walls along their backyards along

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Speaker 1: the southern border. Yeah, but I mean, look, we still

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Speaker 1: have laws, and we're still doing this fucking, terrible, awful

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Speaker 1: ship to innocent people. So it's some laws work, Yeah,

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Speaker 1: they work for some of us, that's right. Just not us.

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Speaker 1: All right, guys, let's talk about weather reporting. It's about

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Speaker 1: time we talked about this. Uh. There was an interesting

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Speaker 1: article in The New Yorker last week about how weather

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Speaker 1: forecasting keeps getting better, and it's basically making the point

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Speaker 1: that all it's important to all different industries, so there's

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Speaker 1: a reason, uh that everybody would want to keep making

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Speaker 1: it better. Um, but there's also it's like such a

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Speaker 1: common plex like math equation like that. Basically the message

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Speaker 1: I got from this article is that, like landing a

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Speaker 1: spacecraft on Mars requires dealing with hundreds of mathematical variables,

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Speaker 1: making a global atmospheric model requires hundreds of thousands. I

424
00:26:16,280 --> 00:26:19,880
Speaker 1: think that's like the key quote from the article. So

425
00:26:19,920 --> 00:26:25,399
Speaker 1: basically we're still like it is the furthest frontier, like

426
00:26:25,440 --> 00:26:29,160
Speaker 1: being able to model out weather patterns and it it

427
00:26:29,240 --> 00:26:31,800
Speaker 1: is we are getting better at it, like we're but

428
00:26:31,920 --> 00:26:36,760
Speaker 1: it's over the course of you know, decades. Uh, you

429
00:26:36,920 --> 00:26:39,520
Speaker 1: won't be able to notice the difference, like from one

430
00:26:39,920 --> 00:26:43,040
Speaker 1: year to the next, but from one decade to the next.

431
00:26:43,080 --> 00:26:46,440
Speaker 1: If you like looked back on the nineties and compared

432
00:26:46,760 --> 00:26:50,200
Speaker 1: like how accurate the weather forecast were back then to today,

433
00:26:50,400 --> 00:26:53,320
Speaker 1: it's pretty it's a pretty significant difference. Like what are

434
00:26:53,320 --> 00:26:56,240
Speaker 1: we batting now? Uh, I don't They didn't break it

435
00:26:56,240 --> 00:26:59,240
Speaker 1: down to a percentage, but it's just a much higher percentage,

436
00:26:59,359 --> 00:27:02,000
Speaker 1: and they can get more detailed about like when things

437
00:27:02,040 --> 00:27:05,280
Speaker 1: are going, yeah, happen, what's funny? Yeah? How Like weather

438
00:27:05,400 --> 00:27:07,480
Speaker 1: really is one of those things that are so hard

439
00:27:07,520 --> 00:27:11,600
Speaker 1: to accurately understand because we don't even understand like our

440
00:27:11,600 --> 00:27:14,480
Speaker 1: own planet so much to really be able to do that.

441
00:27:14,560 --> 00:27:17,560
Speaker 1: Like that's why I know the Juno mission and Jupiter.

442
00:27:17,720 --> 00:27:20,920
Speaker 1: That probe that's going around Jupiter is actually also very

443
00:27:20,920 --> 00:27:23,439
Speaker 1: important for weather reporting too, because like, the more they

444
00:27:23,440 --> 00:27:26,560
Speaker 1: can understand about Jupiter like hold so many other keys

445
00:27:26,560 --> 00:27:30,240
Speaker 1: to understand even weather. So I'm glad we're not using

446
00:27:30,240 --> 00:27:33,719
Speaker 1: that old as almanac anymore. Right, Yeah, but we do

447
00:27:33,840 --> 00:27:37,560
Speaker 1: understand a lot, right, it's like super sophisticated techniques sophisticated,

448
00:27:37,600 --> 00:27:40,280
Speaker 1: but there's still a lot about planetary formation we don't

449
00:27:40,320 --> 00:27:42,840
Speaker 1: know when we look at Jupiter. Basically, like they say,

450
00:27:42,880 --> 00:27:44,960
Speaker 1: a lot of the material that wasn't used to make

451
00:27:45,040 --> 00:27:46,720
Speaker 1: Jupiter help create a lot of the other planets in

452
00:27:46,720 --> 00:27:48,919
Speaker 1: the Solar System. So it's like, you know, like we

453
00:27:48,960 --> 00:27:51,840
Speaker 1: have the end product, but we don't have the recipe,

454
00:27:52,280 --> 00:27:54,159
Speaker 1: and that's what we're still trying to figure out. But

455
00:27:54,720 --> 00:27:58,480
Speaker 1: I do think it's crazy that like this crazy complicated

456
00:27:58,480 --> 00:28:05,840
Speaker 1: and sophisticated technologies associated with al Roker. Like that's right, friend,

457
00:28:08,359 --> 00:28:12,199
Speaker 1: someone happy sunshine sticker behind him. I mean this is

458
00:28:12,240 --> 00:28:16,639
Speaker 1: also like scientists. One of the stories is that, you know,

459
00:28:16,720 --> 00:28:20,600
Speaker 1: our ability to forecast weather was the deciding factor in

460
00:28:21,080 --> 00:28:24,760
Speaker 1: World War Two because like when they were going to

461
00:28:24,880 --> 00:28:29,600
Speaker 1: plan the D Day invasion was like really finely tuned,

462
00:28:29,800 --> 00:28:33,639
Speaker 1: and like basically Germany's weather forecasters were like, it won't

463
00:28:33,640 --> 00:28:36,679
Speaker 1: be during this period because it's gonna be stormy, and

464
00:28:37,520 --> 00:28:41,400
Speaker 1: Allied weather forecasters were able to like pinpoint a specific

465
00:28:41,520 --> 00:28:44,080
Speaker 1: day that it wasn't going to be stormy and like

466
00:28:44,160 --> 00:28:48,640
Speaker 1: do a surprise attack on that day. And like I

467
00:28:48,680 --> 00:28:51,720
Speaker 1: think one of the famous generals from World War two,

468
00:28:51,720 --> 00:28:54,520
Speaker 1: people were like why do people speak English instead of

469
00:28:54,560 --> 00:28:57,560
Speaker 1: German today around the world, And he was like, because

470
00:28:57,640 --> 00:29:04,400
Speaker 1: we had better weather forecasters. Like that. Shout out to meteorologists, yeah, meteorology, um,

471
00:29:04,480 --> 00:29:08,600
Speaker 1: shout out to science. Shout out to barometric pressure readings.

472
00:29:08,640 --> 00:29:11,840
Speaker 1: But there's also there's also all sorts of ways that

473
00:29:11,880 --> 00:29:14,440
Speaker 1: it gets fucked up, Like you know, the the at

474
00:29:14,440 --> 00:29:17,280
Speaker 1: a certain point, the butterfly effect does come into account,

475
00:29:17,360 --> 00:29:20,560
Speaker 1: like cast theory. So like just you walking down the street,

476
00:29:20,640 --> 00:29:23,520
Speaker 1: like you can never have a fully accurate model of

477
00:29:23,560 --> 00:29:27,440
Speaker 1: what the weather is going to be in the next

478
00:29:27,520 --> 00:29:30,840
Speaker 1: day because you're walking down the street like creates a

479
00:29:31,040 --> 00:29:34,920
Speaker 1: small draft that creates you know, can like ripple off

480
00:29:34,960 --> 00:29:38,800
Speaker 1: into a million different directions, So you'll never get it

481
00:29:38,880 --> 00:29:42,880
Speaker 1: fully accurate because there's just never going to be a

482
00:29:43,800 --> 00:29:48,880
Speaker 1: input mechanism that is taking like readings at every single

483
00:29:49,000 --> 00:29:52,080
Speaker 1: atom like on the globe. So you just have to

484
00:29:52,600 --> 00:29:55,720
Speaker 1: like get as good as you possibly can basically. Um.

485
00:29:55,760 --> 00:29:59,000
Speaker 1: But it is one of those sorts of progress that

486
00:29:59,040 --> 00:30:02,040
Speaker 1: I feel like we take for granted because it's just

487
00:30:02,120 --> 00:30:05,080
Speaker 1: always around us, it's always there. I'm still gonna get

488
00:30:05,120 --> 00:30:08,800
Speaker 1: fucking mad at that app when it's wrong, though I

489
00:30:08,840 --> 00:30:12,000
Speaker 1: thought it was gonna be hot the fucking pool party No.

490
00:30:12,160 --> 00:30:16,440
Speaker 1: Sixty six to funk up my face Weather Channel. But yeah,

491
00:30:16,800 --> 00:30:20,480
Speaker 1: so lout to all the meteorologist out there. Yeah, all right,

492
00:30:20,520 --> 00:30:24,160
Speaker 1: I'm gonna start the myths out with the fact that

493
00:30:24,280 --> 00:30:26,320
Speaker 1: so my myth is that we don't know what happened

494
00:30:26,320 --> 00:30:32,840
Speaker 1: to that plane, which one from like four years ago. Yeah, yeah,

495
00:30:33,000 --> 00:30:36,120
Speaker 1: we know, we do know. Yeah, we basically know what happened.

496
00:30:36,360 --> 00:30:40,320
Speaker 1: So what happened, So it was the pilot. So yeah,

497
00:30:40,360 --> 00:30:45,040
Speaker 1: so um a, you're talking about ocean the Malaysian air

498
00:30:45,920 --> 00:30:50,160
Speaker 1: that disappeared, so we have so basically the reason we

499
00:30:50,200 --> 00:30:52,240
Speaker 1: didn't think I mean, it was a very strange event.

500
00:30:52,520 --> 00:30:55,200
Speaker 1: But the reason that people weren't able to put it

501
00:30:55,240 --> 00:30:58,560
Speaker 1: together pretty quickly was because Malaysia is a fairly corrupt

502
00:30:58,600 --> 00:31:01,280
Speaker 1: country and they didn't want to be pinned on them

503
00:31:01,320 --> 00:31:04,240
Speaker 1: as like letting this dude fly a plane, so he

504
00:31:04,320 --> 00:31:07,240
Speaker 1: was like made to be this like you know, totally

505
00:31:07,240 --> 00:31:11,760
Speaker 1: stand up person with a record. And so the main

506
00:31:12,040 --> 00:31:15,440
Speaker 1: piece of evidence, like there were all these different beacons

507
00:31:15,480 --> 00:31:18,680
Speaker 1: that showed exactly where the plane went and it like

508
00:31:18,800 --> 00:31:22,120
Speaker 1: made this big turn, and it's just it can't have

509
00:31:22,160 --> 00:31:26,200
Speaker 1: been hijacking, and it can't have been them flying like

510
00:31:26,640 --> 00:31:29,320
Speaker 1: passing out and the plane going on autopilot, like it

511
00:31:29,360 --> 00:31:31,080
Speaker 1: had to have been somebody was flying it and the

512
00:31:31,080 --> 00:31:33,720
Speaker 1: only rational thing is that it was a pilot. But

513
00:31:33,800 --> 00:31:36,600
Speaker 1: the main piece of evidence that like kind of nails

514
00:31:36,640 --> 00:31:39,520
Speaker 1: it for me is the pilot had a flight simulator

515
00:31:39,640 --> 00:31:43,960
Speaker 1: in his house and one of the flights that he

516
00:31:44,000 --> 00:31:49,200
Speaker 1: had taken was this exact route crashing into the Antarctic Ocean.

517
00:31:49,600 --> 00:31:56,200
Speaker 1: Like do what he he had done that and yes,

518
00:31:56,360 --> 00:31:59,280
Speaker 1: and it was the only one of like a thousand

519
00:31:59,280 --> 00:32:02,000
Speaker 1: different ones that he had done on like sped up.

520
00:32:02,440 --> 00:32:04,959
Speaker 1: So it's like usually he does it for the experience

521
00:32:05,000 --> 00:32:08,680
Speaker 1: in practice, but this one he was basically either doing

522
00:32:08,720 --> 00:32:11,760
Speaker 1: it to see how quickly the fuel would run out,

523
00:32:12,000 --> 00:32:14,640
Speaker 1: or as sort of a clue to people, like, yeah,

524
00:32:14,680 --> 00:32:17,640
Speaker 1: I'm doing this that that's what people. But yeah, he

525
00:32:17,680 --> 00:32:21,520
Speaker 1: had flown basically that exact route in a flight simulator,

526
00:32:21,640 --> 00:32:23,760
Speaker 1: but they had like basically covered it up. There's a

527
00:32:23,800 --> 00:32:26,680
Speaker 1: there's a really interesting magazine article about it that people

528
00:32:26,680 --> 00:32:32,400
Speaker 1: should check out. Anyways, we know did he do it,

529
00:32:32,560 --> 00:32:35,200
Speaker 1: did he do it on purpose? And the bread what

530
00:32:35,720 --> 00:32:41,080
Speaker 1: and the breadcrumbs are that he did that practice run

531
00:32:41,240 --> 00:32:43,760
Speaker 1: on his computer game, And that's not unheard of. There

532
00:32:43,800 --> 00:32:46,520
Speaker 1: was that Egyptian air flight where the dude just like no,

533
00:32:47,040 --> 00:32:50,200
Speaker 1: nosed it into the Atlantic Ocean. There's been a couple

534
00:32:50,200 --> 00:32:53,760
Speaker 1: of flights like that where there was the German pilot

535
00:32:54,040 --> 00:32:57,320
Speaker 1: who uh flew into the side of a mountain with

536
00:32:57,360 --> 00:32:59,960
Speaker 1: a bunch of people on board. Yeah, so it's really sad,

537
00:33:00,440 --> 00:33:04,040
Speaker 1: but it is also like, I feel like the media

538
00:33:04,320 --> 00:33:07,360
Speaker 1: is somewhat exploiting it because it's like a huge mystery

539
00:33:07,400 --> 00:33:09,480
Speaker 1: and they like talking about it, but it's actually just

540
00:33:09,520 --> 00:33:14,040
Speaker 1: a very sad story. What is a myth? What's something

541
00:33:14,440 --> 00:33:17,000
Speaker 1: people think it's true? You know, I was gonna say overrated,

542
00:33:17,000 --> 00:33:18,480
Speaker 1: but I guess I did them both at once, So

543
00:33:18,520 --> 00:33:20,680
Speaker 1: then I'll do. A myth is that people with cute

544
00:33:20,720 --> 00:33:26,280
Speaker 1: socks are fun people. I love that myth. I think

545
00:33:26,320 --> 00:33:29,200
Speaker 1: the biggest sociopaths in the world use cute socks as

546
00:33:29,240 --> 00:33:32,440
Speaker 1: a way to disguise or to try. You'll meet the

547
00:33:32,680 --> 00:33:39,360
Speaker 1: biggest Yeah, you'll meet the most sociopathic hedge fun motherfucker.

548
00:33:40,040 --> 00:33:43,040
Speaker 1: He'll be wearing the cutest socks you ever saw. And

549
00:33:43,160 --> 00:33:46,280
Speaker 1: I'm just telling you, ladies, I'm wearing white sox right now.

550
00:33:46,520 --> 00:33:53,320
Speaker 1: Yeah we're not cute. I will brand is that that's

551
00:33:53,320 --> 00:33:56,360
Speaker 1: cb g mL, that's my cold brew. Got me like

552
00:33:56,680 --> 00:34:01,160
Speaker 1: tube socks. Um So, I I just want to let

553
00:34:01,160 --> 00:34:06,720
Speaker 1: you know that I am a nice person with nothing.

554
00:34:06,800 --> 00:34:08,759
Speaker 1: I have no money or anything because I am a

555
00:34:08,840 --> 00:34:11,480
Speaker 1: nice person and Uh, you know, my socks are normal,

556
00:34:11,760 --> 00:34:14,520
Speaker 1: and it's my way of saying I will treat you right.

557
00:34:14,600 --> 00:34:16,200
Speaker 1: And if you if I was wearing socks that had

558
00:34:16,239 --> 00:34:18,319
Speaker 1: little typewriters on them or I don't know what people

559
00:34:18,520 --> 00:34:20,719
Speaker 1: get on their socks, you know you better watch out

560
00:34:20,719 --> 00:34:23,400
Speaker 1: for me because I'm trying to compensate for some evil

561
00:34:23,440 --> 00:34:25,959
Speaker 1: ship that I got. Also, sometimes I do wear cute

562
00:34:25,960 --> 00:34:28,840
Speaker 1: socks and I'm still nice, but it doesn't mean that

563
00:34:29,360 --> 00:34:31,680
Speaker 1: where the line, you know, where's the line? You don't know?

564
00:34:32,600 --> 00:34:34,840
Speaker 1: Draw the line when the guy you're dealing with with

565
00:34:34,880 --> 00:34:38,040
Speaker 1: the cute socks. And I would say, guy you're with

566
00:34:38,160 --> 00:34:40,920
Speaker 1: is a creep, that's where you should draw the line.

567
00:34:41,320 --> 00:34:44,200
Speaker 1: And don't don't let his cute socks factor into well

568
00:34:44,239 --> 00:34:47,160
Speaker 1: he's creep, but he wears those cutes socks. Don't like

569
00:34:47,200 --> 00:34:51,000
Speaker 1: cute socks, you know, dilute, dilute. I'm just saying, cute

570
00:34:51,040 --> 00:34:54,520
Speaker 1: socks have been weaponized by bad people. And you know what,

571
00:34:54,560 --> 00:34:58,680
Speaker 1: I don't care if that was that, I'm never gonna

572
00:34:58,680 --> 00:35:06,120
Speaker 1: get that. So totally, that was a real thing. I said, dude,

573
00:35:06,120 --> 00:35:09,560
Speaker 1: I know that's yeah, you know what I mean though,

574
00:35:09,560 --> 00:35:11,480
Speaker 1: I mean, like, even if it's like, yeah, even if

575
00:35:11,480 --> 00:35:13,560
Speaker 1: it's super far out and I know it's bullshit, I

576
00:35:13,600 --> 00:35:16,480
Speaker 1: still like to try and stick with it. Just just

577
00:35:16,480 --> 00:35:20,319
Speaker 1: just try it out. Yeah for sure, why not. We're

578
00:35:20,320 --> 00:35:22,399
Speaker 1: gonna take a quick break and we'll be right back.

579
00:35:31,040 --> 00:35:35,560
Speaker 1: And we're back, And so are Janko jeans? Did you

580
00:35:35,560 --> 00:35:38,920
Speaker 1: wear Jenkos? I had a whole Janko outfit that was

581
00:35:39,040 --> 00:35:43,960
Speaker 1: like cream cream jeans, cream shirt with like black stitching

582
00:35:44,080 --> 00:35:48,719
Speaker 1: on it. Oh my god, cream in my jeans. You

583
00:35:48,800 --> 00:35:53,799
Speaker 1: had like off white jinkle outfit? Yeah, my god, Like

584
00:35:53,920 --> 00:35:56,680
Speaker 1: and the sh it was shorts and a shirt and

585
00:35:56,680 --> 00:35:59,800
Speaker 1: the shorts came down to like write about my ankles. Yeah,

586
00:35:59,840 --> 00:36:02,359
Speaker 1: I'm I had rocketwear shorts that were like that long

587
00:36:02,400 --> 00:36:05,760
Speaker 1: back in the day. Um, did you ever wear Jinkos?

588
00:36:06,280 --> 00:36:08,440
Speaker 1: I rock them? You did? Yeah? Damn I'm the only

589
00:36:08,440 --> 00:36:11,200
Speaker 1: one not rocking Jinkos. Never rock Jenkos, No, because I

590
00:36:11,200 --> 00:36:14,720
Speaker 1: mean when that was for like ravers, and I wasn't

591
00:36:14,760 --> 00:36:17,200
Speaker 1: like I'm already I had baggy jeans, but like the

592
00:36:17,239 --> 00:36:19,440
Speaker 1: way how baggy those things where I was like, no,

593
00:36:19,600 --> 00:36:22,120
Speaker 1: I don't have enough candy necklaces on to rock the

594
00:36:22,200 --> 00:36:25,399
Speaker 1: Jinko necklace or the jeans. What what was your style

595
00:36:25,440 --> 00:36:28,080
Speaker 1: when you were rocking ginkos. They were just like a

596
00:36:28,160 --> 00:36:32,040
Speaker 1: light blue jean and I was just like, what were

597
00:36:32,080 --> 00:36:36,759
Speaker 1: you into? At the time, I was really lost as

598
00:36:36,800 --> 00:36:39,760
Speaker 1: far as my fashion style. I was wet in middle school.

599
00:36:39,800 --> 00:36:42,920
Speaker 1: I think it was like A was like in middle school. Yeah,

600
00:36:43,000 --> 00:36:44,920
Speaker 1: and I was just trying things out. I was just

601
00:36:44,920 --> 00:36:46,880
Speaker 1: trying to I was trying to make up out I couldn't.

602
00:36:46,960 --> 00:36:48,520
Speaker 1: I still wasn't buying a lot of my own makeup.

603
00:36:48,560 --> 00:36:50,680
Speaker 1: I was using my mom's makeup. It was a real

604
00:36:50,760 --> 00:36:53,800
Speaker 1: experimental time, I really. I mean I did ditch them

605
00:36:53,880 --> 00:36:57,040
Speaker 1: soon after that, but I would rock them with like

606
00:36:57,080 --> 00:37:03,680
Speaker 1: white sneakers and like some sort of or something. I

607
00:37:03,719 --> 00:37:05,239
Speaker 1: have no. I was doing a lot of weave at

608
00:37:05,239 --> 00:37:07,759
Speaker 1: the time, like the hair extensions. It's trying to be cool,

609
00:37:07,960 --> 00:37:12,479
Speaker 1: just trying to find that that target. They were like

610
00:37:12,600 --> 00:37:15,040
Speaker 1: it was a lot of the what do you call

611
00:37:15,040 --> 00:37:19,160
Speaker 1: those kids, like the the goth kids would rock them,

612
00:37:19,880 --> 00:37:22,479
Speaker 1: and so I felt like I'm not goth, but like

613
00:37:23,120 --> 00:37:26,040
Speaker 1: I'm not trying to figure out my style, but that

614
00:37:26,160 --> 00:37:29,319
Speaker 1: was a goth trend. Yeah, definitely a vibe a lot

615
00:37:29,320 --> 00:37:31,480
Speaker 1: of chain while it's hanging off of it. Well, there

616
00:37:31,560 --> 00:37:33,799
Speaker 1: let me just tell you y'all something. They're back, okay,

617
00:37:33,960 --> 00:37:36,040
Speaker 1: And I don't know if you remember, back in the day,

618
00:37:36,239 --> 00:37:38,680
Speaker 1: you could get that ship pretty much anywhere, like Pacific

619
00:37:38,760 --> 00:37:42,000
Speaker 1: sunwhere wherever. It wasn't like it wasn't like trying to

620
00:37:42,080 --> 00:37:45,080
Speaker 1: get off white, you know, some Virgil Ablow type ship.

621
00:37:45,440 --> 00:37:48,520
Speaker 1: Well now they're coming back, and it's like in a

622
00:37:48,640 --> 00:37:51,640
Speaker 1: limited quantities and they're trying to make it like a

623
00:37:51,719 --> 00:37:55,439
Speaker 1: bespoke denim experience. Why not because you know how much

624
00:37:55,640 --> 00:37:57,279
Speaker 1: you know how much the cheapest fair of Jinkos costs

625
00:37:57,280 --> 00:38:01,839
Speaker 1: in the new issue or the new reissue dollars up

626
00:38:01,880 --> 00:38:07,479
Speaker 1: to three fifty for the fucking the l limited edition ship. Yeah,

627
00:38:07,560 --> 00:38:13,480
Speaker 1: capitalism um. Yeah. And they look exactly like they did before.

628
00:38:13,520 --> 00:38:17,280
Speaker 1: They are huge. They look like you hate your parents

629
00:38:17,640 --> 00:38:20,719
Speaker 1: exactly you hate your parents in the early nineties, right, Yeah,

630
00:38:20,760 --> 00:38:22,880
Speaker 1: But I guess that's whole. It's so funny how everything's

631
00:38:22,880 --> 00:38:26,200
Speaker 1: coming like cyclically back. Because I was wearing I have

632
00:38:26,320 --> 00:38:29,840
Speaker 1: like a Christina Aguilera stripped Justified Tour T shirt and

633
00:38:29,880 --> 00:38:32,960
Speaker 1: at the Farmer's Market, this like younger woman was like,

634
00:38:33,239 --> 00:38:35,440
Speaker 1: oh my god, that shirts so cool. Where'd you buy that?

635
00:38:35,760 --> 00:38:40,280
Speaker 1: And I was like at the tour four and she's like, wow,

636
00:38:40,320 --> 00:38:42,799
Speaker 1: that's so cool, like as if like it was like

637
00:38:43,280 --> 00:38:45,319
Speaker 1: that's like currency. Now it's like, damn, where'd you get

638
00:38:45,320 --> 00:38:48,080
Speaker 1: that old as shirt? I'm like, came here, look at

639
00:38:48,080 --> 00:38:52,600
Speaker 1: this old guy wearing a lot of bell bottom jeans.

640
00:38:53,320 --> 00:38:59,520
Speaker 1: At the time. Where where were you living at the time, Massachusetts, Norwood,

641
00:38:59,520 --> 00:39:03,600
Speaker 1: MASSACHUSETTLD Middle School. But even then we felt like, oh

642
00:39:03,800 --> 00:39:07,399
Speaker 1: it's coming back like bell Bontoms seventies and I think

643
00:39:07,520 --> 00:39:09,359
Speaker 1: Bell Bonnoms back again now? And did you have the

644
00:39:09,360 --> 00:39:11,600
Speaker 1: heels of them all just stepped on and tattered from

645
00:39:11,600 --> 00:39:14,400
Speaker 1: like walking all over him, just like everybody has to

646
00:39:14,440 --> 00:39:18,040
Speaker 1: have every everyone has to have you know, ready at

647
00:39:18,080 --> 00:39:21,120
Speaker 1: least around the hem. But yeah, I'm just it's so weird.

648
00:39:21,200 --> 00:39:23,400
Speaker 1: I just don't get it. And this these are the

649
00:39:23,440 --> 00:39:26,719
Speaker 1: moments where I'm like, yes, I am old, and I

650
00:39:26,760 --> 00:39:29,880
Speaker 1: do not I no longer understand. I think it's like

651
00:39:30,000 --> 00:39:33,920
Speaker 1: the everlasting value of nostalgia, right, that's all it's showing.

652
00:39:34,000 --> 00:39:37,520
Speaker 1: I was like my these whole last like ten years.

653
00:39:37,600 --> 00:39:40,560
Speaker 1: I feel like everything like Netflix came on and Friends

654
00:39:40,560 --> 00:39:43,440
Speaker 1: is the biggest show on Netflix is the biggest show.

655
00:39:43,680 --> 00:39:48,560
Speaker 1: Like everyone's just recycling old things because yeah, nostalgia was valuable.

656
00:39:48,640 --> 00:39:52,080
Speaker 1: Let's just regress together in every possible way. I mean,

657
00:39:52,280 --> 00:39:55,760
Speaker 1: this might be like I think this is the line

658
00:39:55,840 --> 00:40:00,640
Speaker 1: if fashion cycles back to like real baggy jeans, Like like,

659
00:40:00,719 --> 00:40:04,640
Speaker 1: I'm not going with it. I'm stuck here. Yeah, yeah, yeah,

660
00:40:04,719 --> 00:40:07,719
Speaker 1: I think I'm just wearing straight leg jeans for the

661
00:40:07,719 --> 00:40:12,920
Speaker 1: rest of my boots. Yeah. Anybody if people start dressing

662
00:40:12,920 --> 00:40:17,279
Speaker 1: like Michael Jordan again, like I'm out. We're all at

663
00:40:17,320 --> 00:40:19,399
Speaker 1: that age though. I feel like millennials are all at

664
00:40:19,400 --> 00:40:21,719
Speaker 1: that age where we're gonna whatever we're doing now is

665
00:40:21,719 --> 00:40:24,480
Speaker 1: what we'll be doing when we're fifty, right, Like what

666
00:40:24,520 --> 00:40:27,359
Speaker 1: I'm listening to. I started listening to Lauren Hill again

667
00:40:27,400 --> 00:40:28,920
Speaker 1: because that's what I was listening to when I was

668
00:40:28,960 --> 00:40:31,680
Speaker 1: like in high school and college and it's still fire

669
00:40:32,239 --> 00:40:33,759
Speaker 1: and I was like, Yo, this stuff is still I

670
00:40:33,880 --> 00:40:40,160
Speaker 1: started listening to the Score again. I'll be listening to

671
00:40:40,200 --> 00:40:43,279
Speaker 1: it till I'm fifty. Now there's like little Nasac. I

672
00:40:43,320 --> 00:40:48,440
Speaker 1: don't know who that is or why it's happening. Mean, yeah,

673
00:40:48,480 --> 00:40:50,080
Speaker 1: but I don't know, you know, I'm just like, oh,

674
00:40:50,120 --> 00:40:53,560
Speaker 1: I'm at that age when I just like ignore new stuff. Yeah.

675
00:40:53,680 --> 00:40:55,400
Speaker 1: There are times too when I'm like looking at like

676
00:40:55,520 --> 00:40:57,560
Speaker 1: even artists I know, like rappers, and I'm looking at

677
00:40:57,560 --> 00:41:03,560
Speaker 1: the feature funk is this man person like Toddler Man

678
00:41:03,640 --> 00:41:05,960
Speaker 1: that this fucking screen name like rapper names. It is

679
00:41:06,040 --> 00:41:09,960
Speaker 1: not a screen name like xx roxy chick one x X.

680
00:41:11,440 --> 00:41:16,120
Speaker 1: I mean, like, all right, that's gonna do it for

681
00:41:16,160 --> 00:41:20,240
Speaker 1: this week's weekly Zeitgeist. Please like and review the show.

682
00:41:20,880 --> 00:41:24,720
Speaker 1: If you like the show, uh means the world to Miles.

683
00:41:24,800 --> 00:41:29,239
Speaker 1: He needs your validation, folks. I hope you're having a

684
00:41:29,280 --> 00:42:05,839
Speaker 1: great weekend and I will talk to him Monday by

685
00:42:03,280 --> 00:42:09,000
Speaker 1: up