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Speaker 1: Hello the Internet, and welcome to season eighty nine, episode

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Speaker 1: one of Joe Daly's eight Geist, a production of I

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Speaker 1: Heart Radio. This is a podcast where we take a

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Speaker 1: deep dive into American share consciousness and safely off the top,

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Speaker 1: fuck Coke Industries and funck Fox News. It's Monday, July first,

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Speaker 1: two thousand nineteen. My name's Jack O'Brien, A K, Beto,

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Speaker 1: O dork a K Julian podcastro A K, Bill de Blasio,

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Speaker 1: Brian a k A Corny booker, work, corny hooker, or

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Speaker 1: horny cooker, depend of how close speet. That's courtesy of

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Speaker 1: Christi Amagucci Man and I'm thrilled to beat. Joining by

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Speaker 1: today's acting co host Caitlyn Durante. Hello, A K A.

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Speaker 1: Since I'm sitting in for Miles Gray, my name anagrams

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Speaker 1: to Nile Cray plus some other letters that that don't fit. Also,

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Speaker 1: so last week, when I was at the guest uh,

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Speaker 1: you might remember that my AK was Lauren C Titanic.

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Speaker 1: So this week, um, my name also anagrams to ruined Atlantic,

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Speaker 1: which when the Titanic sunk into the Atlantic Ocean, it

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Speaker 1: kind of ruined it the Atlantic for all of us. Yeah,

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Speaker 1: I refused to swim in it anymore. Same and then

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Speaker 1: finally also anagrams to nine tit dracula. Does it really? Yes?

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Speaker 1: It does? Good God, that is fine. That's the best

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Speaker 1: one yet. I don't know how they keep getting better.

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Speaker 1: But nine tit dracula is so it's a it's a

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Speaker 1: cow right with nine Oh yeah, yeah, no, I got that.

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Speaker 1: And I think it's always coming, even them. Uh you

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Speaker 1: would think and usually yes, But I've discovered re cently

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Speaker 1: that opossums have thirteen nipples. They form a circle and

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Speaker 1: then there's one in the middle because the rule is

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Speaker 1: that it's supposed to be two x the number of

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Speaker 1: like babies that you generally give birth. Is that the

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Speaker 1: So that's why we have to and you know dogs

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Speaker 1: have a whole shipload because they usually give birth to

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Speaker 1: the whole litter. Whole litter. Uh. Well, we are thrilled

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Speaker 1: to be joined in our third seat by a very

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Speaker 1: special guest from the Sleepwalkers podcast, Cara Price. Hi, everybody, Hi,

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Speaker 1: welcome nine tied Dracula. Blood must spew out of the

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Speaker 1: of the Oh yeah, I didn't even think of that.

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Speaker 1: That actually, would you milk that nine titted Dracula. Yeah,

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Speaker 1: you're gonna get some. I wonder if there's like one

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Speaker 1: tip for every blood type, so there's like a positive

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Speaker 1: oh you know, the universal donor the universal recipient like

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Speaker 1: a d picky about their blood types, like which ones

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Speaker 1: they like, because mosquitoes are and my poor wife and

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Speaker 1: son are whatever their favorite type is and they just

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Speaker 1: attack the ship out of them, and me and my

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Speaker 1: other son just like get left alone. It's really terrible.

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Speaker 1: It's mean, mosquitoes are so discriminatory. Yeah, it's really well.

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Speaker 1: I know, in like True Blood when they were like

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Speaker 1: selling like the synthetic blood blood at bars, they had

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Speaker 1: like oh negative and like different types of blood. So

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Speaker 1: I think in that show, at least the cannon was

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Speaker 1: that different vampires preferred different blood types. Yeah, that would

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Speaker 1: be a great solution. If somebody could breed a nine

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Speaker 1: tips Dracula, like blood spewing cow, then you know, no

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Speaker 1: more need to attack the humans. I think the mass

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Speaker 1: of animal rights issue, Yeah, that's true, would not like this.

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Speaker 1: Peter probably would would advocate for humans to be turned

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Speaker 1: to vampires instead of the milking of a cow clearly

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Speaker 1: built specifically for blood milk. If it's it's an undead cow.

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Speaker 1: Everybody's just digging because this is the whole episode, by

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Speaker 1: the way, So I hope you're ready. Uh yeah, no,

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Speaker 1: it's great antagram, great questions all around. Kara, Let's just

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Speaker 1: say right off top, in case anyone's trying to google

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Speaker 1: your name, how are we spelling care pres r E

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Speaker 1: I S as in sam ss and Sam. Yeah. Yeah,

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Speaker 1: Kara is k A r A Yeah, that's right. Yeah.

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Speaker 1: So I don't know what you'll find if you google,

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Speaker 1: but I do have a podcast called Sleepwalker, Yeah, which

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Speaker 1: you do. Alls. Um, Well, we're gonna get to know

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Speaker 1: you a little bit better in a moment. But for

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Speaker 1: we're going to tell our listeners a couple of things

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Speaker 1: we're talking about today. The first story we have here

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Speaker 1: that is eight poll about, you know, the favorability and

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Speaker 1: percentages for like whether people would vote for candidates before

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Speaker 1: and after the first night. So it shows, you know,

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Speaker 1: how much the debates affected people's favorability and whether someone's

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Speaker 1: going to vote for them. And we can talk about

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Speaker 1: we can talk about how the first night changed things

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Speaker 1: as well, and how the second night changed things in

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Speaker 1: our hearts. We're gonna talk about Twitter's new rules, which

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Speaker 1: may or may not play directly into the president's hands.

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Speaker 1: We're gonna talk about the straight Pride Parade, aren't we, Caitlin, Um,

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Speaker 1: We're gonna talk about uh. New York City has declared

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Speaker 1: a climate emergency. But first, Kara, we like to ask

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Speaker 1: our guests, what is something from your search history that

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Speaker 1: is revealing about who you are? So El Torrero de

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Speaker 1: la Tora, the Jewish gay mad No, what I had

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Speaker 1: googled was gay Jewish matador. That was something that I

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Speaker 1: looked up in my recent search history. And this he

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Speaker 1: you know, this is a guy who was the first

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Speaker 1: of his kind in many ways. I think he was

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Speaker 1: a gay Jewish matador who lived in Spain but was

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Speaker 1: based in Brooklyn. No, I was based in Brooklyn, and

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Speaker 1: Um died in Brooklyn. Pennyless now died in Greenwich Village, penniless. Um.

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Speaker 1: But my sister had sent me a screenshot of him,

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Speaker 1: and he is my grandfather's twin. I mean, really looked

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Speaker 1: like my grandfather, who was a straight Jew from Brooklyn exactly. Um.

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Speaker 1: So I googled it and I've been obsessed with the story.

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Speaker 1: It's The New York Times reported on it because you know,

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Speaker 1: Pride so they're like scouring the net for the gays

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Speaker 1: they can find, and he's one of them. Although I

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Speaker 1: think being a gay Jewish Man sort of lends itself

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Speaker 1: also to being a matador in terms of he's very lean.

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Speaker 1: He loved fashion. And there's a great quote from the

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Speaker 1: article that they're quoting him and he says, people would say,

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Speaker 1: but you're Jewish, Mr mark Wood said, and he'd say, yes,

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Speaker 1: but the bulls are Catholic. Pretty good. Yeah, that has

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Speaker 1: to do with me there there it is. Now. I

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Speaker 1: think we know everything we need to know. What is

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Speaker 1: something you think is overrated? Uh? Plant based milk like

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Speaker 1: almond milk and stuff. Yeah, coconut milk. It's disgusting. Should

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Speaker 1: I said, maybe I could say something more about you

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Speaker 1: know AI, but I just think plant based milk is disgusting. Uh,

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Speaker 1: you have any particular well chased like cardboard, all of it.

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Speaker 1: Being in l I live in New York and being

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Speaker 1: in l A is particularly agregious for the half and

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Speaker 1: half users of America. I went to a cafe the

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Speaker 1: other day here and they had no milk. They only

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Speaker 1: had plant milk. There's a lot of utter based content.

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Speaker 1: I mean, oh yeah, black they're up exactly. Plant based

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Speaker 1: milk is disgusting because we all want Dracula tit milk

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Speaker 1: I want all day, So you are not lactose intolerant.

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Speaker 1: It doesn't sound any I wouldn't know, but it's it's

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Speaker 1: I mean, I am probably makes me ill because I'm Jewish,

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Speaker 1: as you know from my search history, and it does

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Speaker 1: make me ill, but not as much as oat milk.

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Speaker 1: I was because people are like, well, have you tried

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Speaker 1: oat milk? Milk? Coconut is the coconut coconut, oat almond.

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Speaker 1: I love almond milk. Macadamia nut is a new one. Yeah,

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Speaker 1: but milk is like milk is from an other that

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Speaker 1: this is almond, this is almond water. That's what milk,

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Speaker 1: almond milk is. So I think it's overrated and it's

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Speaker 1: just I mean, there was like totally was back ordered. Yeah,

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Speaker 1: people went wild in Los Angeles for oat milk, especially

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Speaker 1: the oat milk craze of was a violent period in

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Speaker 1: l A's history. I would imagine, yeah, I would imagine.

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Speaker 1: I think it's also like if you I was talking

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Speaker 1: to this guy yesterday who's a scientist, and he said,

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Speaker 1: just put quantum or neuro on anything and you can

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Speaker 1: sell it. I think if quantum milk, yeah, well that

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Speaker 1: but if you quantum oat milk, I was like, I

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Speaker 1: need to go home right now and create a product

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Speaker 1: that's a quantum plant based milk because it gets it

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Speaker 1: clears your neurotoxins. It's tiny and it just clears you

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Speaker 1: out right exactly neurotoxins. That's another one. Yeah, I'm gonna

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Speaker 1: a lot. I mean, I'll get backlash for everyone likes it.

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Speaker 1: I just I get half and have it makes my

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Speaker 1: friends sick that I use half and half. It's disgusting.

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Speaker 1: But it makes coffee tastes like a milkshake. Yeah. I

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Speaker 1: wouldn't put almond milk or any of those like nut

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Speaker 1: milks or anything in coffee that is gross. Oh no, no, no,

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Speaker 1: I put it on cereal. I might do that just

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Speaker 1: because it would just be it would be watered down

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Speaker 1: by the sugar of cereal, which I can get on

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Speaker 1: board with. Well, yeah, I find real milk now taste

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Speaker 1: disgusting because I've made I made the transition to almond.

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Speaker 1: But I only ever like, I still eat like milk

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Speaker 1: ice cream right, Like vegan ice cream is like, just

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Speaker 1: don't eat it. Yeah. I don't eat ice creams, vegan cheese,

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Speaker 1: no thing diet. Literally, like someone gave me a vegan

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Speaker 1: cheese like slice. Sorry, I was just thinking about it,

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Speaker 1: That's why I was speakingless. It was that it was disgusting. Disgusting. Um,

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Speaker 1: I wass one skim milk. I mostly drink plant based

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Speaker 1: milks these days, but I still like, I think Cereal

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Speaker 1: is twenty times better with whole milk. Ye. There's just

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Speaker 1: something about that thick, rich juice that every time. What

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Speaker 1: is something you think is under rage? Oh? The USPS.

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Speaker 1: I there's a sorry sorry, sorry, sorry I am. I

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Speaker 1: think it's probably our strongest heritage brand in the US.

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Speaker 1: And I think that it is. When I think about

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Speaker 1: what happens every day for a letter to get somewhere else,

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Speaker 1: it brings me into tears. I don't know, maybe, I mean,

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Speaker 1: I think a lot of other people respect the USPS,

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Speaker 1: although it's dwindle or actually Trump would not call the

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Speaker 1: USPS the failing USPS. For some reason, he likes it.

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Speaker 1: I think it's like an establishment thing. Um. But yeah,

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Speaker 1: the the United States Postal Service stamps. I mean, I'm

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Speaker 1: a philatelist. There's just a everything about it to me,

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Speaker 1: is so attractive. Stamp collect yes, stamp enthusiasts, not someone

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Speaker 1: who flates. No, that's a flation. Well there's not. There's

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Speaker 1: a facialist, which is someone who treats the top of

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Speaker 1: a stamp with their finger. But yeah, I have a

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Speaker 1: lineage of filatelists. What's your favorite stamp? You have it?

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Speaker 1: But right now, the Ellsworth Kelly stamp is very cool.

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Speaker 1: It's the artist that you know. They do like these

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Speaker 1: capsule collections of stamps, and he's one of them. I

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Speaker 1: had one Sally Rye and may she rest? Um I

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Speaker 1: think she's dead and um okay. So he is a

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Speaker 1: very simple abstract artist. His art is a wow. It

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Speaker 1: is really cool. It's cool, it's made for stamps. I

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Speaker 1: also buy. I think stamps are great and I loved

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Speaker 1: you know. Recently I signed a lease and they were like,

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Speaker 1: do you want to you the you know, the easy

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Speaker 1: pay thing, and I said, no, I want to mail

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Speaker 1: the check because you want to and I don't know

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Speaker 1: what it is. I'm a hope for shipping and handling.

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Speaker 1: I've always been one. I love going to the I

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Speaker 1: love to mail. I'm in the USPS like at least

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Speaker 1: four so you like the process of going to the

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Speaker 1: post office, at which I have to tell you, as

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Speaker 1: a podcaster, that is sacrilege because podcasting was built on

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Speaker 1: the assumption that going to the post office is literal

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Speaker 1: hell on Earth because of stamps dot Com, Like, oh,

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Speaker 1: stamp stright, go back and listen to early podcasts that

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Speaker 1: have the baked in ads. They're gonna be like, wait,

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Speaker 1: why was Why did everybody hate the post office so

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Speaker 1: much back then? Because that was just the number one

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Speaker 1: built in presumption of of all podcasts was that, well,

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Speaker 1: we all fucking hate the post office? Am I right? Guys? Um,

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Speaker 1: I have a stamps dot com account, do yeah all

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Speaker 1: the time? Media ex Yeah, but I listened to podcasts

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Speaker 1: in line of the post office. That's the whole thing.

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Speaker 1: I mean, you're not supposed to be allowed to do that,

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Speaker 1: but I'm sorry. If those were unrelated to my themes, No,

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Speaker 1: it's great. We're learning a lot about that. Those are

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Speaker 1: my things. And then finally, what is a myth? What's

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Speaker 1: something people think is true you know to be false?

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Speaker 1: Well I don't. I don't know so much of this

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Speaker 1: is a myth. But there's this sense when you nine

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Speaker 1: of ten people if you ask them what AI is.

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Speaker 1: It's the same people where you ask what gluten is,

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Speaker 1: they're like bread. AI is the same way where I

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Speaker 1: think most people think of robots or like artificially intelligent

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Speaker 1: robots or people who are going to take your job,

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Speaker 1: or robots are gonna take your jobs, whereas AI is

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Speaker 1: really like what allows you to order a Starbucks coffee

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Speaker 1: when you're in your car we're still on the train. Like,

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Speaker 1: I mean, that's not the best example, but like AI

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Speaker 1: is basically part of and parcel everything that we now

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Speaker 1: do on our part sold something that yes, you're building.

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Speaker 1: I mean, it is my dream to do a short

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Speaker 1: form web series where I visit all of the post

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Speaker 1: offices of America. Would that be possible? Um no, not

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Speaker 1: when when they when? When? Development people are like, what's

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Speaker 1: the audience? That would be a hard question, old people

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

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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, and yeah, well it's also

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

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Speaker 1: really 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 adda, 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 tumors,

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

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Speaker 1: loading scans into or into brain. I think, yeah, that's right,

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

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Speaker 1: used AI to help better there in drastic market, the

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Speaker 1: open doors, the Drake raptors, Drake raptors, they should just

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Speaker 1: change their I'm so sorry, Toronto, how did they use

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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: more shop. That's right. So there's machine learning. I mean,

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Speaker 1: this is machine learning. Machine learning is basically now being

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Speaker 1: harnessed in like every possible field you can imagine, And

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Speaker 1: 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 too make predictions about other things legislatively. So

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Speaker 1: Oz took the phrase my co host took the phrase

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Speaker 1: from a British history book. But um, but but I think,

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Speaker 1: you know, for him also, when he was thinking of

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Speaker 1: the show, which he asked me to do a little

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Speaker 1: later on, but when he was thinking of this show,

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Speaker 1: you know, he he started to think about, well, what

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Speaker 1: are all the things that I don't know that aren't

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Speaker 1: going to be happening in the future, but actually are

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Speaker 1: happening right now. And how can I talk to the

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Speaker 1: people who have built these rules who a lot of

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Speaker 1: them are like sounding the alarm. You know, people who

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Speaker 1: worked at Facebook who were like, yeah, well they trained

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Speaker 1: people created the light button, were like trained on the

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Speaker 1: 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 nothing

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Speaker 1: to hide and I'm a boy scout to know, but

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

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Speaker 1: they know so much about you. And then they're you know,

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Speaker 1: manipulating your life in the background. Uh, and some people don't.

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

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

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

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Speaker 1: that makes my life a lot easier. But it's also

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Speaker 1: are you okay with the fact that Amazon also sells

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

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Speaker 1: my face is very hot, so many 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 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 can paired to other things that have

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Speaker 1: been scanned, is using AI to detect or like white

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Speaker 1: list or blacklist someone. So now they're doing it with

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

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Speaker 1: as a biometric monitor, so like, oh, that's Jack's heart.

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

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Speaker 1: they're starting 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 that, Yeah, exactly.

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

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

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

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

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

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

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Speaker 1: 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 no. 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 fakes. 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 But

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

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Speaker 1: retinal skirt. Well. They've 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 I'll be saying it's something spect it's something ecticals,

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

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

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

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Speaker 1: in the funk are you creating better than saving people's

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Speaker 1: lives from malaria? Crazy? Whatever? Yeah, we also have a

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Speaker 1: search for extraterrestrial yeah. Intelligence. Also, don't keep your eyes

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Speaker 1: open and just keep your ears open listen to podcast

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Speaker 1: they Yeah, that's right. Audio is very safe. Walk around

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Speaker 1: with your eyes closed and podcasts in your ears and

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Speaker 1: you will be safe. Um, no danger there. So basically,

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Speaker 1: be a blind hipster for the rest of your life.

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Speaker 1: There you alright, we're gonna take a quick break. We'll

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Speaker 1: be right back, and we're back and uh, kind of

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Speaker 1: staying on a similar subject to uh, you know, ways

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Speaker 1: that Silicon Valley is controlling us, So Twitter announced that

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Speaker 1: they will start blocking the tweets of famous celebrities and politicians. Basically,

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Speaker 1: they will start demanding that politicians follow their terms of service,

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Speaker 1: which I assumed that they I knew they didn't do

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Speaker 1: this because of things that the President tweets and gets

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Speaker 1: away with. But I assumed they that was like an

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Speaker 1: unspoken thing, and in this case, they're just like, no, no,

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Speaker 1: we we have a total double standard for influential people,

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Speaker 1: and we let them do whatever they want up to

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Speaker 1: this point. But now we're going to start, you know,

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Speaker 1: paying a little bit more attention to them, which is

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Speaker 1: pretty wild. M Yeah, how does the celebrity, you know,

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Speaker 1: break the terms? And so some of the examples that

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Speaker 1: people were pointing to are when the President used his

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Speaker 1: Twitter account to threaten nuclear war with North Korea, share

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Speaker 1: a video depicting violence against CNN, where like a wrestler

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Speaker 1: beat the ship out of a another wrestler who had

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Speaker 1: CNN photoshopped over their head. Very subtle, motherfucker. He's funny

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Speaker 1: a lot of the time. It's just terrorists, it's terror Yeah,

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Speaker 1: he's very entertaining, which I think is a big part

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Speaker 1: of what got us that he like hacked the entertainment

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Speaker 1: industrial complex. But what makes him different entertaining? Yeah, yeah,

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Speaker 1: actually she's pretty or when he retweets misinformation about Muslims.

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Speaker 1: Twitter spokesperson said the change was not inspired by any

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Speaker 1: specific world leader. Okay. People also pointed to Uh. Brazil's

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Speaker 1: far right, homophobic president Balsonnaro tweeted, like, during Carnivall, like

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Speaker 1: Carnival celebrations started to turn against him a little bit.

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Speaker 1: People were like Ballsonnaro. Uh. And he then tweeted a

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Speaker 1: video of somebody giving someone else a golden shower during

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Speaker 1: Carnival and people celebrating that, and he was like, I mean,

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Speaker 1: you see what these sickos are doing right at the

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Speaker 1: p P party And it's like you pulled that pretty

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Speaker 1: quickly on the file. Huh. Yeah, So that's another example

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Speaker 1: of this sort of thing that Twitter would Uh. So

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Speaker 1: they're holding people more accountable despite their influence. They are,

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Speaker 1: but it's they're acknowledged. So they're basically overtly setting up

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Speaker 1: a sort of tear system where users are officially allowed

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Speaker 1: to break the rules and instead of getting removed from

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Speaker 1: the platforms, like they have this thing where it just

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Speaker 1: like grays out the content and you have to basically

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Speaker 1: asked to see it and then right exactly and they

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Speaker 1: will not spread those tweets they want like feature them

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Speaker 1: and even if they are like viral tweets, you won't

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Speaker 1: see them in your moments feed. But our writer jam

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Speaker 1: McNab was kind of pointing out that this system could

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Speaker 1: very easily play right into Trump's tiny hands because by

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Speaker 1: playing the victim like that's his go to move, is

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Speaker 1: acting like he's being victimized. I mean, it's all fascists

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Speaker 1: go to move. But you know, he recently said on

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Speaker 1: Fox News that Google and Facebook should be sued over

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Speaker 1: their bias towards conservatives, and uh, the subreddit are that

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Speaker 1: Donald was quarantined recently after you know, members were inciting

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Speaker 1: violence against cops and basically threatening to murder police officers.

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Speaker 1: The subreddit people finally got shut down. By the way,

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Speaker 1: do you guys know that story about the Oregon politicians.

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Speaker 1: I don't think so. The Democratic Congress in Oregon was

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Speaker 1: trying to pass a climate bill and the Republicans left

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Speaker 1: the state to like go hide out to avoid having

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Speaker 1: to vote on it. Because that is apparently like their

422
00:28:44,760 --> 00:28:47,640
Speaker 1: version of a filibuster, which is a thing that has

423
00:28:47,680 --> 00:28:50,160
Speaker 1: happened before, and in fact, the Democrats in Texas did

424
00:28:50,200 --> 00:28:53,920
Speaker 1: that I think in the nineties, But this time the

425
00:28:54,000 --> 00:28:56,800
Speaker 1: Republicans are hanging out with three per centers, which is

426
00:28:56,800 --> 00:29:00,760
Speaker 1: like this heavily armed militia and they're basically saying the police,

427
00:29:00,880 --> 00:29:04,360
Speaker 1: if you bring the police to us, we will kill them.

428
00:29:04,400 --> 00:29:07,560
Speaker 1: And then people on Are the Donald were like basically

429
00:29:07,680 --> 00:29:11,920
Speaker 1: talking about like starting a war in Oregon, which I

430
00:29:12,240 --> 00:29:14,080
Speaker 1: should have known ship, like this was going to happen

431
00:29:14,080 --> 00:29:17,880
Speaker 1: in Oregon. When Robert Evans, the host of Behind the Bastards,

432
00:29:18,880 --> 00:29:22,240
Speaker 1: was like, I'm he moved to Oregon from southern California

433
00:29:22,360 --> 00:29:24,960
Speaker 1: because he was like, Yeah, that's where a bunch of

434
00:29:25,040 --> 00:29:27,400
Speaker 1: really crazy ship is going to be and I need

435
00:29:27,400 --> 00:29:29,280
Speaker 1: to be there. He's the one who told who we

436
00:29:29,280 --> 00:29:31,280
Speaker 1: were on his show, and he told me about the

437
00:29:31,280 --> 00:29:33,920
Speaker 1: three percenters. Yeah. He also told me that it was

438
00:29:34,000 --> 00:29:38,000
Speaker 1: literally illegal. There were no black people all out in Oregon,

439
00:29:38,120 --> 00:29:40,440
Speaker 1: like it was one of the states in the Yeah,

440
00:29:40,520 --> 00:29:44,040
Speaker 1: until like the nineteen twenties or something like that. Some

441
00:29:44,040 --> 00:29:48,760
Speaker 1: some like too soon ago. Yes. So as a result

442
00:29:48,800 --> 00:29:51,080
Speaker 1: of a bunch of death threats being posted on Our

443
00:29:51,160 --> 00:29:55,600
Speaker 1: the Donald by Donald Trump supporters, they quarantined that, which

444
00:29:55,640 --> 00:29:57,360
Speaker 1: means you can still get to it, but you have

445
00:29:57,400 --> 00:29:59,040
Speaker 1: to like go through a couple of paywalls, and you

446
00:29:59,040 --> 00:30:01,479
Speaker 1: can't find it on or not pay walls, but like

447
00:30:01,560 --> 00:30:03,520
Speaker 1: blocks where you have to be like are you sure,

448
00:30:04,000 --> 00:30:07,959
Speaker 1: Are you sure? And so I don't know. It just

449
00:30:08,000 --> 00:30:11,680
Speaker 1: seems like what's going to be more popular than Trump

450
00:30:11,680 --> 00:30:15,120
Speaker 1: tweeting something that does get flagged by Twitter's new rule

451
00:30:15,280 --> 00:30:17,560
Speaker 1: and then Trump like freaking out about that, like that

452
00:30:17,640 --> 00:30:22,080
Speaker 1: becomes a news cycle, you know. And I mean people

453
00:30:22,120 --> 00:30:26,280
Speaker 1: are also pointing out that Twitter itself is not like

454
00:30:26,440 --> 00:30:30,040
Speaker 1: his Twitter feed is not the thing that actually gets

455
00:30:30,080 --> 00:30:35,040
Speaker 1: his message out. It's that he tweets, tweets out messages

456
00:30:35,080 --> 00:30:38,400
Speaker 1: that then get covered by the news, by TV news,

457
00:30:38,440 --> 00:30:42,160
Speaker 1: by all manner of news. So it's just this wouldn't

458
00:30:42,200 --> 00:30:44,760
Speaker 1: really affect that. It would just be a way for

459
00:30:44,920 --> 00:30:48,680
Speaker 1: him to act like he's getting victimized while still getting

460
00:30:48,720 --> 00:30:51,960
Speaker 1: the same message out and on Fox News and when

461
00:30:52,000 --> 00:30:55,520
Speaker 1: it's outrageous enough in the rest of the mainstream media.

462
00:30:55,880 --> 00:31:00,600
Speaker 1: So yeah, just a something to keep an eye on, guys.

463
00:31:01,800 --> 00:31:06,640
Speaker 1: Uh let's check in with eight uh. So pre debate,

464
00:31:07,400 --> 00:31:13,440
Speaker 1: Joe Biden was forty one percent. After the first debate,

465
00:31:13,800 --> 00:31:17,280
Speaker 1: he went down to thirty five percent. After the second debate,

466
00:31:17,360 --> 00:31:19,920
Speaker 1: he went down to thirty one percent. Well that's a

467
00:31:20,000 --> 00:31:23,720
Speaker 1: huge drop. Yeah, so he fell off ten percent before

468
00:31:23,840 --> 00:31:27,680
Speaker 1: the first debate. Elizabeth Warren was twelve percent. After the

469
00:31:27,720 --> 00:31:31,080
Speaker 1: first debate she was eighteen percent, and then after the

470
00:31:31,120 --> 00:31:33,800
Speaker 1: second debate, for some reason, she got hurt really bad.

471
00:31:33,840 --> 00:31:36,160
Speaker 1: By the second debate, she went back down to fourteen percent.

472
00:31:36,720 --> 00:31:40,600
Speaker 1: I'm assuming because Kamala Harris, let's look at what rocked

473
00:31:40,640 --> 00:31:44,960
Speaker 1: it till the break dawn. So she went before the

474
00:31:45,000 --> 00:31:48,280
Speaker 1: first debate was at eight percent. After the first debate

475
00:31:48,440 --> 00:31:51,680
Speaker 1: was six point three percent, So she lost a percentage point,

476
00:31:52,000 --> 00:31:56,000
Speaker 1: presumably to Elizabeth Warren, because Elizabeth Warren had a big

477
00:31:56,080 --> 00:31:59,640
Speaker 1: jump after the first debate. After this last debate she

478
00:31:59,840 --> 00:32:04,560
Speaker 1: is at sixteen seventeen percent, so she jumped up over

479
00:32:04,680 --> 00:32:10,440
Speaker 1: ten percentage points, so she's doing good. Uh. People went

480
00:32:10,640 --> 00:32:14,200
Speaker 1: from six point seven before the debates to four point

481
00:32:14,240 --> 00:32:19,480
Speaker 1: eight after them. Corey Booker went from three percent before

482
00:32:19,520 --> 00:32:22,040
Speaker 1: the first debate to four percent after the first debate,

483
00:32:22,120 --> 00:32:26,080
Speaker 1: back down to three percent, and Beto Or just continues

484
00:32:26,160 --> 00:32:30,320
Speaker 1: to hemorrhage support. Uh, from three point six percent down

485
00:32:30,360 --> 00:32:32,600
Speaker 1: to two point two. But I mean, that might not

486
00:32:32,920 --> 00:32:37,520
Speaker 1: seem like that significant of a change, but um, you know,

487
00:32:37,680 --> 00:32:40,280
Speaker 1: when that's all you've got is two percent, that's probably

488
00:32:40,320 --> 00:32:43,840
Speaker 1: seems like a lot. So by now this is probably

489
00:32:44,440 --> 00:32:47,960
Speaker 1: in the news stream and been done and dusted and

490
00:32:48,000 --> 00:32:51,720
Speaker 1: digested by everybody, but it's still you know, we we

491
00:32:51,800 --> 00:32:56,640
Speaker 1: assumed after Biden's performance that he was going to be hurt.

492
00:32:57,000 --> 00:33:00,160
Speaker 1: People made a huge deal of the Kamala Harris thing,

493
00:33:00,160 --> 00:33:02,160
Speaker 1: and I think she did a really good job of

494
00:33:02,280 --> 00:33:06,080
Speaker 1: driving home some of the racial issues that he was

495
00:33:06,120 --> 00:33:09,560
Speaker 1: already having. But I think the thing that was new

496
00:33:09,760 --> 00:33:13,800
Speaker 1: was how kind of unsteady on his feet. He seemed

497
00:33:13,880 --> 00:33:17,240
Speaker 1: like he he just didn't seem very nimble like mentally.

498
00:33:17,560 --> 00:33:20,200
Speaker 1: And I think the thing that stuck with me the

499
00:33:20,280 --> 00:33:23,600
Speaker 1: most was when they asked, okay, first day in the

500
00:33:23,600 --> 00:33:26,040
Speaker 1: White House, what is your number one priority going to be?

501
00:33:26,560 --> 00:33:29,360
Speaker 1: And everybody was like, you know, climate change, this, that,

502
00:33:29,480 --> 00:33:36,280
Speaker 1: and he said beat Donald Trump, which is so you've

503
00:33:36,320 --> 00:33:40,000
Speaker 1: already he means that he wants to beat him up, right,

504
00:33:40,680 --> 00:33:46,520
Speaker 1: some tweet making that point. Um, But yeah, it seems

505
00:33:46,560 --> 00:33:51,000
Speaker 1: like a big portion of his support went to Elizabeth Warren,

506
00:33:51,480 --> 00:33:56,040
Speaker 1: Kamala Harris, some went to Bernie Sanders. What did Sanders do?

507
00:33:56,320 --> 00:33:59,959
Speaker 1: Let's see, he went to UM before the first debate,

508
00:34:00,320 --> 00:34:02,880
Speaker 1: he was at fourteen point four. After the first debate,

509
00:34:03,160 --> 00:34:05,560
Speaker 1: six rose up to sixteen point four, and then after

510
00:34:05,600 --> 00:34:09,719
Speaker 1: the second debate rose again about one percent to seventeen

511
00:34:09,760 --> 00:34:13,720
Speaker 1: point three. Are we looking at a Lizzie Lizzie Bernie

512
00:34:15,120 --> 00:34:19,600
Speaker 1: a ticket ticket? Yeah? I could see that. Um, it

513
00:34:19,680 --> 00:34:23,840
Speaker 1: seems like a lot of her support went to Kamala

514
00:34:23,880 --> 00:34:28,160
Speaker 1: Harris and uh and Bernie Sanders, so it seems like

515
00:34:28,360 --> 00:34:33,480
Speaker 1: similar sort of appeal. I'd like a Warren Harris ticket. Yes,

516
00:34:33,640 --> 00:34:38,960
Speaker 1: give me that please? Yeah? Who's who? You know? What?

517
00:34:39,080 --> 00:34:43,480
Speaker 1: That good question? Let me think on it. Do you

518
00:34:43,520 --> 00:34:47,080
Speaker 1: need me to What do you mean? Who's like? Which

519
00:34:47,120 --> 00:34:53,560
Speaker 1: one would be? Which one to me? Which one is? Sorry,

520
00:34:53,560 --> 00:34:56,960
Speaker 1: which Elizabeth Warren? And which one is Kamala Harris? Just

521
00:34:57,040 --> 00:35:01,319
Speaker 1: imagine Elizabeth Warren with Kamala Harris's name. That's like what

522
00:35:01,360 --> 00:35:06,520
Speaker 1: if combine the two together into a hybrid person and

523
00:35:06,520 --> 00:35:10,120
Speaker 1: then add some AI in there. That's right, Well, a

524
00:35:10,320 --> 00:35:14,600
Speaker 1: I could predict whose name This is why machine learning school.

525
00:35:15,040 --> 00:35:19,640
Speaker 1: You could predict whose name would be the likeliest to

526
00:35:19,840 --> 00:35:23,759
Speaker 1: win a presidential election based on certain Well, you'd have

527
00:35:23,800 --> 00:35:26,040
Speaker 1: to set the rules, so either you could set it

528
00:35:26,160 --> 00:35:29,840
Speaker 1: based on consonants and vowels. You could set it on

529
00:35:30,000 --> 00:35:34,000
Speaker 1: like syllables. For some reason, I think be having a

530
00:35:34,120 --> 00:35:37,719
Speaker 1: bee in a middle name or anything, just a but

531
00:35:38,120 --> 00:35:48,560
Speaker 1: that is Bill Biden, Bernie, Bob Dole, the sexiest politician,

532
00:35:49,360 --> 00:35:57,399
Speaker 1: Corey Booker, beat Oh, it seems like these are amazing. Yeah, Brian, Yeah,

533
00:35:57,520 --> 00:36:05,160
Speaker 1: that's right, or in yeah, Beth, we call her, yeah Beth. Okay,

534
00:36:05,200 --> 00:36:07,520
Speaker 1: So Kamala needs a B in her name? Was Mamala

535
00:36:07,560 --> 00:36:13,920
Speaker 1: b Harris? You know what it might actually been Harris. No,

536
00:36:14,040 --> 00:36:19,919
Speaker 1: it's not alright, guys, let's real quick. D D shit

537
00:36:20,400 --> 00:36:23,680
Speaker 1: D is a terrible letter. I think the debate sort

538
00:36:23,719 --> 00:36:27,040
Speaker 1: of made all of this more interesting and possible to

539
00:36:27,040 --> 00:36:28,840
Speaker 1: pay attention to. It just seemed like we had the

540
00:36:28,880 --> 00:36:33,640
Speaker 1: same opinions about everybody for a long time, and at

541
00:36:33,719 --> 00:36:38,359
Speaker 1: least things are moving a little bit. There's a little motion. Yeah, yeah,

542
00:36:38,560 --> 00:36:43,480
Speaker 1: people are paying attention. Yeah. It's funny that Bernie is

543
00:36:43,800 --> 00:36:47,040
Speaker 1: just you know, fourteen to sixteen to seventeen. It's like

544
00:36:47,880 --> 00:36:52,760
Speaker 1: he he changed less between the debates that he actually

545
00:36:52,760 --> 00:36:56,920
Speaker 1: participated in then the debate that he didn't participate in,

546
00:36:57,040 --> 00:37:00,720
Speaker 1: because and that makes sense to me, because there's nothing

547
00:37:00,800 --> 00:37:03,759
Speaker 1: you can learn from a Bernie Sanders speech at this

548
00:37:03,840 --> 00:37:06,840
Speaker 1: point if you've been paying attention at all for the

549
00:37:06,840 --> 00:37:10,399
Speaker 1: past six years and I mean part of me, like

550
00:37:11,560 --> 00:37:14,839
Speaker 1: I I like that because there's something authentic about that

551
00:37:14,840 --> 00:37:18,240
Speaker 1: that like, this is what I believe. It's not changing

552
00:37:18,480 --> 00:37:20,960
Speaker 1: no matter how you frame it. This is what I believe.

553
00:37:21,520 --> 00:37:24,319
Speaker 1: But there there was a point during the debate where

554
00:37:24,320 --> 00:37:27,879
Speaker 1: they asked him a question specifically about race, and he

555
00:37:28,719 --> 00:37:32,040
Speaker 1: was like, we gotta change, we gotta go after the corporations.

556
00:37:32,280 --> 00:37:37,080
Speaker 1: And it's just like is because I'm struggling with like

557
00:37:37,800 --> 00:37:41,799
Speaker 1: who I support and uh, if he's the president of

558
00:37:41,800 --> 00:37:44,960
Speaker 1: the United States, it's a complicated job, I guess, So

559
00:37:45,040 --> 00:37:50,960
Speaker 1: like it's authentic to be that ideologically like centered and

560
00:37:51,239 --> 00:37:54,719
Speaker 1: you know, have that same answer and that lens that

561
00:37:54,760 --> 00:37:59,600
Speaker 1: you view everything through. But if there is a racial crisis,

562
00:37:59,600 --> 00:38:02,200
Speaker 1: which they is an ongoing one in the country with

563
00:38:02,480 --> 00:38:05,719
Speaker 1: you know, cops shooting people of color, or if there

564
00:38:05,800 --> 00:38:10,160
Speaker 1: is a national security crisis, like does he's just like,

565
00:38:10,200 --> 00:38:13,359
Speaker 1: we have to go after the corporation. Like that's not

566
00:38:13,640 --> 00:38:18,480
Speaker 1: quite the response to that particular problem, Bernie, So we

567
00:38:18,480 --> 00:38:24,360
Speaker 1: will see what I think. So that that his uh,

568
00:38:24,600 --> 00:38:29,000
Speaker 1: like numbers changed more in the debate that he didn't participation.

569
00:38:29,320 --> 00:38:33,439
Speaker 1: That means that, like I guess, people had like we're

570
00:38:33,480 --> 00:38:36,080
Speaker 1: favoring two candidates and then they were like, oh, well,

571
00:38:36,120 --> 00:38:39,160
Speaker 1: I didn't like how that person did in that first debate.

572
00:38:39,200 --> 00:38:43,000
Speaker 1: I guess I'll switch to Bernie, Like, well I was

573
00:38:43,120 --> 00:38:47,879
Speaker 1: on Bernie, Yeah, now I'm back something like that. Yeah, yeah,

574
00:38:48,040 --> 00:38:51,680
Speaker 1: that is funny though. All right, we're gonna take a

575
00:38:51,760 --> 00:38:53,959
Speaker 1: quick break and then we'll be back to talk about

576
00:38:54,000 --> 00:38:56,399
Speaker 1: the Straight Pride Pride. I don't think we forgot about

577
00:38:56,400 --> 00:39:11,200
Speaker 1: you straight guys. Yeah, and we're back and the Straight

578
00:39:11,280 --> 00:39:15,879
Speaker 1: Pride Parade is back in the news. I had kind

579
00:39:15,920 --> 00:39:18,200
Speaker 1: of assumed I didn't want to talk too much about

580
00:39:18,200 --> 00:39:20,279
Speaker 1: this because it seemed like it could be one of

581
00:39:20,320 --> 00:39:25,040
Speaker 1: those things like the stunt the parade. The Grand Marshal

582
00:39:25,080 --> 00:39:30,520
Speaker 1: of the parade is Milounopolis, who is a uh you know,

583
00:39:30,600 --> 00:39:37,560
Speaker 1: he's a troll icon everyone's favorite. Yeah, but he's he's

584
00:39:37,560 --> 00:39:41,520
Speaker 1: a troll who like will announce an appearance. And he

585
00:39:41,560 --> 00:39:43,080
Speaker 1: did that thing where he was like going to go

586
00:39:43,120 --> 00:39:46,400
Speaker 1: to Stanford and take over campus and people are like,

587
00:39:46,480 --> 00:39:49,880
Speaker 1: please don't or maybe it was Berkeley, and then uh,

588
00:39:50,120 --> 00:39:52,200
Speaker 1: you know, he there was a bunch of controversy and

589
00:39:52,239 --> 00:39:54,360
Speaker 1: then he never ended up doing it because he was

590
00:39:54,400 --> 00:39:58,880
Speaker 1: just there too. Rack up some mentions. Um, but it

591
00:39:59,000 --> 00:40:01,799
Speaker 1: does seem like this is going forward. Yes, it does,

592
00:40:02,160 --> 00:40:07,680
Speaker 1: because the City of Boston officials they're approved the application

593
00:40:07,960 --> 00:40:13,399
Speaker 1: that would allow a group to hold the straight Pride parade. Um.

594
00:40:13,440 --> 00:40:20,520
Speaker 1: The group is called super Happy Fun America. So that

595
00:40:20,520 --> 00:40:24,600
Speaker 1: they clearly are disproving the idea that straight people don't

596
00:40:24,640 --> 00:40:27,560
Speaker 1: have a way with language, because those are very I

597
00:40:27,600 --> 00:40:31,560
Speaker 1: mean that that they know dozens of words in the

598
00:40:31,600 --> 00:40:38,880
Speaker 1: English language, super Happy Fun America. I sure, okay? Or

599
00:40:38,960 --> 00:40:42,680
Speaker 1: is that what they think it gay people represented like

600
00:40:42,800 --> 00:40:47,799
Speaker 1: super Happy Fun there it's too yeah, or this is

601
00:40:47,840 --> 00:40:50,799
Speaker 1: their attempt to compete with well, because I would say

602
00:40:50,840 --> 00:40:54,240
Speaker 1: if you did straight pride, it would be like flannel khaki,

603
00:40:55,080 --> 00:40:58,080
Speaker 1: the opposite of super happy and right right, it would

604
00:40:58,080 --> 00:41:03,000
Speaker 1: be extremely boring, medioc or out KACKI yeah, events No

605
00:41:04,680 --> 00:41:09,359
Speaker 1: people are terrible, mediocre might be beyond their right, right,

606
00:41:09,520 --> 00:41:12,600
Speaker 1: right right. So this is an event that is meant to,

607
00:41:12,840 --> 00:41:16,759
Speaker 1: of course, counter the City of Boston's and many other

608
00:41:16,760 --> 00:41:19,880
Speaker 1: cities across the country l g B t Q plus

609
00:41:19,920 --> 00:41:25,319
Speaker 1: Pride parade that happens every year. Why why why do

610
00:41:25,440 --> 00:41:30,040
Speaker 1: black people get a e t entertainment channel? Why do yeah,

611
00:41:30,400 --> 00:41:34,520
Speaker 1: you name it yes. So. The president of this group,

612
00:41:34,680 --> 00:41:40,080
Speaker 1: Super Happy Fun America, John Hugo, submitted the application and

613
00:41:40,400 --> 00:41:44,879
Speaker 1: this lated date for the parade is August thirty one,

614
00:41:45,920 --> 00:41:51,640
Speaker 1: which is straight pride months. August. Yes, I would say

615
00:41:51,640 --> 00:41:56,000
Speaker 1: the worst months of the year in America. Yeah, easily,

616
00:41:56,239 --> 00:41:58,719
Speaker 1: Like the worst movies come out in August. I feel

617
00:41:58,760 --> 00:42:03,080
Speaker 1: like it's just every everyone's like they're no good holidays

618
00:42:03,120 --> 00:42:06,640
Speaker 1: in August. Yeah. So, I mean sorry if you know

619
00:42:06,680 --> 00:42:11,200
Speaker 1: your birthdays in August, but it is. Yeah, it's great.

620
00:42:11,320 --> 00:42:13,960
Speaker 1: But people were born in the month are great. Yes, agree,

621
00:42:14,200 --> 00:42:17,560
Speaker 1: my mom, your mom should have held it, Jamie Loftus.

622
00:42:17,600 --> 00:42:21,640
Speaker 1: Of course it was born augusta. My sister is August.

623
00:42:21,760 --> 00:42:26,759
Speaker 1: Butack Obama, Oh yeah, is he August? Yeah, well I

624
00:42:26,800 --> 00:42:30,399
Speaker 1: think so. Yeah. I just hope he's not a great guy.

625
00:42:30,560 --> 00:42:34,920
Speaker 1: It was amazing. Sorry. June uh the special guest co

626
00:42:35,040 --> 00:42:41,719
Speaker 1: host from last Friday's episode guest Mary and Williamson's astrological sign,

627
00:42:42,040 --> 00:42:47,040
Speaker 1: what is it? Cancer? Cancer? Oh yeah? She was like, yeah,

628
00:42:47,280 --> 00:42:53,760
Speaker 1: of course, right, so now I believe in mention. Uh so,

629
00:42:54,280 --> 00:43:00,680
Speaker 1: what what can we do to uh participate? No? Want to?

630
00:43:00,400 --> 00:43:03,000
Speaker 1: It will happen? Do you think August that it will

631
00:43:03,040 --> 00:43:07,080
Speaker 1: go down. I certainly hope not. But so everyone's like, why,

632
00:43:07,360 --> 00:43:10,880
Speaker 1: like Boston, why are you letting this happen? And the Mayor,

633
00:43:11,000 --> 00:43:16,319
Speaker 1: Marty Walsh, said that Boston cannot deny super Happy Fund

634
00:43:16,360 --> 00:43:20,680
Speaker 1: America's public event application even though they disagree with the

635
00:43:20,800 --> 00:43:27,160
Speaker 1: organizers values and or beliefs. Um quote. Permits to host

636
00:43:27,239 --> 00:43:30,480
Speaker 1: a public event are granted based on operational feasibility, not

637
00:43:30,560 --> 00:43:33,319
Speaker 1: based on values or endorsements or beliefs. The City of

638
00:43:33,360 --> 00:43:36,719
Speaker 1: Boston cannot deny a permit based on an organization's values.

639
00:43:37,200 --> 00:43:39,319
Speaker 1: This is what he tweeted on June six, and then

640
00:43:39,320 --> 00:43:42,719
Speaker 1: he added whatever outside groups may try to do, our

641
00:43:42,800 --> 00:43:45,120
Speaker 1: values won't change. I invite each and every person to

642
00:43:45,160 --> 00:43:47,760
Speaker 1: stand with us and show that love will always prevail.

643
00:43:47,840 --> 00:43:51,920
Speaker 1: So he's basically saying like, well, just because we recognize

644
00:43:51,960 --> 00:43:54,399
Speaker 1: that these people are a bunch of idiots who also

645
00:43:54,480 --> 00:43:58,600
Speaker 1: have ties to uh, you know, all right groups, why supremacists,

646
00:43:58,600 --> 00:44:02,160
Speaker 1: all this stuff, we you still have to allow them.

647
00:44:02,200 --> 00:44:04,839
Speaker 1: You know, they're right to assemble. I feel like there

648
00:44:04,840 --> 00:44:08,760
Speaker 1: should be some sort of like hate group exception, though

649
00:44:09,120 --> 00:44:12,120
Speaker 1: you know, I don't know why that's not a thing,

650
00:44:12,840 --> 00:44:15,640
Speaker 1: but I mean that people like I guess what you know,

651
00:44:15,680 --> 00:44:21,000
Speaker 1: the KKK has been assembling legally for a while. It's

652
00:44:21,080 --> 00:44:26,360
Speaker 1: just I guess it'll be another opportunity to get pictures

653
00:44:26,400 --> 00:44:32,320
Speaker 1: of these people fired by publicly sanding them from their jobs,

654
00:44:32,600 --> 00:44:34,839
Speaker 1: which I mean, then we have to think about it's

655
00:44:34,880 --> 00:44:38,600
Speaker 1: like this, who where are they working? Right? Where's there

656
00:44:38,680 --> 00:44:40,520
Speaker 1: nine to five? Well, but I think a lot of

657
00:44:40,560 --> 00:44:44,320
Speaker 1: people I remember during the Unite the Right rally, people

658
00:44:44,320 --> 00:44:46,800
Speaker 1: were like I didn't know, Like, yeah, he seemed like

659
00:44:46,880 --> 00:44:49,520
Speaker 1: an asshole, but I didn't know he was a white supremacist.

660
00:44:50,239 --> 00:44:53,520
Speaker 1: There are yeah, there's a lot of woodworkers right inside

661
00:44:53,560 --> 00:44:58,160
Speaker 1: of the woodwork come out. But Boston was the site

662
00:44:58,280 --> 00:45:03,680
Speaker 1: of the like allow up to the Unite the Right rally,

663
00:45:03,719 --> 00:45:08,799
Speaker 1: which like almost no one right poorly attended, and that's

664
00:45:08,840 --> 00:45:12,400
Speaker 1: that's a that's a insult, the poorly attended Unite the

665
00:45:12,520 --> 00:45:17,359
Speaker 1: Right festival right exactly. So maybe I could see this

666
00:45:18,080 --> 00:45:22,480
Speaker 1: going any number of ways. I imagine the attendance wouldn't

667
00:45:22,480 --> 00:45:24,600
Speaker 1: be great if they're able to go through with it.

668
00:45:24,680 --> 00:45:29,120
Speaker 1: I mean, like just imagine what what would even be

669
00:45:29,200 --> 00:45:32,000
Speaker 1: happening at this parade? Like what That's what I was

670
00:45:32,040 --> 00:45:34,560
Speaker 1: going to say. It wouldn't be super happy fun, and

671
00:45:34,600 --> 00:45:38,520
Speaker 1: there are going to be no corporate sponsors, which we have. Now,

672
00:45:39,120 --> 00:45:51,319
Speaker 1: what what are straight specific products? We're like, yeah, right,

673
00:45:51,960 --> 00:45:55,160
Speaker 1: like what is the unicorn? I guess like cock rock

674
00:45:55,640 --> 00:45:58,239
Speaker 1: is that? But like that's oh you know what is

675
00:45:58,600 --> 00:46:05,280
Speaker 1: uh skull skull. Yeah it's very straight. Yeah, yeah, skull presents.

676
00:46:06,120 --> 00:46:14,919
Speaker 1: But I don't know. Yeah that's true. Yeah, I think

677
00:46:15,040 --> 00:46:20,080
Speaker 1: Bonobos Bobos. Oh, come on, it's a good brand. I'm

678
00:46:20,120 --> 00:46:25,920
Speaker 1: just saying it's straight. Yeah, okay, fine, it's straight. I'm

679
00:46:25,920 --> 00:46:31,160
Speaker 1: sure plenty of men. Although crocs are fashioned, now that's true.

680
00:46:31,920 --> 00:46:35,760
Speaker 1: Oakley is a really Oakley is with baseball cap and beard.

681
00:46:36,040 --> 00:46:41,839
Speaker 1: It's a shirt that's like, oh, hunting things like all hunting. Yeah, tactical. Yeah,

682
00:46:42,160 --> 00:46:46,120
Speaker 1: a shirt that's like not like touching me. It's just

683
00:46:46,400 --> 00:46:49,600
Speaker 1: check fil A still hate. Yeah. As someone in Atlanta

684
00:46:49,719 --> 00:46:52,359
Speaker 1: said to me recently, you know, Chick fil A hates

685
00:46:52,400 --> 00:46:54,839
Speaker 1: the lb G T S. I'm like, yes, they do.

686
00:46:55,480 --> 00:47:02,000
Speaker 1: Someone said that, yes, well not queer people though, yeah.

687
00:47:02,200 --> 00:47:12,120
Speaker 1: Now yeah the UM so the group's slogan happy Fun

688
00:47:12,160 --> 00:47:14,920
Speaker 1: America or whatever. Then they're called um. Their slogan is

689
00:47:15,000 --> 00:47:24,439
Speaker 1: it's great to be straight, whereas it pays to be days. Uh,

690
00:47:24,480 --> 00:47:29,080
Speaker 1: and they claim that straight people are an oppressed majority

691
00:47:29,160 --> 00:47:32,919
Speaker 1: and that they should be included as equals among all

692
00:47:32,960 --> 00:47:39,160
Speaker 1: of the other orientations. So that's where they're coming from. Uh.

693
00:47:40,480 --> 00:47:43,960
Speaker 1: I guess they're saying, I don't know why people need

694
00:47:44,000 --> 00:47:47,799
Speaker 1: to be constantly reminded of this, But you know, when

695
00:47:47,840 --> 00:47:52,480
Speaker 1: you are the oppressor and you are oppressing marginalized groups,

696
00:47:52,520 --> 00:47:57,200
Speaker 1: that does not also mean that you are somehow oppressed yourself.

697
00:47:58,160 --> 00:48:01,480
Speaker 1: I don't. I do think this idea of straight visibility

698
00:48:01,600 --> 00:48:06,160
Speaker 1: is very fine. Yeah, it's like open your open your eyes,

699
00:48:06,920 --> 00:48:12,879
Speaker 1: there's there. It's visible. Yeah, it's it's everywhere that it's ubiquitous. Yeah.

700
00:48:12,920 --> 00:48:17,160
Speaker 1: I'm trying to think of like a reverse Becdel test

701
00:48:17,440 --> 00:48:21,320
Speaker 1: that you could for two straight people, try and find

702
00:48:21,360 --> 00:48:26,000
Speaker 1: a movie that in any way discriminates, like, doesn't contain

703
00:48:26,960 --> 00:48:31,960
Speaker 1: straight people, discriminates against straight people. Are just like one

704
00:48:32,160 --> 00:48:35,399
Speaker 1: cultural example, like there, their examples are going to be like,

705
00:48:35,680 --> 00:48:39,960
Speaker 1: don't put that stuff in my face. Man, you're setting

706
00:48:39,960 --> 00:48:44,600
Speaker 1: a bad example from my food. Thanks for everything, Julie

707
00:48:44,680 --> 00:48:50,520
Speaker 1: newmar Right, get that out of here. Huh. Yeah, that's

708
00:48:50,560 --> 00:48:54,680
Speaker 1: probably the But the fact that they think that's the norm,

709
00:48:54,840 --> 00:49:00,520
Speaker 1: it's interesting not really Actually, it's very boring. It's always well, no,

710
00:49:00,640 --> 00:49:03,960
Speaker 1: it's not interesting all. You're right, but it is curious,

711
00:49:04,640 --> 00:49:10,520
Speaker 1: that's right that they feel as though the the scales

712
00:49:10,600 --> 00:49:15,560
Speaker 1: have tipped in the favor of the l g B,

713
00:49:15,760 --> 00:49:21,440
Speaker 1: t q I a community. It's just a a sensitivity

714
00:49:21,520 --> 00:49:28,640
Speaker 1: where they feel like any time somebody takes pride in

715
00:49:29,800 --> 00:49:35,080
Speaker 1: anything that doesn't include them, that they being discriminated, being

716
00:49:35,080 --> 00:49:39,000
Speaker 1: discriminated against. Anytime somebody says, Doug, please don't say that

717
00:49:39,080 --> 00:49:42,919
Speaker 1: word in front of me, they are like, wait, I'm

718
00:49:42,920 --> 00:49:46,120
Speaker 1: going to come from outside of the room to like

719
00:49:46,719 --> 00:49:48,960
Speaker 1: argue that I should be able to say that word,

720
00:49:49,000 --> 00:49:52,680
Speaker 1: even though I wasn't involved in Um, yeah, I I know.

721
00:49:53,640 --> 00:49:57,000
Speaker 1: I know people like that. So we're all gonna go

722
00:49:57,239 --> 00:50:02,680
Speaker 1: right August thirty one? Should we? Honestly, I'll be in

723
00:50:02,760 --> 00:50:07,920
Speaker 1: Boston like two weeks before that. That is when I

724
00:50:08,000 --> 00:50:10,319
Speaker 1: missed working for huff Posts. Maybe they would have let

725
00:50:10,400 --> 00:50:14,359
Speaker 1: me go just to cover yeah, to cover it. That's

726
00:50:14,400 --> 00:50:18,879
Speaker 1: the thing. It's like a fun gay A fun gay

727
00:50:18,880 --> 00:50:21,799
Speaker 1: person would never want to even go No, why would

728
00:50:21,800 --> 00:50:25,279
Speaker 1: you want to go to that? Which sounds terrible? Like

729
00:50:25,320 --> 00:50:28,200
Speaker 1: what music would It's just a gathering of white men.

730
00:50:29,320 --> 00:50:32,840
Speaker 1: That's every other parade. Go to the Veterans Day Parade.

731
00:50:33,120 --> 00:50:37,239
Speaker 1: The Dave Matthews band. That's a horribly it's a Dave

732
00:50:37,280 --> 00:50:44,560
Speaker 1: Matthews concert. Is that like, Yeah, there's nothing like Edge. Yeah,

733
00:50:44,840 --> 00:50:49,200
Speaker 1: Like Dave Matthews is like brought to you by Cargo

734
00:50:49,280 --> 00:50:57,439
Speaker 1: Shorts Straight pre parade and Cargo Shorts. Um. New York

735
00:50:57,520 --> 00:51:01,520
Speaker 1: City has declared a climate emergency. It really speaks to

736
00:51:01,560 --> 00:51:04,360
Speaker 1: why it's an emergency that I didn't know that they declared.

737
00:51:07,960 --> 00:51:13,520
Speaker 1: That's I was like, oh, ship, so what what specifically

738
00:51:13,560 --> 00:51:17,759
Speaker 1: are they declaring? So they're you know, they've declared this

739
00:51:18,200 --> 00:51:23,120
Speaker 1: climate emergency. They're calling for an immediate response to the

740
00:51:23,160 --> 00:51:28,719
Speaker 1: global climate crises. There's more than one crisis. There's crises

741
00:51:28,800 --> 00:51:32,800
Speaker 1: out there. Uh, they've New York City Council has passed

742
00:51:32,800 --> 00:51:39,080
Speaker 1: this legislation on June TWI, so it was a few

743
00:51:39,160 --> 00:51:43,799
Speaker 1: days ago. UM. The lawmakers wrote, the United States of

744
00:51:43,800 --> 00:51:47,080
Speaker 1: America has disproportionately contributed to the climate emergency and has

745
00:51:47,120 --> 00:51:50,719
Speaker 1: repeatedly obstructed global efforts to transition towards a green economy,

746
00:51:51,040 --> 00:51:55,600
Speaker 1: and thus bears an extraordinary responsibility to rapidly address these

747
00:51:55,800 --> 00:52:00,000
Speaker 1: existential threats. We already know all of this stuff. Um,

748
00:52:00,080 --> 00:52:03,919
Speaker 1: so basically climate emergencies like this one that New York

749
00:52:03,960 --> 00:52:07,399
Speaker 1: just declared. New York City just declared UH function as

750
00:52:07,440 --> 00:52:10,640
Speaker 1: a symbol of the commitment to fight climate change, which

751
00:52:10,719 --> 00:52:16,000
Speaker 1: with future legislation, rather than actually including any specific policy

752
00:52:16,040 --> 00:52:19,839
Speaker 1: measures on what will actually be done to slow climate change.

753
00:52:19,840 --> 00:52:22,640
Speaker 1: So it's basically just saying like, hey, everyone calling your

754
00:52:22,680 --> 00:52:26,799
Speaker 1: attention to this, which like sure, I guess that step one.

755
00:52:27,280 --> 00:52:32,359
Speaker 1: But the fact that like climate change is happening at

756
00:52:32,480 --> 00:52:37,719
Speaker 1: such a more rapid pace than climate scientists had even

757
00:52:37,840 --> 00:52:42,759
Speaker 1: previously anticipated, and like every time anyone comes out with

758
00:52:42,760 --> 00:52:47,000
Speaker 1: a study there, you know, the results are very alarming.

759
00:52:47,040 --> 00:52:49,920
Speaker 1: It's like, oh, the you know, the permafrost is melting

760
00:52:50,400 --> 00:52:52,839
Speaker 1: at a bazillion times faster than we thought. Like all,

761
00:52:53,040 --> 00:52:56,279
Speaker 1: you know, like the the accelerated change is happening is

762
00:52:56,400 --> 00:53:00,959
Speaker 1: very very alarming, and it just feels like everyone else

763
00:53:01,040 --> 00:53:04,040
Speaker 1: is so slow to catch up. We're like, Okay, we're gonna,

764
00:53:04,120 --> 00:53:07,040
Speaker 1: you know, say that we need to do stuff to

765
00:53:07,120 --> 00:53:12,680
Speaker 1: change things, and we're gonna you know, declare a climate emergency,

766
00:53:12,800 --> 00:53:15,640
Speaker 1: but like you know, we're just putting it. You know,

767
00:53:15,719 --> 00:53:17,680
Speaker 1: that's kind of on the back burner for when we

768
00:53:18,200 --> 00:53:22,200
Speaker 1: actually do something about it. Yeah. It reminds me of

769
00:53:22,880 --> 00:53:26,960
Speaker 1: like a meeting that somebody calls without putting any thought

770
00:53:27,040 --> 00:53:31,040
Speaker 1: into it, Like a meeting that a one sentence email. Right,

771
00:53:31,239 --> 00:53:37,359
Speaker 1: we're saying it's bad. Yea, so what we already knew

772
00:53:37,400 --> 00:53:41,080
Speaker 1: that decades ago. So can you please like actually enact

773
00:53:41,239 --> 00:53:45,759
Speaker 1: change to do something to Blasi? Is it, you know,

774
00:53:45,800 --> 00:53:50,000
Speaker 1: out to lunch anyway? Right? De Blasio is trying to

775
00:53:50,040 --> 00:53:54,319
Speaker 1: figure out if it's problematic to say he looks like

776
00:53:54,360 --> 00:53:58,680
Speaker 1: a rat because he does. Well you are the company

777
00:53:58,719 --> 00:54:04,680
Speaker 1: you keep right, Yeah, exactly New York City. Um, but yeah,

778
00:54:04,840 --> 00:54:07,720
Speaker 1: I don't know. I don't think it is. I've decided

779
00:54:07,760 --> 00:54:11,000
Speaker 1: it's not problematic. You let me know if it is problematic.

780
00:54:11,000 --> 00:54:14,520
Speaker 1: I know it's like an anti Semitic trope, but uh,

781
00:54:14,560 --> 00:54:20,480
Speaker 1: he is not. Well, you know he did he did

782
00:54:20,520 --> 00:54:23,080
Speaker 1: something I guess, which was to declare this. I wonder

783
00:54:23,120 --> 00:54:25,359
Speaker 1: how much of this is him just like looking for

784
00:54:25,400 --> 00:54:29,479
Speaker 1: like a cheap media pop. Oh could be. Yeah, Well,

785
00:54:29,680 --> 00:54:35,240
Speaker 1: here's some more hot facts if you'll let me. Yeah, yeah,

786
00:54:35,280 --> 00:54:39,480
Speaker 1: these facts are sounds super hot. They're very hot. According

787
00:54:39,560 --> 00:54:46,000
Speaker 1: to data from the Innovation Innovation for Cool Earth Forum

788
00:54:46,120 --> 00:54:50,760
Speaker 1: UM cool Earth so like happy Fun America but anyway.

789
00:54:51,520 --> 00:54:55,080
Speaker 1: More than six seventy governments in fifteen different countries have

790
00:54:55,160 --> 00:54:59,799
Speaker 1: declared these climate emergencies, but only eighteen of those local governments,

791
00:55:00,160 --> 00:55:03,279
Speaker 1: not including New York, are in the US, So the

792
00:55:03,400 --> 00:55:06,799
Speaker 1: U s seems to be you know, not pulling its

793
00:55:06,800 --> 00:55:11,360
Speaker 1: weight in terms of climate emergencies. UM. San Francisco and Hoboken,

794
00:55:11,400 --> 00:55:15,279
Speaker 1: New Jersey have been some of the few to declare

795
00:55:15,360 --> 00:55:20,359
Speaker 1: these climate emergencies, while Los Angeles, where we are right now,

796
00:55:20,800 --> 00:55:24,920
Speaker 1: believe it or not ever heard of it, UM has

797
00:55:25,040 --> 00:55:29,359
Speaker 1: yet to formally declare an emergency, but is included in

798
00:55:29,680 --> 00:55:33,880
Speaker 1: the forums count for initiatives the council members will have

799
00:55:34,040 --> 00:55:37,400
Speaker 1: to UH that have made to combat climate change. I

800
00:55:37,440 --> 00:55:41,400
Speaker 1: barely understood what I just read, but UM, basically, basically,

801
00:55:41,520 --> 00:55:44,320
Speaker 1: you know, the US as always is, you know, doing

802
00:55:44,360 --> 00:55:46,920
Speaker 1: a lot to contribute to climate change and then not

803
00:55:47,040 --> 00:55:51,600
Speaker 1: taking any accountability for it or not enough. So I

804
00:55:51,640 --> 00:55:55,120
Speaker 1: guess New York declaration of this you know emergency is

805
00:55:55,120 --> 00:55:58,879
Speaker 1: significant just because of its sheer scope of the constituency there.

806
00:55:59,000 --> 00:56:00,560
Speaker 1: It's because it's you know, the are just sitting in

807
00:56:00,600 --> 00:56:06,920
Speaker 1: the US. UM. But even so, like I said before, sure,

808
00:56:07,040 --> 00:56:10,760
Speaker 1: let's declare an emergency, but what is it doing. Remember

809
00:56:10,760 --> 00:56:13,759
Speaker 1: when Pittsburgh when Trump pulled the U S out of

810
00:56:13,800 --> 00:56:16,239
Speaker 1: the Climate Accord and then Pittsburgh was like, we're going

811
00:56:16,280 --> 00:56:19,120
Speaker 1: to stick to the Climate Accord. Did they really good

812
00:56:19,120 --> 00:56:23,279
Speaker 1: for Pittsburgh? Well, they are very techno focused. Yeah, they

813
00:56:23,320 --> 00:56:30,120
Speaker 1: really are. Carnegie Mellon is really underrated learning institute. That's

814
00:56:30,120 --> 00:56:32,759
Speaker 1: what I should have said for underrated Carnegie if I

815
00:56:32,880 --> 00:56:37,720
Speaker 1: came out like swinging was like Carnegie Melon. Yeah. Also

816
00:56:37,840 --> 00:56:44,600
Speaker 1: good acting school, Oh yes, I've heard. But conservatory. Yeah,

817
00:56:44,680 --> 00:56:47,600
Speaker 1: I I feel like I don't know. I don't know

818
00:56:47,680 --> 00:56:51,359
Speaker 1: why all local governments that are, you know, run by

819
00:56:51,360 --> 00:56:55,040
Speaker 1: Democrats aren't doing that. Yeah, like just being like, well

820
00:56:55,080 --> 00:56:58,120
Speaker 1: we're sticking to the rules. Um, Because again, I guess

821
00:56:58,120 --> 00:57:00,759
Speaker 1: it's the first step because it's to say, oh, we

822
00:57:00,840 --> 00:57:05,520
Speaker 1: need to do something about this. But like being in asylums, right,

823
00:57:05,560 --> 00:57:08,279
Speaker 1: all you need to do is declare an emergency. It

824
00:57:08,280 --> 00:57:10,719
Speaker 1: sounds pretty so, I mean, I'm sure there's you know,

825
00:57:10,920 --> 00:57:14,600
Speaker 1: writing some some legislation, but oh yeah that does sound hard.

826
00:57:14,840 --> 00:57:19,720
Speaker 1: Sounds so it's just chool. I gotta I gotta do

827
00:57:19,880 --> 00:57:23,040
Speaker 1: my job as a lawmaker. To write some legislation. I

828
00:57:23,120 --> 00:57:27,120
Speaker 1: can't believe this. You know that sounds like a lot, Kara,

829
00:57:27,280 --> 00:57:33,720
Speaker 1: it has been so fun having you. I love your brieves.

830
00:57:34,160 --> 00:57:36,840
Speaker 1: Where can people find you? Follow you to listen to you.

831
00:57:37,160 --> 00:57:42,560
Speaker 1: People can find Sleepwalkers podcast on the I Heart Radio app,

832
00:57:43,360 --> 00:57:48,760
Speaker 1: Apple Podcasts, Spotify, or wherever you listen to podcasts. Actually,

833
00:57:48,840 --> 00:57:52,040
Speaker 1: I should say I'll give a little plug. We launched.

834
00:57:52,080 --> 00:57:56,680
Speaker 1: We put out episode nine on Thursday. Our finale, Season

835
00:57:56,680 --> 00:58:04,040
Speaker 1: one finale comes out next Thursday. Four Celebrate Nation, Happy Fun,

836
00:58:04,120 --> 00:58:09,040
Speaker 1: super cool America. Yes, with you've all. Noah Harare is

837
00:58:09,040 --> 00:58:16,680
Speaker 1: our finale guest. Yeah, that's right, that's right. Um he's

838
00:58:16,680 --> 00:58:23,960
Speaker 1: had a gay man outa door. Now I get it. Oh,

839
00:58:24,000 --> 00:58:27,280
Speaker 1: I get it. Yeah. Uh. And is there a tweet

840
00:58:27,280 --> 00:58:32,400
Speaker 1: you've been enjoying? Oh yeah, yeah, there's two tweets. There's

841
00:58:32,440 --> 00:58:36,720
Speaker 1: a there's a girl named whatever. I'm not going to

842
00:58:36,800 --> 00:58:41,200
Speaker 1: say her handle all Titiano McGrath, but she made a

843
00:58:41,200 --> 00:58:43,880
Speaker 1: trolley tweet that was anyone who was fathered by a

844
00:58:43,880 --> 00:58:46,320
Speaker 1: male as a byproduct the patriarchy and should be ashamed

845
00:58:46,360 --> 00:58:50,080
Speaker 1: of themselves. But Luandy less Epp said something that I

846
00:58:50,280 --> 00:58:52,760
Speaker 1: who's on Real House Lives of New York City said

847
00:58:52,800 --> 00:58:57,160
Speaker 1: something really I thought profound, which was you don't help

848
00:58:57,280 --> 00:59:04,760
Speaker 1: someone and throw it in their face. Yeah, really good point. Yeah. Yeah,

849
00:59:05,120 --> 00:59:07,320
Speaker 1: So that's a tweet that I'm really that I thought

850
00:59:07,400 --> 00:59:15,400
Speaker 1: was good man. Yeah. And her new singles out Feeling Giovanni, Yeah, good,

851
00:59:15,520 --> 00:59:18,400
Speaker 1: which is a What's incredible about Bravo the Bravo verse

852
00:59:18,560 --> 00:59:20,960
Speaker 1: is that it's a complete it's everything in it is

853
00:59:20,960 --> 00:59:24,600
Speaker 1: a meme. Giovanni is a clothing store in Beverly Hills,

854
00:59:25,400 --> 00:59:30,000
Speaker 1: and it's like not a thing, but she's decided to

855
00:59:30,320 --> 00:59:34,760
Speaker 1: put out a song called Feeling Jovanni. I guess Jovanni

856
00:59:34,920 --> 00:59:38,200
Speaker 1: is whatever you want it to be. Yea um, but

857
00:59:38,240 --> 00:59:40,600
Speaker 1: I mean it's a state of mind. If she's creating

858
00:59:40,680 --> 00:59:45,160
Speaker 1: industry around a clothing store, which just blows my genius.

859
00:59:45,280 --> 00:59:50,800
Speaker 1: It's great branded content. Caitlyn, Where can people find you?

860
00:59:50,800 --> 00:59:55,760
Speaker 1: You can follow me on Instagram, Twitter, my website at

861
00:59:56,120 --> 01:00:01,400
Speaker 1: Caitlin Durante. You can listen to my podcast right here

862
01:00:01,520 --> 01:00:06,440
Speaker 1: on the I Heart Radio How Stuff works Etter. Still

863
01:00:06,480 --> 01:00:10,120
Speaker 1: don't know what we're called network, It's called the Bechtel

864
01:00:10,160 --> 01:00:15,240
Speaker 1: Cast and uh my co host Jamie Loftus and I

865
01:00:15,280 --> 01:00:18,200
Speaker 1: discussed the representation of women in movies and how it's

866
01:00:18,200 --> 01:00:21,480
Speaker 1: almost always bad. You're taking down the patriarchy one film

867
01:00:21,520 --> 01:00:24,760
Speaker 1: at a time. That's right, right, um? And is there

868
01:00:24,760 --> 01:00:28,000
Speaker 1: a tweet you've been enjoying well? Speaking of Jamie Loftus,

869
01:00:28,040 --> 01:00:31,000
Speaker 1: she tweeted a few days ago, thrilled to announce that

870
01:00:31,040 --> 01:00:33,480
Speaker 1: I made it seven days into a new job to

871
01:00:33,760 --> 01:00:41,600
Speaker 1: aggressively suggest we cast Alfred Molina. So that's very funny,

872
01:00:41,760 --> 01:00:46,520
Speaker 1: continuing to stand for Alfred Molina. I love Alfred Molina's

873
01:00:46,560 --> 01:00:49,360
Speaker 1: a lesbian icon. Is he really? I don't know that.

874
01:00:49,400 --> 01:00:53,160
Speaker 1: I don't we know that he's a feminist icon, which

875
01:00:53,200 --> 01:00:56,320
Speaker 1: is something that we've declared on our show many times.

876
01:00:56,360 --> 01:01:01,320
Speaker 1: I adore him. He's great tweet I've been enjoying. Annie

877
01:01:01,880 --> 01:01:06,000
Speaker 1: ian j I x c X tweeted, high school teachers,

878
01:01:06,040 --> 01:01:09,320
Speaker 1: you could be real with history teachers, English teachers, high

879
01:01:09,360 --> 01:01:13,320
Speaker 1: school teachers that were cops, math teachers, jim teachers. I

880
01:01:13,320 --> 01:01:17,120
Speaker 1: identify with that. And then Dave It's cough tweeted, how

881
01:01:17,120 --> 01:01:19,920
Speaker 1: close do you think Beto came to attending fire Festival.

882
01:01:21,920 --> 01:01:26,640
Speaker 1: I think that definitely happened, or almost happened. I just

883
01:01:26,680 --> 01:01:28,280
Speaker 1: saw them. That's pretty good that I would like to

884
01:01:28,720 --> 01:01:35,000
Speaker 1: do it. Marion Williamson bringing powerful, divorced and energy. Yeah,

885
01:01:35,000 --> 01:01:38,600
Speaker 1: there was another uh that I could just keep oh ship,

886
01:01:38,680 --> 01:01:41,680
Speaker 1: I almost forgot so I read one of a great

887
01:01:41,720 --> 01:01:46,000
Speaker 1: old Marion Williamson tweet on last week's episode, But I

888
01:01:46,040 --> 01:01:50,040
Speaker 1: totally miss the Avatar thing. She's tweeted. If you want

889
01:01:50,080 --> 01:01:54,160
Speaker 1: a simple explanation for what's happening in America, watch Avatar again.

890
01:01:54,440 --> 01:01:57,320
Speaker 1: And all the films were good, but Avatar has changed

891
01:01:57,480 --> 01:02:00,240
Speaker 1: the world. He didn't win an Oscar tonight, but James

892
01:02:00,320 --> 01:02:04,960
Speaker 1: Cameron deserves a Nobel Peace Prize. Uh fu. Yeah, I

893
01:02:05,120 --> 01:02:09,800
Speaker 1: love her love. I mean, it's no Titanic, just James Cameron's.

894
01:02:09,960 --> 01:02:14,480
Speaker 1: But it's a full plant based set I've heard come

895
01:02:14,520 --> 01:02:18,040
Speaker 1: full circle. You can find me on Twitter at Jack

896
01:02:18,120 --> 01:02:20,680
Speaker 1: Underscore O'Brien. You can find us on Twitter at Daily's

897
01:02:20,720 --> 01:02:22,880
Speaker 1: like Ice for at the Daily's Like Ice on Instagram.

898
01:02:22,880 --> 01:02:26,240
Speaker 1: We have Facebook fan page on a website, dailiesius dot com,

899
01:02:26,240 --> 01:02:29,200
Speaker 1: where we post our episodes and our foot no not

900
01:02:29,480 --> 01:02:33,320
Speaker 1: where we like off nice to our to the things

901
01:02:33,320 --> 01:02:35,080
Speaker 1: we talked about in the episode, as well as the

902
01:02:35,160 --> 01:02:38,840
Speaker 1: song we ride out on. The producer Aronda hosny A.

903
01:02:38,960 --> 01:02:43,720
Speaker 1: What song are we going to be riding out on today? UM?

904
01:02:44,000 --> 01:02:49,640
Speaker 1: I recommend a song by Devan Banhart's former I Guess

905
01:02:49,720 --> 01:02:54,120
Speaker 1: side project called Mega Push. This song is called Duck

906
01:02:54,200 --> 01:02:58,040
Speaker 1: People duck Man. Uh. It's really good, but it's also

907
01:02:58,120 --> 01:03:01,960
Speaker 1: like pretty funny and as he's been sorry, makes an

908
01:03:01,960 --> 01:03:05,480
Speaker 1: appearance in like a very weird role. Uh. And I

909
01:03:05,520 --> 01:03:08,120
Speaker 1: know he's semi canceled. I don't really know what his

910
01:03:08,200 --> 01:03:10,720
Speaker 1: deal is anymore. I don't think he was. He wasn't canceled,

911
01:03:11,240 --> 01:03:14,920
Speaker 1: okay as much as he should have been. But anyway,

912
01:03:15,000 --> 01:03:17,880
Speaker 1: it's a fun song. Duck People, duck Man buy Mega Place.

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01:03:18,280 --> 01:03:20,320
Speaker 1: All right, we're gonna ride out on that. The Daily

914
01:03:20,400 --> 01:03:22,360
Speaker 1: sit Guys is a production of my Heart Radio for

915
01:03:22,440 --> 01:03:24,560
Speaker 1: more podcasts for my art radio than the I R

916
01:03:24,640 --> 01:03:26,840
Speaker 1: Radio app, Apple podcast or wherever you listening to your

917
01:03:26,880 --> 01:03:29,160
Speaker 1: favorite shows. That's gonna do it for today. We will

918
01:03:29,200 --> 01:03:31,440
Speaker 1: be back tomorrow because it is a daily podcast, and

919
01:03:31,560 --> 01:03:36,280
Speaker 1: we will talk to you guys. Bye bye. Nobody you

920
01:03:36,320 --> 01:03:46,120
Speaker 1: could ever be told you could ever be like Nobody

921
01:03:46,160 --> 01:03:59,400
Speaker 1: has told you. Nobody you could have a Nobody ever

922
01:03:59,520 --> 01:04:04,400
Speaker 1: told you you could ever be like that? Like time

923
01:04:06,120 --> 01:04:09,840
Speaker 1: and you want body ever do He wouldn't know his

924
01:04:10,040 --> 01:04:10,600
Speaker 1: body ever