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Speaker 1: You're listening to Amma Mia podcast.

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Speaker 2: Mamma Mia acknowledges the traditional owners of land and warders

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Speaker 2: that this podcast is recorded on She's ruined my life

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Speaker 2: because I thought I was cool? Well, what do I

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Speaker 2: think that.

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Speaker 3: I was? Like?

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Speaker 2: What am I talking about? Hello and welcome tomorrow. We

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Speaker 2: are out louds news free Friday Show. It's Friday, the

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Speaker 2: twenty eighth of February, and I'm Pollymen, I'm Mere.

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Speaker 4: Friedman, and I'm Jesse Stevens.

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Speaker 2: And on today's show, the three things that have happened

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Speaker 2: to Jesse to convince her, to convince us to talk

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Speaker 2: about the robots, how AI is already changing everybody's lives

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Speaker 2: and often for the better. Also, MIA's new shoes, a

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Speaker 2: bargain beauty game changer, and a cure for picky eaters.

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Speaker 2: It's our recommendations and best and worst of the weeks,

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Speaker 2: which include talking politicians, a boyfriend emergency, and one of

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Speaker 2: us being a right cow. But first, Mere Friedman.

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Speaker 1: Actually missed it. Not all sex talk needs to be dirty.

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Speaker 1: Last week we launched Butter, which is audio erotica.

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Speaker 2: It's going gangbusters right it is. People like it.

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Speaker 1: It's kicking starting the libido of some people who might

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Speaker 1: just be a little bit tired or sluggish in that department.

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Speaker 1: But if you are having sex and you would like

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Speaker 1: to talk dirty, but you don't want to necessarily have

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Speaker 1: a potty mouth, and you don't like swear words, there

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Speaker 1: are a.

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Speaker 4: Lot of people who don't like swear words.

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Speaker 1: Eve.

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Speaker 4: When we talk, sometimes toy I've got kids in the car,

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Speaker 4: or I just don't like it, doesn't.

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Speaker 1: Ca, Well, hopefully you don't have kids in the car

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Speaker 1: when you're having sex.

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Speaker 2: Listening to butter.

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Speaker 1: That's so true. It's a private experience. Thankfully, reddits come

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Speaker 1: to the rescue with the answers. People have provided the

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Speaker 1: words that they might use during sex that aren't swear words.

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Speaker 1: Would you like to hear them? Yes?

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Speaker 4: You in context, and then I can talk back to you.

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Speaker 1: Scrumptious, Oh, heavens sublime.

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Speaker 2: This is like the season on Hurself.

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Speaker 1: Goodness, gracious, I'm approaching the crescendo, my love. Oh, I

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Speaker 1: almost almost almost there. We are there, we are I'll

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Speaker 1: what sorry I should have done? There we are in

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Speaker 1: proper spirit. Make love to me. Oh, if that doesn't

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Speaker 1: kill any boner, I don't know, but lady boners as well.

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Speaker 1: I don't know what would Now there's some others that aren't.

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Speaker 1: I don't know if I want to say, just because

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Speaker 1: you might, you know, I'll just skip those two. Give

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Speaker 1: me everything you have. Take that, Take that, Take that.

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Speaker 2: You could also be fending off an intruder. Don't stop.

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Speaker 1: That's fairly standard. Heck and frick frick.

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Speaker 4: Heck's my favorite? Hey heck?

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Speaker 1: Oh right, that's good. Oh and this is my favorite.

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Speaker 1: Boom goes to dynamite. And if anyone ever has sex

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Speaker 1: again after hearing that list, god lovely.

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Speaker 4: Well, this is what's good is that if you are

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Speaker 4: you know, some people have little kids around, and the

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Speaker 4: walls might be thin, so true, they don't know each

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Speaker 4: a man to say, heck frick, it's like, oh, they're having.

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Speaker 1: A boom goes to dynamite.

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Speaker 4: Talking about dynamite again. I heard some other words, because

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Speaker 4: you know, they might not feel natural to everyone. But

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Speaker 4: may I suggest using the word blimey.

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Speaker 2: Old blimey very good.

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Speaker 4: Shiver me timbers, yeah, and good heavens.

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Speaker 2: I am arriving.

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Speaker 4: You know, I'm riding.

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Speaker 2: You knows told me that when my kids were little,

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Speaker 2: they were like, instead of saying ship around your kids

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Speaker 2: used to say, oh, sharks, sharks could be a good one.

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Speaker 2: And then I'm very English, I was thinking like, golly, gosh,

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Speaker 2: oh gosh, that's jolly good, jolly would what Polly says

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Speaker 2: during sex.

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Speaker 4: The other day, three things happened. The first is that

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Speaker 4: I was speaking to my brother who works in early

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Speaker 4: childhood education, and he told me that he estimated he

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Speaker 4: was saving five hours a week thanks to AI tools

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Speaker 4: that fulfilled a lot of the administrative tasks he used

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Speaker 4: to labor over. They do a lot of like note

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Speaker 4: taking and putting things in like a book that then

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Speaker 4: goes to the parents.

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Speaker 2: And the BLA and that, and a lot of paperworking.

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Speaker 4: Yeah, and even things like reports. Like you think about

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Speaker 4: what AI could do with reports in terms of finishing

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Speaker 4: a sentence and just giving you a general sense of

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Speaker 4: it rather than you're writing them. The second is that

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Speaker 4: I walked into my local GP and she said, I

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Speaker 4: hope you don't mind, but I'm going to use this

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Speaker 4: AI tool to make notes so for our whole consult.

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Speaker 4: She didn't look at a computer. She had something that was.

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Speaker 1: I don't know, listening, because it can be quite disconcerting

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Speaker 1: that I've never thought about that before when you're talking

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Speaker 1: to the GP and you're confessing some deep, dark secret,

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Speaker 1: and the.

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Speaker 2: Look at the computer because what you're always doing at

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Speaker 2: the GPS.

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Speaker 4: And the third thing is that I received a company

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Speaker 4: wide email from my husband with another rocket ship emoji

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Speaker 4: in the subject, and I muttered under my breath, he

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Speaker 4: needs to stop getting AI to write his emails. It's

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Speaker 4: the rocket emojis that are the giveaway, the exclamation marks,

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Speaker 4: the weird ellipses. It's a dead giveaway that your husband's

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Speaker 4: become a bot, which is an issue in our marriage anyway.

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Speaker 4: This led me to the conclusion that this year, Generative

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Speaker 4: AI has entered the workplace in a big way. Not

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Speaker 4: every workplace, but most places are at least talking about

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Speaker 4: how to integrate it to make workers more productive. And two,

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Speaker 4: as the cell goes, save us all more time.

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Speaker 1: Which has freaked some people out. We were all quite

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Speaker 1: freaked out about it, not freaked out, probably wary.

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Speaker 4: Is it good?

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Speaker 1: Well, we ranged maybe a spectrum from freaked out to excited,

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Speaker 1: and wary would probably be in the middle of that.

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Speaker 1: And in the last of months we've been trying to

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Speaker 1: integrate AI more into Mamameha and into the way we

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Speaker 1: do business. And I have to say I was one

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Speaker 1: of those people that had my fingers in my ears

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Speaker 1: and it was a bit la la la la la.

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Speaker 1: I'm freaked out, but now I'm fully into it.

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Speaker 4: I'll show my cards early. I'm freaked because I'm even thinking,

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Speaker 4: like historically about the Industrial Revolution. This isn't the first

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Speaker 4: technology that's disrupted how people work. But if you make

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Speaker 4: workers more productive, doesn't a whole portion of our workforce

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Speaker 4: become redundant. That's what I'm terrified about. We're going to

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Speaker 4: get to that. But it's not just work that AI has,

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Speaker 4: you know, weazled its way into I spoke recently about

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Speaker 4: an AI dating app called a Martyr, where your chatbot

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Speaker 4: is essentially your matchmaker.

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Speaker 1: How do you spell that? Because a lot of people

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Speaker 1: are keen to.

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Speaker 4: Know Amata and apparently it's not live in every Australian state,

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Speaker 4: but I think it's live in Most people are using

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Speaker 4: Generative AI for meal planning, to organize a travel, itinerary,

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Speaker 4: for movie recommendations and gift ideas.

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Speaker 1: So the one most people know is probably chat GPT. Yeah,

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Speaker 1: is that the one you're talking about? Yeah?

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Speaker 4: There are lots of different ones, some you pay for,

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Speaker 4: some you don't, and they do various things. So I

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Speaker 4: think the one that most people would use, especially in

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Speaker 4: their everyday life would be your chat GPT. So a

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Speaker 4: lot of people who work in offices have probably started

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Speaker 4: using that to put together their emails or to help

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Speaker 4: with reports or sort of admin tasks. I want to

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Speaker 4: start by asking you both how you're using it, and

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Speaker 4: then I want to talk about whether we're optimistic or terrified. Maya,

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Speaker 4: you love to run at things with enthusiasm and not

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Speaker 4: a whole lot of foresight. How are you feeling?

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Speaker 1: Yeah, that's so true. I felt really intimidated. I felt

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Speaker 1: like there was this whole thing that I didn't understand

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Speaker 1: and that it was somehow too late to understand it.

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Speaker 2: Could I talk to you for one second on that,

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Speaker 2: because I just think for our if you're listening out

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Speaker 2: there and you're like, I don't really understand it, yes,

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Speaker 2: I think we just need a very simple line about

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Speaker 2: what it is, because basically people are like what everyone's

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Speaker 2: from about AIAI and the latt is it like you're saying,

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Speaker 2: and it is basically right simulation of human intelligence into

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Speaker 2: machines so they can do things that only humans used

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Speaker 2: to be able to do. That's basically what AI is right.

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Speaker 1: And the way that they've been trained, and this is

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Speaker 1: quite controversial, is that they have crawled a huge amount

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Speaker 1: of data on you know, novels, textbooks, documents, reports, news,

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Speaker 1: a huge amount of information creative things, movies, you know,

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Speaker 1: TV shows, scripts, poetry, music, and they're almost facsimiles of

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Speaker 1: people with the ability to learn on their own.

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Speaker 4: With no compensation to the people whose work they have

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Speaker 4: drawn from.

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Speaker 1: Well in some most cases, no, you're right. So in

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Speaker 1: some cases there are big either publishers or media companies

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Speaker 1: that have done deals with AI companies to crawl all

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Speaker 1: of their material that they own. And in other cases

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Speaker 1: we know that things have just been crawled and they're

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Speaker 1: called l elms or large language models, which basically need

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Speaker 1: a huge amount of human created content to learn how

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Speaker 1: to make content like a human.

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Speaker 4: So I've used it recently. I was trying to get

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Speaker 4: across something for work, which was the new South Wales

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Speaker 4: train strike. It's actually really hard to get across it

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Speaker 4: because it's what's being asked by unions. And then this

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Speaker 4: happened at the Fair Work Commission and you can read

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Speaker 4: ten articles and not understand it. But what some of

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Speaker 4: these models will do is you can ask some questions

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Speaker 4: and they'll just answer as though they are your friend

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Speaker 4: who knows everything about it. It's a lot more specific

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Speaker 4: than Google in terms of you going, hang on, what

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Speaker 4: do they want? Why do they want it? Has there

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Speaker 4: been criticism? Did it?

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Speaker 1: Now? We don't use chat GPT for that anymore, do we?

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Speaker 1: You know?

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Speaker 4: Yes, I was using Perplexity for that.

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Speaker 2: Large language models are the ones that will write things

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Speaker 2: for you. They write your emails, they'll write the kids

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Speaker 2: reports that you're talking about, although I would hope not

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Speaker 2: to be honest, but they will do all that kind

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Speaker 2: of stuff. The other kinds of machines are the ones

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Speaker 2: that are great for research. So there are all these

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Speaker 2: different kinds of mes and they're good for different things.

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Speaker 4: So maya, what are you using it for in your

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Speaker 4: everyday life?

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Speaker 1: Yeah, so think of them a little bit like apps

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Speaker 1: that do different things. So I use Perplexity in place

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Speaker 1: of Google.

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Speaker 4: Now it's Perplexity free.

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Speaker 1: Perplexity there's a free model. You can pay for it,

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Speaker 1: but there's a model that allows you to have I

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Speaker 1: think eighty searches a day, and so the difference between

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Speaker 1: Perplexity and Google, for example, is that Google will just

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Speaker 1: give you a list of links on the Internet, and

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Speaker 1: often they'll be the sponsored links at the top, and

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Speaker 1: you have to go into those links to find the information. Now,

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Speaker 1: what Perplexity will do is that you can tell it

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Speaker 1: to look for certain sources, or it will go and

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Speaker 1: look around the internet, summarize and give you the sources

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Speaker 1: that it's gotten information from so you can see and

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Speaker 1: then it will give you the answer, so you don't

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Speaker 1: have to visit all of those different places as you say,

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Speaker 1: and it will paraphrase it, and then you can say,

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Speaker 1: can you give this to me in point form? Can

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Speaker 1: you give me a shorter version of this? And So

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Speaker 1: what I've been using it for is in place of

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Speaker 1: Google to answer questions and also to get myself across

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Speaker 1: complicated topics quickly, because what I like about it is

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Speaker 1: that it will take a number of sources, whereas with

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Speaker 1: Google you have to click on each individual source. And so, yeah,

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Speaker 1: I like that it can summarize it.

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Speaker 4: How about you, Holly, are you as enthusiastic about these models?

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Speaker 2: Look, I am using it in my every day life,

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Speaker 2: in that I am using it at work I'm using

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Speaker 2: it for research. I don't use it for writing, but

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Speaker 2: that's because I'm a writer, right, So I think depending

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Speaker 2: on what bits of your life and bits of your

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Speaker 2: job that you need it for is going to be different.

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Speaker 2: So I've got friends whose lives have been changed by

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Speaker 2: the fact that because they've always really struggled with written communication, right.

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Speaker 2: So the fact that you can ask chat, GPT or

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Speaker 2: another LM to write you an email to your landlord

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Speaker 2: about a rental issue is life changing for them because

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Speaker 2: it's putting it in language that really works and all

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Speaker 2: those kind of things. So I don't really use it

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Speaker 2: for that because that's not my challenge, but I'm using

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Speaker 2: it a lot for research like you Ama, Although the

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Speaker 2: other thing that I would say is that you get

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Speaker 2: the research, but you still need to kind of human

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Speaker 2: the research, right, because you can't just cut and past

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Speaker 2: and go, right. I know, you have to read it,

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Speaker 2: absorb it, understand it. So it's not replacing that, but

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Speaker 2: it's very helpful. I also use it for things like

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Speaker 2: you know, I'm trying to eat more protein, right. We've

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Speaker 2: talked about this on the show before the cliche, and

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Speaker 2: I will sometimes put what I've eaten in there and

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Speaker 2: say does this include enough protein for a woman who's

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Speaker 2: this age blah blahlah blah blah, and it'll go well done,

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Speaker 2: well done on your protein quest. It might be better

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Speaker 2: if you included a little bit more cut like that

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Speaker 2: kind of stuff. Right, It's actually amazing. It's good for

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Speaker 2: starting brainstorm stuff. So I might be struggling with a

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Speaker 2: title for a podcast or with the description for something,

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Speaker 2: and although it won't always nail it, it's a great

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Speaker 2: place to start. It takes you away from having to

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Speaker 2: start everything with a blank page, and so I think

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Speaker 2: as a creative that is really good. I do, however,

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Speaker 2: share a lot of the worries because I think think

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Speaker 2: that one of the things that's interesting, like anything, and

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Speaker 2: I'm not talking necessarily about massive global scale, but more

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Speaker 2: just in our individual lives. We love using it, but

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Speaker 2: do we love being on the receiving end of it? Right,

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Speaker 2: So it's great to think, oh, I can get Ai

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Speaker 2: to write my email to the landlord. The email you're

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Speaker 2: getting back the landlord got AI to write that. So

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Speaker 2: now it's just robots talking to robots talking to robots,

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Speaker 2: and they're all talking in the same language. And when

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Speaker 2: you think about that sometimes you're like, what's that going

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Speaker 2: to do to how we learn to communicate with each other?

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Speaker 2: The other thing another thing about reports and stuff as well.

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Speaker 2: I don't like the idea that my kids report might

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Speaker 2: have been written by robots. And it's the same thing

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Speaker 2: where people go, oh, it's great because you can put

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Speaker 2: a book in there and it can tell you a

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Speaker 2: summary of the book, And I'm like, but i'd write books,

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Speaker 2: and I would be really upset if someone it's like,

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Speaker 2: I don't want to read it. So I think sometimes

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Speaker 2: it's like a lot of things, you like the utility

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Speaker 2: of it for you, but do you like the idea

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Speaker 2: that you're only talking to robots now? Not necessarily.

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Speaker 4: It reminds us that progress is never sort of all

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Speaker 4: good or all bad exactly. The democratization of information even

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Speaker 4: I've heard how it's transformed education and healthcare. So education,

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Speaker 4: it used to be that if you lived in a

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Speaker 4: remote area that didn't have access to great teachers, then

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Speaker 4: your education suffered because of that. These models means that

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Speaker 4: you can have a curriculum or whatever. Like there's information

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Speaker 4: available to anyone with a Wi Fi connection, which is

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Speaker 4: pretty good. But even in your example, Holly and I

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Speaker 4: know we've talked about this, where you might have gone

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Speaker 4: and seen a dietitian or I might have gone and

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Speaker 4: seen a PT. We now have these models. There's an

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Speaker 4: elimination of the human. And I think that the anxiety

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Speaker 4: that a lot of us have is job loss, which

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Speaker 4: I think is fair enough. And also I came across

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Speaker 4: this essay that was written about eight years ago, but

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Speaker 4: it's so relevant, and it was in the MIT Technology Review,

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Speaker 4: and it was called Eliminating the Human by David Byrne,

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Speaker 4: and it said that the unspoken overarching agenda of tech

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Speaker 4: more broadly seemed to be creating a world with less

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Speaker 4: human inte action and that that was a feature, not

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Speaker 4: a bug. So it was saying, think about online.

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Speaker 2: We already have been down the path of right like

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Speaker 2: trying with all kinds of other technological advancements that we

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Speaker 2: don't want to talk to the person of the checkout.

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Speaker 2: We'd rather do self serve. Thanks, and we've.

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Speaker 4: Yeah, yeah, so's it's the online ordering, it's the automated checkout,

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Speaker 4: it's Uber.

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Speaker 1: You know, you might as cars are coming.

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Speaker 4: Yeah, not go to a financial planner. Personal assistance can

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Speaker 4: now just be AI What he argues is that human history,

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Speaker 4: the reason we've got to this point is through cooperation,

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Speaker 4: and also that decision making is a deeply emotional and

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Speaker 4: human experience. We think it's logical, but it's actually not.

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Speaker 4: Like emotion is critical to it. And I think that's

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Speaker 4: what I worry about, is that we know that less

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Speaker 4: human interaction makes us lonelier and more unhappy. But a

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Speaker 4: lot of this if you're not writing the email, and

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Speaker 4: I get that there are people that really struggle with writing,

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Speaker 4: But these are all these tiny, little one percent building

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Speaker 4: blocks of connection, And when they're eroded, what are we

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Speaker 4: left with if all we do is talk to robots

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Speaker 4: all day? Like that, to me feels really depressed.

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Speaker 1: Some people would argue two things. I've heard it said

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Speaker 1: that AI probably won't take your job. Someone who knows

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Speaker 1: how to use AI will probably tell me.

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Speaker 4: I think that's what business owners say to make us

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Speaker 4: less stress about being made redundant.

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Speaker 1: Maybe maybe the other thing that you could say is

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Speaker 1: in the time that you save by using AI, does

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Speaker 1: that give you more time to spend with people that

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Speaker 1: you actually want to spend time with? Like is I

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Speaker 1: appreciate what you're saying about the human interaction of actually

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Speaker 1: talking to your landlord on the phone or in person

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Speaker 1: or whatever. But actually, if you can use AI to

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Speaker 1: make that a faster process, you can actually spend more

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Speaker 1: time with your friends or your family.

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Speaker 2: I think that's it, right.

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Speaker 1: I think that I'm trying to be up, Tom miss.

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Speaker 1: I'm trying to.

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Speaker 2: Be because the thing is one of the things about

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Speaker 2: all the conversations about AI and Jesse said this already

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Speaker 2: and it's absolutely right. Is it's either always it's the

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Speaker 2: end of the world or it's absolutely amazing, and it's

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Speaker 2: both those things. You know, it is the end of

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Speaker 2: the world as we know it. It is right, like,

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Speaker 2: there is no question that it's going to change everything

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Speaker 2: at an unbelievable pace. One of my robotic Roman empires

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Speaker 2: a little bit of a rabbit hole, but it stands up.

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Speaker 2: Do you remember the movie Up in the Air with

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Speaker 2: George Clooney and Anna Kenoss right, it's only I think

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Speaker 2: fifteen years old. The whole premise of that movie was

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Speaker 2: that George Clooney's job was to fire people, and he

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Speaker 2: used to fly to factories and sit in front of

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Speaker 2: people and fire people. And Anna Kendrick had worked out

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Speaker 2: a way to do it basically by what we would

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Speaker 2: call now zoom. And the whole premise of the movie

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Speaker 2: was like, oh my god, how inhumane. Imagine not sitting

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Speaker 2: across from a person and doing it on zoom. And

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Speaker 2: that was the whole premise of the movie. Fifteen years later,

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Speaker 2: the whole idea that someone might even call you on

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Speaker 2: the phone to fire you. When you look at what's

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Speaker 2: happening in America at the minute, which is you get

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Speaker 2: an email and you opt in or opt out, that

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Speaker 2: seems so quaint and you think about how much has

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Speaker 2: changed in fifteen years, imagine what things are going to

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Speaker 2: be like another ten or fifteen. It's going to change

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Speaker 2: the world unbelievably. Some of it will be good, some

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Speaker 2: of it'll be bad. One of the things that keeps

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Speaker 2: me up at night is what's everybody going to do?

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Speaker 2: Like literally, if you think about, if you think about

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Speaker 2: the amount, what are we freeing ourselves up? The industries

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Speaker 2: that are probably going to be really really changed by it,

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Speaker 2: And people say a lot of middle level admin jobs,

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Speaker 2: and that's it's kind of echoed by what's happening in

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Speaker 2: the States at the minute, which is like, we don't

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Speaker 2: need all these bureaucrats. We don't need all this. A

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Speaker 2: rough example is the law is that you have clerks

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Speaker 2: who do all the junior work for all the lawyers.

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Speaker 2: The robots can do all that and all these things,

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Speaker 2: and you think, amazing adv answers. But then what are

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Speaker 2: all these people going to do? What's everybody going to do?

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Speaker 2: That's what I wonder, and I think all the time

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Speaker 2: when I look at my kids, I think what jobs

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Speaker 2: can AI not do? Do you know what I mean?

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Speaker 2: And often I think, well, physical ones, like what job

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Speaker 2: couldn't a robot do? Maybe?

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Speaker 4: Speaking of the fires? Speaking of the fires, this is

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Speaker 4: something I got into a rabbit hole down over Christmas.

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Speaker 4: The environmental impact. I didn't realize this that it's something

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Speaker 4: like half a liter of water every time you ask

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Speaker 4: chat jpt a query. There's actually a massive environmental even

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Speaker 4: the electricity. What this is doing too.

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Speaker 1: Is already all through our lives, and that's the thing

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Speaker 1: that most people, and it also might fix the environment

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Speaker 1: and every exactly every chatbot that you use every time

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Speaker 1: you know, customer service telephones. I completely understand everything that

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Speaker 1: you're saying. And as we say, two things can be true.

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Speaker 1: You can be excited by it and open to the

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Speaker 1: idea of it, while also being nervous about it and

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Speaker 1: feel threatened by it for sure, but ultimately it's inevitable.

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Speaker 1: Oh so it's kind of like, well, how do we

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Speaker 1: live with that?

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Speaker 2: Yeah?

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Speaker 1: I often think and I saw this when social media

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Speaker 1: came along, and people were like, oh, no, I don't

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Speaker 1: do social media and I'm not And usually those things

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Speaker 1: are based.

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Speaker 4: Out of fear, but they were also.

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Speaker 1: Don't want to be leaving or oh I know, but

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Speaker 1: you don't want to be left behind. So I think

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Speaker 1: even if you don't use it, you need to understand it.

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Speaker 1: And I think the best way that I've come to

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Speaker 1: understand it is like having a virtual assistant.

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Speaker 2: I think that's a really good enough to explain that

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Speaker 2: a little bit.

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Speaker 1: So it's like having someone that can help you, but

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Speaker 1: you don't necessarily you won't find all their skills in

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Speaker 1: the one person. So if you think that you've got several,

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Speaker 1: one of your assistants might be really good at research,

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Speaker 1: which would be perplexity. But then another assistant, maybe chat GPT,

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Speaker 1: is really good at writing things and is more creative

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Speaker 1: and has more of a sort of a tone of voice.

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Speaker 1: But you wouldn't necessarily ask chat GPT to do your research,

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Speaker 1: and you wouldn't necessarily ask perplexity to write the email.

431
00:20:51,462 --> 00:20:53,941
Speaker 2: And you might also ask your assistant and if you

432
00:20:54,022 --> 00:20:56,181
Speaker 2: have in your personal life. So imagine that you've got

433
00:20:56,222 --> 00:20:58,342
Speaker 2: to write a speech for your best friend's wedding, right,

434
00:20:58,381 --> 00:21:00,261
Speaker 2: but you're not a writer, and that's a really challenging

435
00:21:00,262 --> 00:21:02,942
Speaker 2: thing to do. It's like having a person there who

436
00:21:02,982 --> 00:21:05,141
Speaker 2: can go, I'm going to draft you a speech for

437
00:21:05,182 --> 00:21:07,661
Speaker 2: your best friend's wedding, and then you can then human it.

438
00:21:07,742 --> 00:21:09,181
Speaker 2: And that's one of the things I think, because I

439
00:21:09,222 --> 00:21:11,581
Speaker 2: think you're absolutely right me, is that it is inevitable.

440
00:21:11,621 --> 00:21:15,341
Speaker 2: So although I have all kinds of big, overarching, like

441
00:21:15,581 --> 00:21:19,542
Speaker 2: existential questions about it, you've got to figure out how

442
00:21:19,982 --> 00:21:21,421
Speaker 2: to do about it. And I think that it's going

443
00:21:21,502 --> 00:21:25,141
Speaker 2: to make human touches and human interactions and human creativity

444
00:21:25,182 --> 00:21:28,141
Speaker 2: even more valuable in a way, right because when we

445
00:21:28,222 --> 00:21:30,821
Speaker 2: are all just our emails are all just robots talking

446
00:21:30,821 --> 00:21:32,421
Speaker 2: to each other every now and again, when you get

447
00:21:32,462 --> 00:21:34,702
Speaker 2: an actual human connection with someone, it's going to mean

448
00:21:34,702 --> 00:21:35,182
Speaker 2: a lot more.

449
00:21:35,462 --> 00:21:38,341
Speaker 1: And this is really important because just like if you

450
00:21:38,502 --> 00:21:42,101
Speaker 1: had a junior assistant, you would not trust them with

451
00:21:42,581 --> 00:21:46,422
Speaker 1: reading all your emails or your bank details or anything

452
00:21:46,502 --> 00:21:51,421
Speaker 1: really really personal and private, So be careful of not

453
00:21:51,702 --> 00:21:55,542
Speaker 1: putting everything into any kind of AI app out.

454
00:21:55,302 --> 00:21:57,981
Speaker 4: Louders in a moment our recommendations for the week, and

455
00:21:58,022 --> 00:22:01,102
Speaker 4: you won't believe it, but it includes a controversial fashion

456
00:22:01,141 --> 00:22:02,141
Speaker 4: recommendation from me.

457
00:22:08,182 --> 00:22:12,061
Speaker 2: Vibes ideas atmosphere, something casual, something fun.

458
00:22:12,621 --> 00:22:16,982
Speaker 1: This is my best recommendation. It's Friday, so we want

459
00:22:16,982 --> 00:22:20,181
Speaker 1: to set up your weekend with our best recommendations of

460
00:22:20,302 --> 00:22:24,581
Speaker 1: things to do by where, eat, read, watch. I'm going

461
00:22:24,621 --> 00:22:27,462
Speaker 1: to go first. I went a bit viral this week

462
00:22:27,502 --> 00:22:30,421
Speaker 1: with my jeans which were actually tracksuit pants.

463
00:22:30,581 --> 00:22:32,181
Speaker 4: I'm going to check why you speak. I'm going to

464
00:22:32,262 --> 00:22:34,542
Speaker 4: check the results of our pole sow that.

465
00:22:34,621 --> 00:22:38,462
Speaker 1: Is, in case you missed Monday's show and accompanying real,

466
00:22:39,182 --> 00:22:42,782
Speaker 1: I spoke about my new jeans. They're from Rag and Bone.

467
00:22:42,942 --> 00:22:45,421
Speaker 1: They're not cheap. They look like jeans, but they're actually

468
00:22:45,422 --> 00:22:48,262
Speaker 1: tracksuit pants and they've got jeans printed on them.

469
00:22:48,542 --> 00:22:50,942
Speaker 2: They're like pretend jeans. They're like a hi jeans. It's

470
00:22:50,982 --> 00:22:53,341
Speaker 2: like the front of them looks like there's a button,

471
00:22:53,422 --> 00:22:55,462
Speaker 2: it looks like there's a zipper, but if you touch them,

472
00:22:55,621 --> 00:22:56,141
Speaker 2: there's nothing.

473
00:22:56,182 --> 00:22:57,741
Speaker 1: There's nothing there, and you can just pull them on

474
00:22:57,782 --> 00:23:00,221
Speaker 1: and off because they have an ace was They do

475
00:23:00,302 --> 00:23:03,862
Speaker 1: have real pockets though, So if you think about jeggings,

476
00:23:04,302 --> 00:23:07,461
Speaker 1: this is like the next generation of jegging.

477
00:23:08,341 --> 00:23:09,341
Speaker 4: I don't think the world needed.

478
00:23:10,462 --> 00:23:11,982
Speaker 2: They're a bit cooler than checking something.

479
00:23:12,022 --> 00:23:14,742
Speaker 1: You two thought that they were a boring Yes.

480
00:23:14,702 --> 00:23:16,302
Speaker 4: So I put a pole out to the outlouders and

481
00:23:16,341 --> 00:23:17,061
Speaker 4: here are the results.

482
00:23:17,141 --> 00:23:17,901
Speaker 2: Yeah, have you checked?

483
00:23:18,022 --> 00:23:18,461
Speaker 1: I have not.

484
00:23:18,621 --> 00:23:20,862
Speaker 4: Okay, So I said, what do we think about tracksuit

485
00:23:20,861 --> 00:23:23,181
Speaker 4: pant jeans? I thought the answer was inherent in the question.

486
00:23:23,782 --> 00:23:26,742
Speaker 4: Seventy five percent of people said yes, they love them,

487
00:23:27,141 --> 00:23:30,462
Speaker 4: thirteen percent said no, and twelve percent said criminal.

488
00:23:30,581 --> 00:23:32,741
Speaker 1: That was just YouTube voting. A lot of times. You

489
00:23:32,782 --> 00:23:35,502
Speaker 1: both like comfort. I don't understand what could be wrong?

490
00:23:36,341 --> 00:23:38,421
Speaker 1: What could you hate about them? Okay, we'll be wearing

491
00:23:38,462 --> 00:23:41,701
Speaker 1: them suits anyway. I'm sure if you hang out, I'm

492
00:23:41,742 --> 00:23:44,101
Speaker 1: sure a lot of labels will make cheaper versions. The

493
00:23:44,182 --> 00:23:46,661
Speaker 1: rag and bone ones are sort of the original ones.

494
00:23:46,661 --> 00:23:48,342
Speaker 1: They come in a few different styles and a few

495
00:23:48,341 --> 00:23:51,661
Speaker 1: different washes, but there you go. I also get asked

496
00:23:51,702 --> 00:23:54,861
Speaker 1: a lot about trainers. People are wanting to know trainers.

497
00:23:55,061 --> 00:23:57,982
Speaker 1: What should I buy? The editor Samber ones were of

498
00:23:58,061 --> 00:24:00,902
Speaker 1: course huge over the last eighteen months or so. If

499
00:24:00,901 --> 00:24:02,502
Speaker 1: you want to try some new trainers if you're in

500
00:24:02,542 --> 00:24:04,742
Speaker 1: the market, so some new trainers as the weather gets cooler.

501
00:24:05,222 --> 00:24:08,222
Speaker 1: Puma Speedcats. I've got a pair of those in red.

502
00:24:08,262 --> 00:24:10,741
Speaker 1: They also come in pale pink, which is.

503
00:24:10,742 --> 00:24:15,421
Speaker 2: What's different about those similar to get on board with

504
00:24:15,462 --> 00:24:17,861
Speaker 2: what as wearing something different.

505
00:24:18,022 --> 00:24:20,461
Speaker 1: So they're all the same type of shoe. They're like

506
00:24:20,462 --> 00:24:22,742
Speaker 1: a low profile. They're not the kind of shoe you

507
00:24:22,742 --> 00:24:25,701
Speaker 1: would go running in or do any kind of exercise in.

508
00:24:25,702 --> 00:24:28,461
Speaker 1: They're kind of that real retro. The added as Samba

509
00:24:28,581 --> 00:24:32,901
Speaker 1: kind of popularized this whole new genre of sneaker, and

510
00:24:32,942 --> 00:24:35,621
Speaker 1: the others are honored Suker Tigers.

511
00:24:35,141 --> 00:24:39,502
Speaker 2: Which are very like yellow.

512
00:24:39,381 --> 00:24:41,621
Speaker 1: With black is the kind of iconic ones. I think

513
00:24:41,661 --> 00:24:44,861
Speaker 1: they're probably the most comfortable trainers I've ever worn.

514
00:24:44,861 --> 00:24:46,621
Speaker 4: When you told, you gave me a good tip, which

515
00:24:46,702 --> 00:24:49,782
Speaker 4: is they don't have to In fact, the point is

516
00:24:49,901 --> 00:24:51,702
Speaker 4: they don't go with what you want to know, because

517
00:24:51,702 --> 00:24:54,022
Speaker 4: I didn't understand what one was to do with actually

518
00:24:54,101 --> 00:24:56,581
Speaker 4: a yellow shoe or a red sneaker, Like what are

519
00:24:56,621 --> 00:24:58,502
Speaker 4: you going to do with that, but you wear them

520
00:24:58,861 --> 00:25:01,181
Speaker 4: as if they are just a neutral color.

521
00:25:01,381 --> 00:25:04,181
Speaker 2: She's ruined my life because I thought I was cool,

522
00:25:04,742 --> 00:25:07,581
Speaker 2: Well what do you think?

523
00:25:08,942 --> 00:25:09,502
Speaker 1: First mistake?

524
00:25:09,581 --> 00:25:12,182
Speaker 2: And I was like, what am I talking about with

525
00:25:12,222 --> 00:25:14,622
Speaker 2: my colorful, exciting Puma trainers? And then I went into

526
00:25:14,621 --> 00:25:16,222
Speaker 2: a trainer shop the other day and all I had

527
00:25:16,222 --> 00:25:18,461
Speaker 2: in my head was Speedcat, Speedcat, Speedcakes.

528
00:25:18,502 --> 00:25:22,902
Speaker 1: And I'm like, Dabba, if you can't afford a new

529
00:25:22,982 --> 00:25:26,021
Speaker 1: pair of trainers, but you would still like to upgrade

530
00:25:26,022 --> 00:25:27,862
Speaker 1: your look a little bit, you might want to consider

531
00:25:28,022 --> 00:25:30,982
Speaker 1: shoe jewelry, which I bought.

532
00:25:30,661 --> 00:25:34,381
Speaker 4: First style, can I suggest? I came in last week

533
00:25:34,422 --> 00:25:37,061
Speaker 4: with my new I don't know what they're called, spezzy hows,

534
00:25:37,182 --> 00:25:41,061
Speaker 4: I don't know, and I got three compliments people.

535
00:25:41,542 --> 00:25:42,302
Speaker 1: One of them was from me.

536
00:25:42,422 --> 00:25:44,982
Speaker 4: But you might recall Jesse's not buying anything new this year,

537
00:25:45,022 --> 00:25:46,302
Speaker 4: so how did she get a new pair of.

538
00:25:46,982 --> 00:25:49,341
Speaker 2: Did you get the ones that may just know?

539
00:25:49,901 --> 00:25:52,662
Speaker 4: I went to deepop and I got brand new never

540
00:25:53,022 --> 00:25:54,821
Speaker 4: someone hadn't worn them, but they were reselling them. So

541
00:25:54,861 --> 00:25:57,422
Speaker 4: there's your other option. The secondhand.

542
00:25:57,502 --> 00:25:58,621
Speaker 2: Because I was on.

543
00:26:00,101 --> 00:26:03,222
Speaker 1: Fashion app for mostly fast fashion, I talked.

544
00:26:03,422 --> 00:26:05,901
Speaker 2: This pair of expensive shoes that I like when I

545
00:26:05,982 --> 00:26:07,982
Speaker 2: was on nothing to wear last week and lots of

546
00:26:07,982 --> 00:26:10,542
Speaker 2: people messaged me and said there are so many Because

547
00:26:10,542 --> 00:26:12,141
Speaker 2: I was like, I've always wanted to buy these shoes,

548
00:26:12,141 --> 00:26:14,581
Speaker 2: but they're really expensive. And everyone said, there are so many.

549
00:26:14,581 --> 00:26:16,981
Speaker 2: Buy sell loads of people sell new shoes because you

550
00:26:17,022 --> 00:26:18,981
Speaker 2: know how secondhand shoes you can be a little bit like,

551
00:26:19,061 --> 00:26:23,021
Speaker 2: especially trainers secondhand shoes. But everyone said, there's a whole market,

552
00:26:23,621 --> 00:26:24,462
Speaker 2: so this is good.

553
00:26:24,262 --> 00:26:28,422
Speaker 4: To really maybe your echo should be Deep Hopes recommended.

554
00:26:29,022 --> 00:26:31,262
Speaker 4: And it's my whole outfit today's shoes and dressed Deep

555
00:26:31,302 --> 00:26:34,862
Speaker 4: hop It's changed my entire life. This is exciting, it's

556
00:26:34,861 --> 00:26:35,262
Speaker 4: really fun.

557
00:26:35,422 --> 00:26:38,341
Speaker 2: I have a very quick, very shallow recommendation for a

558
00:26:38,381 --> 00:26:42,061
Speaker 2: new concealer friends, because I, as we all know, we

559
00:26:42,141 --> 00:26:44,262
Speaker 2: have a guru in the house, which is Lee Campbell.

560
00:26:44,302 --> 00:26:47,382
Speaker 2: She's in the mom and Mere house. No not you,

561
00:26:47,502 --> 00:26:50,741
Speaker 2: also you, but you know, okay, And I needed a

562
00:26:50,782 --> 00:26:53,701
Speaker 2: new concealer under eye conceala specifically, which can be a

563
00:26:53,702 --> 00:26:58,022
Speaker 2: tricky purchase as your skin changes. And she said, you're

564
00:26:58,061 --> 00:27:00,101
Speaker 2: in luck because the last one she recommended for me

565
00:27:00,341 --> 00:27:03,821
Speaker 2: was Deal. Now, oh god, that was good. I bore

566
00:27:04,542 --> 00:27:07,661
Speaker 2: Judy Free when I was going somewhere and then, and

567
00:27:07,742 --> 00:27:10,502
Speaker 2: it was very spendy. But the good news is that

568
00:27:10,581 --> 00:27:13,542
Speaker 2: this year's recommendation from Lee Campil the sixteen dollars from

569
00:27:13,581 --> 00:27:18,902
Speaker 2: price line and it is the revolution illuminating under a concealer. Now,

570
00:27:18,982 --> 00:27:22,422
Speaker 2: I thought that I had moved past illuminating conceal its

571
00:27:22,422 --> 00:27:24,981
Speaker 2: because under our concealers, you know how they used to

572
00:27:24,982 --> 00:27:27,662
Speaker 2: be really pale in the old days. Not bad, not good,

573
00:27:27,702 --> 00:27:29,821
Speaker 2: not good, not good, big mistake. And Trinny was always

574
00:27:29,821 --> 00:27:31,502
Speaker 2: banging on about this, your conceal it should be the

575
00:27:31,502 --> 00:27:34,341
Speaker 2: same color as your foundation. But one of the things

576
00:27:34,381 --> 00:27:36,701
Speaker 2: is as you maybe get older and maybe get a

577
00:27:36,742 --> 00:27:40,101
Speaker 2: bit crease, is that anything that's too pigmented sits in

578
00:27:40,101 --> 00:27:42,901
Speaker 2: your lines and doesn't necessarily look how you want. So

579
00:27:43,022 --> 00:27:44,902
Speaker 2: when I first bought this, because I did, she said,

580
00:27:44,942 --> 00:27:47,302
Speaker 2: you'll think it's not right, but it's right. I went

581
00:27:47,341 --> 00:27:49,302
Speaker 2: and got it. She told me exactly what shade to get.

582
00:27:49,702 --> 00:27:52,981
Speaker 2: I brit Concealer sixteen dollars. It comes out and it

583
00:27:53,022 --> 00:27:55,181
Speaker 2: doesn't look like it's got any pigment. It looks like

584
00:27:55,262 --> 00:27:59,461
Speaker 2: it's a little bit just siera me or something, but

585
00:27:59,621 --> 00:28:01,341
Speaker 2: it's really lightning.

586
00:28:01,542 --> 00:28:03,621
Speaker 1: I've ever heard of this brand Revolution.

587
00:28:03,942 --> 00:28:06,702
Speaker 2: They're in price line and they're like affordable, and it's

588
00:28:06,702 --> 00:28:09,102
Speaker 2: got a little puff concealer. So you go dab dab

589
00:28:09,182 --> 00:28:12,821
Speaker 2: dab and I immediately just I mean, I know you

590
00:28:12,861 --> 00:28:16,821
Speaker 2: can also a little puff applicator. Okay, what do you

591
00:28:16,861 --> 00:28:20,022
Speaker 2: call them? Do I love that? Yeah? It's got a

592
00:28:20,061 --> 00:28:22,462
Speaker 2: little dough foot runs me of doughnuts and little feet.

593
00:28:22,462 --> 00:28:27,181
Speaker 1: It's not really for the vegans anyway.

594
00:28:28,742 --> 00:28:31,742
Speaker 2: Hard you can see me. I know, I don't look

595
00:28:31,782 --> 00:28:35,742
Speaker 2: like Giselle or whatever, but I reckon it really like fresh.

596
00:28:36,661 --> 00:28:38,622
Speaker 2: I like it and it's great and it's cheap.

597
00:28:38,661 --> 00:28:40,542
Speaker 1: How did she know what shade that you should wear?

598
00:28:40,622 --> 00:28:43,022
Speaker 2: She's so funny. Even in her answer, Tom, she said

599
00:28:43,462 --> 00:28:45,902
Speaker 2: you should wear fair. You'll think it should be light.

600
00:28:45,942 --> 00:28:47,661
Speaker 2: It shouldn't be light, it should be fair. Like she

601
00:28:47,782 --> 00:28:50,982
Speaker 2: was that specific. Everybody needs I'm sorry you can't all

602
00:28:51,022 --> 00:28:52,502
Speaker 2: have one, but just listen to.

603
00:28:53,022 --> 00:28:57,421
Speaker 4: We should make Ailee Campbell put in her brain and

604
00:28:57,502 --> 00:28:59,822
Speaker 4: just replicate it. Then we don't have.

605
00:28:59,742 --> 00:29:00,382
Speaker 2: To pay welcome.

606
00:29:02,462 --> 00:29:03,382
Speaker 1: This is the problem.

607
00:29:03,542 --> 00:29:06,782
Speaker 2: She and Heroin lies the problem.

608
00:29:06,902 --> 00:29:10,462
Speaker 4: My recommendation is a podcast episode of Search Engine. It

609
00:29:10,542 --> 00:29:12,582
Speaker 4: dropped like once a month, and listening.

610
00:29:12,342 --> 00:29:14,382
Speaker 1: To that, that's very very He was one of the

611
00:29:14,382 --> 00:29:17,502
Speaker 1: hosts of reply All, which is a fantastic podcast, which

612
00:29:17,822 --> 00:29:18,742
Speaker 1: brilliant podcast.

613
00:29:19,062 --> 00:29:22,502
Speaker 4: Now, this one was titled can You Cure Picky Eating?

614
00:29:22,702 --> 00:29:25,342
Speaker 4: And what I love in a podcast is when they

615
00:29:25,422 --> 00:29:28,062
Speaker 4: take you on an adventure. And this started with a

616
00:29:28,222 --> 00:29:31,782
Speaker 4: grown man, my kids, grown man who has an aversion

617
00:29:31,822 --> 00:29:35,702
Speaker 4: to anything fish, seafood just cut the smell, the texture,

618
00:29:35,982 --> 00:29:38,181
Speaker 4: all of it, and he said, is actually ruining my life.

619
00:29:38,222 --> 00:29:41,262
Speaker 4: Like my girlfriend really likes shrimp, and like I would

620
00:29:41,302 --> 00:29:42,862
Speaker 4: like to go out for dinner with her and I can't.

621
00:29:43,182 --> 00:29:46,181
Speaker 4: So he went and spoke to a psych who I

622
00:29:46,222 --> 00:29:48,302
Speaker 4: think works in like disordered eating, and I'd never thought

623
00:29:48,302 --> 00:29:52,542
Speaker 4: about picky eating as disordered before, but she kind of said,

624
00:29:53,142 --> 00:29:55,221
Speaker 4: this is how you define if it's kind of causing

625
00:29:55,222 --> 00:29:57,382
Speaker 4: issues in your life, and then gave him a bit

626
00:29:57,422 --> 00:30:01,062
Speaker 4: of a strategy for how one might incorporate eating that

627
00:30:01,102 --> 00:30:02,102
Speaker 4: food into their life.

628
00:30:02,342 --> 00:30:04,782
Speaker 3: So this is very simplistic, but like I have a

629
00:30:04,822 --> 00:30:08,902
Speaker 3: grid where it's like, you know, sweet and salty is,

630
00:30:08,982 --> 00:30:11,542
Speaker 3: let's say, on the X axis, and like chili and

631
00:30:11,582 --> 00:30:15,382
Speaker 3: crunchy is on the wire access And so like manny,

632
00:30:15,382 --> 00:30:17,142
Speaker 3: if you were to like plant the foods that you

633
00:30:17,182 --> 00:30:19,662
Speaker 3: currently prefer and eat. You know, on this you can

634
00:30:19,661 --> 00:30:21,382
Speaker 3: get the picture right, like, you know, is there a

635
00:30:21,462 --> 00:30:24,262
Speaker 3: kind of a pattern in terms of the textures and

636
00:30:24,342 --> 00:30:25,501
Speaker 3: tastes that you prefer.

637
00:30:28,862 --> 00:30:31,742
Speaker 1: So basically her advice was like, try to introduce fish

638
00:30:31,782 --> 00:30:34,622
Speaker 1: in a format that many might already like. So maybe

639
00:30:34,622 --> 00:30:37,582
Speaker 1: like a fish dish that's crunchy and salty, you know,

640
00:30:37,702 --> 00:30:38,342
Speaker 1: that sort of thing.

641
00:30:38,661 --> 00:30:40,862
Speaker 4: And it was really interesting.

642
00:30:40,982 --> 00:30:42,981
Speaker 1: I'm glad he did it about adults because you know,

643
00:30:43,382 --> 00:30:45,462
Speaker 1: the idea of pickI eaters is usually a phrase it

644
00:30:45,502 --> 00:30:48,302
Speaker 1: same to parents. You've got children can't get to eat

645
00:30:48,382 --> 00:30:52,062
Speaker 1: a mix of foods. Were there any takeaways for what

646
00:30:52,102 --> 00:30:53,662
Speaker 1: you could do with a child.

647
00:30:53,782 --> 00:30:57,181
Speaker 4: Or yes, absolutely applicable, really applicable. And it was stuff

648
00:30:57,222 --> 00:31:00,062
Speaker 4: that I sort of heard. But remember I checked that

649
00:31:00,182 --> 00:31:02,382
Speaker 4: DNA thing recently and it told me my market and

650
00:31:02,422 --> 00:31:05,221
Speaker 4: it said I was on the extreme end of pickiating

651
00:31:05,302 --> 00:31:08,342
Speaker 4: like that is in my genetics and it explains it

652
00:31:08,422 --> 00:31:10,862
Speaker 4: with lunar. I want to just this was interesting because

653
00:31:10,862 --> 00:31:13,862
Speaker 4: it was saying that in tribes you needed people who

654
00:31:13,942 --> 00:31:17,142
Speaker 4: were really cautious about eating that berry, and then other

655
00:31:17,182 --> 00:31:20,062
Speaker 4: people who crave novelty so they would look at it

656
00:31:20,142 --> 00:31:22,102
Speaker 4: and go oh well, I'll try that. And I would

657
00:31:22,142 --> 00:31:24,062
Speaker 4: sit back and go, yeah, you try it, and then

658
00:31:24,102 --> 00:31:24,941
Speaker 4: when you drop dead.

659
00:31:24,822 --> 00:31:24,902
Speaker 1: I.

660
00:31:26,782 --> 00:31:28,302
Speaker 2: Was going to say, I wonder if it's aligned with

661
00:31:28,462 --> 00:31:30,741
Speaker 2: and those people were the ones with the biggest brains

662
00:31:30,742 --> 00:31:31,622
Speaker 2: that we needed.

663
00:31:31,382 --> 00:31:34,582
Speaker 4: Yes somewhere, and I are more highly evolved because we're

664
00:31:34,622 --> 00:31:36,982
Speaker 4: looking at it going and even fish. You look at fish,

665
00:31:37,182 --> 00:31:37,902
Speaker 4: why would we eat that?

666
00:31:38,182 --> 00:31:38,422
Speaker 3: Yeah?

667
00:31:38,422 --> 00:31:41,661
Speaker 1: But also there's Darwinism in being an adventurous eater, because

668
00:31:41,742 --> 00:31:43,142
Speaker 1: if you're a picky eater.

669
00:31:42,942 --> 00:31:45,462
Speaker 4: The world needs both. Yeah, the world does need boat,

670
00:31:45,582 --> 00:31:48,142
Speaker 4: it needs both. That's so fast the idea that you

671
00:31:48,182 --> 00:31:50,022
Speaker 4: can change it. I just went, yeah, there are a

672
00:31:50,022 --> 00:31:51,622
Speaker 4: few things I would like to eat more of, and

673
00:31:51,661 --> 00:31:54,142
Speaker 4: how might I do that? So I would like to

674
00:31:54,142 --> 00:31:56,382
Speaker 4: eat more fish because I know that it's good for you.

675
00:31:57,062 --> 00:31:59,582
Speaker 4: And then there are you know, some vegetables. I've just

676
00:31:59,622 --> 00:32:01,661
Speaker 4: got a versions too that I'm like, can.

677
00:32:01,542 --> 00:32:03,741
Speaker 1: You give yourself an aversion to something if you want

678
00:32:03,782 --> 00:32:05,662
Speaker 1: to eat less of it? Like could you give yourself

679
00:32:05,661 --> 00:32:10,062
Speaker 1: an aversion to diet coke?

680
00:32:10,582 --> 00:32:13,542
Speaker 4: And I think the answer is hypnosis and I'm thinking

681
00:32:14,222 --> 00:32:16,982
Speaker 4: doing that, Like, imagine I want broccoli to be diet coke,

682
00:32:17,502 --> 00:32:18,382
Speaker 4: and I want like.

683
00:32:18,462 --> 00:32:22,062
Speaker 1: Diet coches, broccoli and finish to be chocolate.

684
00:32:22,502 --> 00:32:24,502
Speaker 4: I think that would improve my life. But yeah, really

685
00:32:24,502 --> 00:32:25,701
Speaker 4: interesting episode.

686
00:32:26,222 --> 00:32:28,822
Speaker 1: After the break, we have politicians in the Mum and

687
00:32:28,862 --> 00:32:32,262
Speaker 1: Maya office. This week we have sibling pride and Holly's

688
00:32:32,382 --> 00:32:35,741
Speaker 1: had a very dramatic week which we are going to unpack.

689
00:32:36,661 --> 00:32:39,982
Speaker 4: Every Tuesday and Thursday, we drop new segments of Mummeya

690
00:32:40,022 --> 00:32:43,542
Speaker 4: out Loud just for Mummeya subscribers. Follow the link in

691
00:32:43,582 --> 00:32:45,582
Speaker 4: the show notes to get your daily dose of out

692
00:32:45,622 --> 00:32:49,262
Speaker 4: Loud and a big thank you to all our current subscribers.

693
00:32:57,582 --> 00:33:00,142
Speaker 2: It's time for best and Worst. This is the part

694
00:33:00,182 --> 00:33:01,662
Speaker 2: of the show where we talk about what's been going

695
00:33:01,702 --> 00:33:05,342
Speaker 2: on behind the scenes, and I had quite a lot

696
00:33:05,382 --> 00:33:06,222
Speaker 2: gone behind the scenes.

697
00:33:06,302 --> 00:33:08,702
Speaker 1: You have to go first because people have been worried

698
00:33:08,702 --> 00:33:10,181
Speaker 1: about us.

699
00:33:10,262 --> 00:33:12,542
Speaker 2: So I wasn't on the show last week. You may

700
00:33:12,582 --> 00:33:15,702
Speaker 2: have noticed the amazing immediately Lester was. She was very good.

701
00:33:15,902 --> 00:33:18,302
Speaker 2: But I wasn't supposed to miss a whole week month

702
00:33:18,342 --> 00:33:21,382
Speaker 2: we were out Loud. What happened was we always joke,

703
00:33:21,422 --> 00:33:23,342
Speaker 2: We've been taught this lesson too many times now that

704
00:33:23,422 --> 00:33:25,942
Speaker 2: anything can happen any minute. But it was an ordinary

705
00:33:25,942 --> 00:33:27,782
Speaker 2: Saturday morning in our house. In fact, it was a

706
00:33:27,782 --> 00:33:29,981
Speaker 2: glorious one because we had the whole day free and

707
00:33:30,062 --> 00:33:32,742
Speaker 2: so we were like, we'd walk the dog. We're planned

708
00:33:32,742 --> 00:33:35,142
Speaker 2: on taking the kids down to a thing in Kangaroo Valley.

709
00:33:35,142 --> 00:33:36,461
Speaker 2: We were going to do this, We're going to do that.

710
00:33:36,502 --> 00:33:38,262
Speaker 2: We had something cooking on the stove, like. It was

711
00:33:38,822 --> 00:33:43,062
Speaker 2: like an idyllic looking saturday. That's always your first regional Australia.

712
00:33:43,142 --> 00:33:46,582
Speaker 2: It was very nice. Anyway, I decided that there was

713
00:33:46,622 --> 00:33:49,142
Speaker 2: a local plant sale and that I needed some plants.

714
00:33:49,182 --> 00:33:50,582
Speaker 2: I went to look at the plants. They were too

715
00:33:50,582 --> 00:33:52,582
Speaker 2: heavy for me to pick up. I came home. I said, Brent,

716
00:33:52,982 --> 00:33:55,062
Speaker 2: get in the car, come with me to pick up

717
00:33:55,102 --> 00:33:57,662
Speaker 2: some heavy things, will you, And he said yes, because

718
00:33:57,702 --> 00:34:00,421
Speaker 2: he's nice. Anyway, this is going to be a bit gruesome.

719
00:34:00,462 --> 00:34:01,142
Speaker 2: Can we handle it?

720
00:34:01,342 --> 00:34:02,662
Speaker 1: Yes, we can handle it.

721
00:34:02,782 --> 00:34:05,942
Speaker 2: Okay, So anyway, he bends down to pick up We're

722
00:34:05,942 --> 00:34:08,102
Speaker 2: in somebody's front yard in the town where I live.

723
00:34:08,502 --> 00:34:11,502
Speaker 2: He bends down to up a plant pot and very quickly,

724
00:34:11,661 --> 00:34:14,622
Speaker 2: split second, none of us saw this, but the plant

725
00:34:14,742 --> 00:34:17,262
Speaker 2: had like a metal like a frame, you know that

726
00:34:17,342 --> 00:34:20,542
Speaker 2: to grow up, you know, you wouldn't steak a very

727
00:34:20,582 --> 00:34:24,501
Speaker 2: thin metal steak that was rusty and old went into

728
00:34:24,542 --> 00:34:25,102
Speaker 2: Brent's eye.

729
00:34:25,741 --> 00:34:26,462
Speaker 4: Did he scream?

730
00:34:26,701 --> 00:34:28,781
Speaker 2: He didn't scream, but what he did was and it

731
00:34:28,862 --> 00:34:30,941
Speaker 2: was so fast, so it was like he bent down,

732
00:34:31,102 --> 00:34:33,341
Speaker 2: he recoiled back, he had his hand over his eye,

733
00:34:33,382 --> 00:34:36,301
Speaker 2: and he obviously went oh, you know, like shouted. The

734
00:34:36,302 --> 00:34:37,901
Speaker 2: people who were there was me and the three people

735
00:34:37,902 --> 00:34:40,181
Speaker 2: who lived in the house were all like oh what what?

736
00:34:40,302 --> 00:34:42,181
Speaker 2: And then you know how there's that split second way

737
00:34:42,261 --> 00:34:44,022
Speaker 2: go is this a serious thing or is this just

738
00:34:44,102 --> 00:34:45,942
Speaker 2: let's get on with getting the fucking plants in the car?

739
00:34:46,741 --> 00:34:50,382
Speaker 2: It was a course. It was apparent very quickly that

740
00:34:50,462 --> 00:34:54,381
Speaker 2: it was a serious thing. He was bleeding. Did you look?

741
00:34:54,661 --> 00:34:56,542
Speaker 2: I had a really quick look. I was like, move

742
00:34:56,582 --> 00:34:58,301
Speaker 2: your hand, move your hand. And then I very quickly

743
00:34:58,342 --> 00:35:00,702
Speaker 2: realized I didn't really want to look what. I can't

744
00:35:00,781 --> 00:35:01,782
Speaker 2: handle gore?

745
00:35:02,422 --> 00:35:03,702
Speaker 4: I could, but it was bleeding.

746
00:35:03,741 --> 00:35:06,382
Speaker 2: Yeah, right, I can see blood, and so I was like, okay,

747
00:35:07,102 --> 00:35:08,461
Speaker 2: So straight in the car?

748
00:35:08,701 --> 00:35:09,982
Speaker 1: Did he go pale? Oh?

749
00:35:10,102 --> 00:35:12,982
Speaker 2: He was so pale. He was so nauseous, he was

750
00:35:13,062 --> 00:35:16,422
Speaker 2: so like he was very quiet though he was not screaming,

751
00:35:16,701 --> 00:35:18,942
Speaker 2: he was just very quiet. I said, do we need

752
00:35:18,982 --> 00:35:20,781
Speaker 2: to go an emergency and he said yes, and I

753
00:35:20,822 --> 00:35:23,142
Speaker 2: was like right, So we went straight to emergency. Obviously,

754
00:35:23,221 --> 00:35:26,461
Speaker 2: like so much. It would have been really good because

755
00:35:26,502 --> 00:35:27,342
Speaker 2: I'm not very good in this.

756
00:35:27,542 --> 00:35:29,622
Speaker 1: No, I bet you were good. I just would have

757
00:35:29,622 --> 00:35:30,261
Speaker 1: been better.

758
00:35:31,342 --> 00:35:33,982
Speaker 2: Top of the leaderboard. She is good in the crisis,

759
00:35:33,982 --> 00:35:37,822
Speaker 2: it is true. So they set scanned him the metal

760
00:35:37,822 --> 00:35:40,342
Speaker 2: thing that had gone and I had fractured his orbital bone,

761
00:35:40,382 --> 00:35:42,542
Speaker 2: his eye socket, basically the bone that goes around your

762
00:35:42,582 --> 00:35:47,702
Speaker 2: eye had fractured. It had apparently missed, you know, his main.

763
00:35:47,582 --> 00:35:51,741
Speaker 1: Eye and also the orbital bone. Like that's good because

764
00:35:51,741 --> 00:35:55,022
Speaker 1: that would have slowed down the spy. Yes, he was.

765
00:35:55,622 --> 00:35:57,981
Speaker 2: It was one of those situations that in the end,

766
00:35:58,462 --> 00:36:01,142
Speaker 2: from a very unlucky moment, we were very lucky. Anyway,

767
00:36:01,622 --> 00:36:04,821
Speaker 2: long story short, we spent two days in hospital, lots

768
00:36:04,862 --> 00:36:07,941
Speaker 2: of tests, lots of checks and surgery and this and that,

769
00:36:08,462 --> 00:36:11,862
Speaker 2: and he is okay and he's recovered really really well.

770
00:36:12,102 --> 00:36:14,741
Speaker 2: So it was lucky. The doctor said, it's like a millimeter,

771
00:36:14,822 --> 00:36:16,781
Speaker 2: as these stories always go as like a millimeter to

772
00:36:16,862 --> 00:36:19,181
Speaker 2: him losing his eye. Really so it was a very

773
00:36:19,221 --> 00:36:22,742
Speaker 2: scary week. Anyone who goes through a sudden health scare,

774
00:36:23,022 --> 00:36:25,741
Speaker 2: it's very destabilizing. But I have to say, so my

775
00:36:25,822 --> 00:36:28,942
Speaker 2: best is the village, right, so including you guys, my mom,

776
00:36:29,022 --> 00:36:31,582
Speaker 2: we are out loud family because first of all, I

777
00:36:31,622 --> 00:36:33,381
Speaker 2: had to arrange, like the kids, we just left them

778
00:36:33,422 --> 00:36:35,582
Speaker 2: on Saturday morning at eleven and never came back, you

779
00:36:35,582 --> 00:36:38,702
Speaker 2: know what I mean. So the village of my friend

780
00:36:38,741 --> 00:36:40,861
Speaker 2: group there, who I was like, can you take the kids?

781
00:36:41,142 --> 00:36:42,622
Speaker 2: And my friends in Sydney, who I was like, can

782
00:36:42,622 --> 00:36:44,341
Speaker 2: I drop the dog with you? You know, like all

783
00:36:44,342 --> 00:36:46,901
Speaker 2: these things that you've got to think of. And then

784
00:36:47,022 --> 00:36:49,382
Speaker 2: by Sunday morning, it was apparent that we were not

785
00:36:49,502 --> 00:36:52,261
Speaker 2: going to be home for a couple of days, and

786
00:36:52,302 --> 00:36:54,342
Speaker 2: so then I was messaging you guys and saying I

787
00:36:54,342 --> 00:36:55,462
Speaker 2: don't think I can do the show.

788
00:36:55,862 --> 00:36:57,582
Speaker 1: And the thing that I think we're were the heroes.

789
00:36:57,622 --> 00:37:01,102
Speaker 2: Really, you were the heroes. You all just hit on calmly,

790
00:37:01,902 --> 00:37:03,661
Speaker 2: and you know, I was supposed to be going on

791
00:37:03,701 --> 00:37:05,742
Speaker 2: a work trip last week, and so I also messaged

792
00:37:05,781 --> 00:37:07,821
Speaker 2: our CEO Nat on Sunday morning, which is not something

793
00:37:07,822 --> 00:37:10,582
Speaker 2: I would normally do because she's very important. I sent

794
00:37:10,661 --> 00:37:12,301
Speaker 2: her a text and I was like, I don't know

795
00:37:12,302 --> 00:37:13,781
Speaker 2: who else to tell this, but I don't think I

796
00:37:13,822 --> 00:37:16,062
Speaker 2: can go to Tasmania this week and blah blah. Anyway,

797
00:37:16,622 --> 00:37:20,142
Speaker 2: so the week that we were recovering, like for several days,

798
00:37:20,142 --> 00:37:23,502
Speaker 2: he was just in bed and really tired and really exhausted,

799
00:37:23,542 --> 00:37:26,341
Speaker 2: and so as I actually not that it's about me, Like, yeah, brain,

800
00:37:26,422 --> 00:37:27,022
Speaker 2: get out of better.

801
00:37:27,502 --> 00:37:30,182
Speaker 1: There's sometimes when you're the person who's injured, the body

802
00:37:30,261 --> 00:37:34,701
Speaker 1: does things to protect you. Like Jersey, you've been through this.

803
00:37:35,142 --> 00:37:37,702
Speaker 1: There's a different type of trauma in being the loved

804
00:37:37,741 --> 00:37:40,582
Speaker 1: one watching it all happen. It was really you also

805
00:37:40,661 --> 00:37:41,382
Speaker 1: don't get drugs.

806
00:37:43,062 --> 00:37:44,421
Speaker 2: That was my very unny week.

807
00:37:44,822 --> 00:37:47,741
Speaker 4: Sending all our love to Brent, thank you, and to

808
00:37:47,822 --> 00:37:49,981
Speaker 4: anyone out there is sitting next to a hospital bed going,

809
00:37:50,862 --> 00:37:54,582
Speaker 4: I don't know what this sucks, all right, My worst

810
00:37:55,022 --> 00:37:59,181
Speaker 4: is being a cow. So this week, Yeah, since having

811
00:37:59,181 --> 00:38:01,901
Speaker 4: a baby, I think it is my hormones and all

812
00:38:01,942 --> 00:38:04,462
Speaker 4: of my cycle has never been clearer. I can now.

813
00:38:04,502 --> 00:38:06,022
Speaker 4: I think I've said this on the show before. I

814
00:38:06,022 --> 00:38:11,142
Speaker 4: can now feel myself ovulate, like I've all the things

815
00:38:11,181 --> 00:38:14,941
Speaker 4: that I've never felt before, and my p mess, which

816
00:38:14,942 --> 00:38:16,982
Speaker 4: I always rolled my eyes out a bit like and

817
00:38:17,062 --> 00:38:19,902
Speaker 4: kind of went, oh, how bad can it be? A

818
00:38:20,142 --> 00:38:25,022
Speaker 4: storm cloud descends into my brain like clockwork. As it descends,

819
00:38:25,181 --> 00:38:27,582
Speaker 4: I get a little notification that says your period is

820
00:38:27,661 --> 00:38:30,982
Speaker 4: due tomorrow, and I'm like, hmm, but I helpful.

821
00:38:31,022 --> 00:38:34,701
Speaker 1: I never put the tee right. It's always your Luca's relationship,

822
00:38:34,822 --> 00:38:36,502
Speaker 1: terrible job, your friends.

823
00:38:37,062 --> 00:38:42,062
Speaker 4: Yeah, no, Luca's being annoying. App You're wrong. So I

824
00:38:42,181 --> 00:38:47,381
Speaker 4: was sitting there and I felt it descend and I went, oh, no, no,

825
00:38:47,582 --> 00:38:50,102
Speaker 4: don't bite. Don't you know when you're going, don't bite,

826
00:38:50,142 --> 00:38:52,261
Speaker 4: don't bite, don't bite. And then you can't help it,

827
00:38:52,342 --> 00:38:53,982
Speaker 4: and then the words fall out and you're like, you're

828
00:38:53,982 --> 00:38:56,982
Speaker 4: fucking annoyed, Like I just am the worst version of

829
00:38:57,022 --> 00:38:59,902
Speaker 4: myself and had this instance on Saturday. You know, you

830
00:38:59,942 --> 00:39:01,502
Speaker 4: can't take it out on anyone else. I was going

831
00:39:01,542 --> 00:39:05,462
Speaker 4: to visit my grandfather with dementia, and I was Luna

832
00:39:05,542 --> 00:39:07,422
Speaker 4: was coming. It's gonna be a long thing, and you know,

833
00:39:07,462 --> 00:39:10,701
Speaker 4: you can't yell at them, and it was just this

834
00:39:10,741 --> 00:39:13,542
Speaker 4: whole thing, and I just snapped at him in a

835
00:39:13,582 --> 00:39:17,102
Speaker 4: way that he did not deserve. And then you've got

836
00:39:17,102 --> 00:39:20,022
Speaker 4: the hangover of the shame. Once the period comes and

837
00:39:20,022 --> 00:39:21,582
Speaker 4: you sit in the toilet, you go, no, I do

838
00:39:21,622 --> 00:39:23,701
Speaker 4: feel a bit ashamed. About how I behaved and.

839
00:39:23,582 --> 00:39:24,381
Speaker 2: Then what do you do?

840
00:39:24,542 --> 00:39:26,582
Speaker 4: Well? Now, he's a bit like a you know when

841
00:39:26,582 --> 00:39:29,221
Speaker 4: a dog's got in trouble, like his ears go down.

842
00:39:29,302 --> 00:39:31,381
Speaker 2: And did you say something you can't take back.

843
00:39:31,701 --> 00:39:34,942
Speaker 4: I wasn't even that bad. I just shouldn't have pressed

844
00:39:34,942 --> 00:39:37,381
Speaker 4: the button, like, and I knew that I shouldn't have,

845
00:39:37,741 --> 00:39:39,422
Speaker 4: but I couldn't help you help yourself.

846
00:39:39,661 --> 00:39:40,901
Speaker 1: Yeah, happen. And then you get your.

847
00:39:40,862 --> 00:39:42,982
Speaker 4: Period and then you walk around and you're like, let's

848
00:39:43,181 --> 00:39:45,502
Speaker 4: get take away and you're in like a great mood,

849
00:39:45,582 --> 00:39:47,701
Speaker 4: and Luke's like, I'm recovering.

850
00:39:47,862 --> 00:39:49,421
Speaker 1: That's what Perry's like for ten years.

851
00:39:49,462 --> 00:39:51,862
Speaker 2: It's also what kids are like, where they've done something

852
00:39:51,942 --> 00:39:54,462
Speaker 2: terrible and then they're like, let's get ice cream and

853
00:39:54,741 --> 00:39:57,221
Speaker 2: I'm still recovering from what you put me through this

854
00:39:57,342 --> 00:39:58,102
Speaker 2: morning exactly.

855
00:39:58,422 --> 00:40:02,221
Speaker 4: It's horrible. And my best is last week I went

856
00:40:02,422 --> 00:40:07,142
Speaker 4: to a book event for Claire, my twin sister, because

857
00:40:07,142 --> 00:40:10,102
Speaker 4: a book coming out later this year, and she read

858
00:40:10,102 --> 00:40:12,821
Speaker 4: an excerpt from it at this book event and I

859
00:40:13,181 --> 00:40:16,901
Speaker 4: was so proud to be standing there and I haven't

860
00:40:16,902 --> 00:40:18,542
Speaker 4: read it yet, I haven't live.

861
00:40:18,502 --> 00:40:19,941
Speaker 2: Well, how can we haven't read it yet?

862
00:40:20,062 --> 00:40:22,062
Speaker 4: We both did this we both kept it from each

863
00:40:22,062 --> 00:40:22,821
Speaker 4: other and it's fine.

864
00:40:22,822 --> 00:40:23,622
Speaker 2: I'm happy with it.

865
00:40:23,781 --> 00:40:26,102
Speaker 4: Yeah, and then you want them to read the whole thing.

866
00:40:26,822 --> 00:40:29,261
Speaker 4: But to see her standing there and the book is

867
00:40:29,302 --> 00:40:31,822
Speaker 4: called the worst thing I've Ever done, and it's about

868
00:40:32,302 --> 00:40:36,661
Speaker 4: cancelation and mass hysteria on social media and feminism and

869
00:40:36,661 --> 00:40:39,861
Speaker 4: how women treat each other online. And she read this

870
00:40:40,181 --> 00:40:43,462
Speaker 4: excerpt out that was just so brilliant, and I was

871
00:40:43,502 --> 00:40:45,741
Speaker 4: just standing there, going my twin sister or not, like

872
00:40:45,822 --> 00:40:48,701
Speaker 4: this is just going to be so big, and like

873
00:40:48,781 --> 00:40:51,821
Speaker 4: she's good. Everyone works hard when they write a book,

874
00:40:51,822 --> 00:40:53,382
Speaker 4: obviously it's one of the hardest things you do. But

875
00:40:53,862 --> 00:40:57,422
Speaker 4: Matilda is thirteen, fourteen months old. She has spent the

876
00:40:57,422 --> 00:41:00,022
Speaker 4: first year of Matilda's life, which is hard for anyone

877
00:41:00,622 --> 00:41:04,462
Speaker 4: writing this book. The dedication, the hours, the sacrifices that

878
00:41:04,502 --> 00:41:07,861
Speaker 4: she has made, Like I just can't wait to see

879
00:41:07,902 --> 00:41:11,221
Speaker 4: it pay off. Yea, like to feel all of that

880
00:41:11,221 --> 00:41:12,582
Speaker 4: pride and all the good feelings. And I didn't do

881
00:41:12,582 --> 00:41:14,981
Speaker 4: a single thing. I loved it, loved it. That must

882
00:41:15,022 --> 00:41:17,382
Speaker 4: be amazing, Maya, what's your worst?

883
00:41:17,701 --> 00:41:20,062
Speaker 1: My worst is a bit heavy. It's feeling like I'm

884
00:41:20,102 --> 00:41:24,901
Speaker 1: betraying my community by not speaking out more about anti Semitism,

885
00:41:25,261 --> 00:41:29,422
Speaker 1: but feeling unsafe to speak out when people are selling

886
00:41:29,502 --> 00:41:34,542
Speaker 1: swastika T shirts and doing Nazi salutes in public. It's

887
00:41:34,582 --> 00:41:40,302
Speaker 1: a very strange, frightening time to be a Jewish person

888
00:41:40,582 --> 00:41:45,182
Speaker 1: in Australia. We published a story on Mamma Mia last

889
00:41:45,221 --> 00:41:48,822
Speaker 1: week and interestingly it was one of our top stories,

890
00:41:48,862 --> 00:41:52,022
Speaker 1: which I wasn't expecting. It was written by someone anonymously

891
00:41:52,102 --> 00:41:54,861
Speaker 1: for all the reasons I've just said, and it wasn't you,

892
00:41:55,022 --> 00:41:56,782
Speaker 1: who wasn't me, And it was called what it feels

893
00:41:56,822 --> 00:41:59,501
Speaker 1: Like to be a Jewish Woman in Australia. I'll link

894
00:41:59,542 --> 00:42:02,221
Speaker 1: to that in the show notes because I think I

895
00:42:02,261 --> 00:42:04,462
Speaker 1: wish that I had been brave enough to write it

896
00:42:04,502 --> 00:42:06,861
Speaker 1: myself under my own name. But yeah, if you've got

897
00:42:06,942 --> 00:42:09,261
Speaker 1: Jewish people in your life or Jewish friends, checking on them,

898
00:42:09,261 --> 00:42:13,582
Speaker 1: because because we're feeling very frightened at the moment, my

899
00:42:13,741 --> 00:42:18,342
Speaker 1: best is. Peter Dutton and Anthony Abernezi both came in

900
00:42:18,382 --> 00:42:21,781
Speaker 1: to Muma Mea this week to do no Filter interviews

901
00:42:22,302 --> 00:42:26,382
Speaker 1: for International Women's Day with Caitline Brook and this is

902
00:42:26,382 --> 00:42:28,542
Speaker 1: the first time a liberal leader has engaged with Muma

903
00:42:28,542 --> 00:42:32,341
Speaker 1: Mea since Malcolm Turnbull. Was Prime Minister. Tony Abbott never would.

904
00:42:32,502 --> 00:42:34,941
Speaker 2: Do you remember when Morrison never would because obviously when

905
00:42:34,982 --> 00:42:38,022
Speaker 2: you were hosting No Filter and you I've always when

906
00:42:38,022 --> 00:42:41,622
Speaker 2: it's been an election time, You've always invited them. Remember

907
00:42:41,661 --> 00:42:44,462
Speaker 2: you were campaigning wants to get Scott Morrison to come here?

908
00:42:44,462 --> 00:42:44,982
Speaker 4: Would come?

909
00:42:45,102 --> 00:42:48,221
Speaker 2: Tony Abbott wouldn't come? And yeah, it's amazing.

910
00:42:48,422 --> 00:42:51,822
Speaker 1: I've just thought it's encouraging that both leaders are indeed

911
00:42:51,862 --> 00:42:58,142
Speaker 1: taking women seriously and understanding finally for some that women

912
00:42:58,862 --> 00:43:00,902
Speaker 1: make the difference in elections.

913
00:43:00,622 --> 00:43:04,582
Speaker 4: And that podcasting is really important to get your exact

914
00:43:04,661 --> 00:43:07,301
Speaker 4: messages across. And I know there's a lot of discussion

915
00:43:07,342 --> 00:43:10,182
Speaker 4: about platforming and endorsing and all that kind of stuff,

916
00:43:10,221 --> 00:43:13,582
Speaker 4: but like during election campaign, what I'm so proud of

917
00:43:13,622 --> 00:43:15,821
Speaker 4: as well is that we've got that specific question, which

918
00:43:15,862 --> 00:43:17,181
Speaker 4: is how are you going to change the lives of

919
00:43:17,221 --> 00:43:20,221
Speaker 4: Australian women, And you know, to hear them answer that

920
00:43:20,302 --> 00:43:22,222
Speaker 4: I think is really telling.

921
00:43:22,422 --> 00:43:26,062
Speaker 2: Platforming isn't endorsing. Having the leaders of the two major

922
00:43:26,102 --> 00:43:27,941
Speaker 2: parties come in and have an interview and the thing,

923
00:43:28,102 --> 00:43:29,582
Speaker 2: it'll be great because it's not going to be a

924
00:43:29,622 --> 00:43:32,062
Speaker 2: conversation like you might have seen them had a million

925
00:43:32,102 --> 00:43:35,422
Speaker 2: times before on TV being grilled about policy. It'll be

926
00:43:35,462 --> 00:43:37,701
Speaker 2: a really different kind of conversation, and the fact that

927
00:43:37,741 --> 00:43:41,902
Speaker 2: they're both doing it. It's not about making a point

928
00:43:42,022 --> 00:43:46,181
Speaker 2: or telling everybody. It's just like, here are two conversations.

929
00:43:46,701 --> 00:43:48,622
Speaker 2: That is all we have time for. Out louder is

930
00:43:48,661 --> 00:43:51,622
Speaker 2: A big thank you to all of you for being

931
00:43:51,661 --> 00:43:55,781
Speaker 2: here with us as you always are. It's very good

932
00:43:55,822 --> 00:43:58,942
Speaker 2: to be back. I missed everybody and I appreciate everybody's support.

933
00:43:59,342 --> 00:44:01,142
Speaker 2: Thank you to all of you for listening to our show.

934
00:44:01,422 --> 00:44:03,062
Speaker 2: We'll be back in you years next week read us

935
00:44:03,102 --> 00:44:03,622
Speaker 2: out friends.

936
00:44:03,781 --> 00:44:06,582
Speaker 4: A big thank you to our team group executive producer

937
00:44:06,741 --> 00:44:11,582
Speaker 4: Ruth Devine, executive producer Emmeline Zillis, our audio producer is

938
00:44:11,661 --> 00:44:12,821
Speaker 4: Leah Porgies, our.

939
00:44:12,741 --> 00:44:16,902
Speaker 1: Video producer Josh Green, and our junior content producers Tessa

940
00:44:16,982 --> 00:44:18,701
Speaker 1: and Coco. Thank you to all of them.

941
00:44:18,982 --> 00:44:22,821
Speaker 4: Bye bye, Out Louders. You're not ready to say goodbye. No,

942
00:44:23,022 --> 00:44:24,142
Speaker 4: so we thought we would leave you.

943
00:44:24,181 --> 00:44:26,821
Speaker 1: Why do we even bother say we just keep going?

944
00:44:26,902 --> 00:44:29,821
Speaker 2: Let the band who go off for an encore. We're

945
00:44:29,822 --> 00:44:31,381
Speaker 2: not waiting for the applause. We're just back.

946
00:44:32,701 --> 00:44:34,301
Speaker 4: We thought we would leave you with a little bit

947
00:44:34,342 --> 00:44:37,422
Speaker 4: of a conversation we had on a subscriber episode yesterday.

948
00:44:38,062 --> 00:44:42,301
Speaker 4: Holly and I answer some really thorny listener dilemmas. One

949
00:44:42,342 --> 00:44:45,022
Speaker 4: of them is about money, a woman who thinks that

950
00:44:45,062 --> 00:44:47,341
Speaker 4: she is a bit of a money pushover I can

951
00:44:47,342 --> 00:44:51,462
Speaker 4: actually relate, and another about an out louder named Karen

952
00:44:51,701 --> 00:44:54,822
Speaker 4: who is experiencing a few issues in her life.

953
00:44:55,582 --> 00:44:59,702
Speaker 2: Hello, out louders, Jesse and I are playing agony ants today.

954
00:45:00,261 --> 00:45:02,062
Speaker 2: There needs to be a better term for that. I

955
00:45:02,181 --> 00:45:05,221
Speaker 2: know too, for agony ant. Bet Dolly Alderton doesn't call

956
00:45:05,221 --> 00:45:06,582
Speaker 2: herself an agony an, does she?

957
00:45:06,741 --> 00:45:08,382
Speaker 4: We're wise witches or something?

958
00:45:08,542 --> 00:45:11,582
Speaker 2: We're wise witch Dear Holly and Jesse were calling this

959
00:45:11,982 --> 00:45:14,301
Speaker 2: and out loud us have got some good dilemmas for us.

960
00:45:14,342 --> 00:45:18,742
Speaker 2: Here we go. Hi, my name is Karen. Obviously everyone

961
00:45:18,781 --> 00:45:20,861
Speaker 2: thinks they know me. My name was chosen by by

962
00:45:20,862 --> 00:45:23,661
Speaker 2: American dad with love and care and over all other

963
00:45:23,741 --> 00:45:26,622
Speaker 2: baby names my mum wanted. He has now passed and

964
00:45:26,661 --> 00:45:29,102
Speaker 2: it is something I will always have that he gifted

965
00:45:29,102 --> 00:45:32,382
Speaker 2: to me. But we went bowling as a family the

966
00:45:32,422 --> 00:45:35,102
Speaker 2: other day and I entered my name on the screen

967
00:45:35,181 --> 00:45:38,022
Speaker 2: above the bowling lanes and the family next door with

968
00:45:38,102 --> 00:45:42,661
Speaker 2: tweens could not stop laughing. Oh. Every time I need

969
00:45:42,701 --> 00:45:44,901
Speaker 2: to ask for a help or a service, now I'm

970
00:45:44,942 --> 00:45:48,981
Speaker 2: reluctant to speak up and say my name help. Do

971
00:45:49,102 --> 00:45:53,221
Speaker 2: I need to change my name from a real life

972
00:45:53,342 --> 00:45:54,182
Speaker 2: Karen Jesse?

973
00:45:54,781 --> 00:45:58,102
Speaker 4: Karen, I feel for you because you know we've spoken

974
00:45:58,142 --> 00:46:01,022
Speaker 4: about this before the Karen. We will pop a link

975
00:46:01,062 --> 00:46:01,661
Speaker 4: in the show.

976
00:46:01,502 --> 00:46:05,582
Speaker 1: Notes shout out to any Mum and MEA subscribers listening.

977
00:46:05,741 --> 00:46:08,022
Speaker 1: If you love the show and want to support us

978
00:46:08,062 --> 00:46:10,902
Speaker 1: as well, subscribing to my omeya is the very best

979
00:46:10,902 --> 00:46:12,701
Speaker 1: way to do so. There is a link in the

980
00:46:12,741 --> 00:46:13,701
Speaker 1: episode description