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Speaker 1: Hey, this is Annie and Samantha and welcome to stuff

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Speaker 1: I've never told your production of I Heart Radio Today.

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Speaker 1: We are really excited to be joined by the host

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Speaker 1: of the podcast The Limit Does Not Exist, a show

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Speaker 1: that digs into what the host describe as human even diagrams.

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Speaker 1: Thank you both so much for being here. We're so

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Speaker 1: happy to be here. Can you introduce yourselves and describe

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Speaker 1: your show a bit more in depth words? Please? Yes. So,

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Speaker 1: I'm Kay and I'm Christina and Annie. You said it right.

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Speaker 1: Our podcast is a show for human ven diagrams, and

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Speaker 1: that's really our term for people who have multiple interests

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Speaker 1: that often feel like they might not go together. And

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Speaker 1: we're so fascinated by the intersections of those interests. We

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Speaker 1: believe when it comes to pursuing your passions, you shouldn't

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Speaker 1: have to choose. We are a fan of The End

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Speaker 1: over the Ore exactly. Both of us kind of have

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Speaker 1: these zigzag paths with um. As Kate said, unexpected intersections.

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Speaker 1: For me, it was math and theater. For Kate, she's

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Speaker 1: got filmmaking, directing, producing all of that world, plus a

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Speaker 1: lot of math and UH and helping girls get super

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Speaker 1: psyched by math. And we thought, well, if we love

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Speaker 1: all these things, there's probably a lot of other people

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Speaker 1: like that too. So that's what our show is about.

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Speaker 1: That's right. It really began as the intersection of STEM science, technology,

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Speaker 1: engineering and math and the arts, and since then, over

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Speaker 1: a hundred plus episodes later, it's really evolved into the

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Speaker 1: identity of work and how do you make sense of

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Speaker 1: what you love and how do you hopefully build a

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Speaker 1: really sustainable, custom built career out of that. So that's us.

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Speaker 1: We're so happy to be here. We're happy. Yeah, I'm

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Speaker 1: really passionate about girls and women and sim as well.

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Speaker 1: I've told this story several times on the show that

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Speaker 1: when I was in elementary school, I used to ask

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Speaker 1: for extra homework in math and science and told Peter

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Speaker 1: told me that no boy would ever want to date me. Yeah, yeah,

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Speaker 1: did you report her? I didn't, But I dropped out.

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Speaker 1: I was in calculus. This was in high school. I

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Speaker 1: was in calculus and I dropped out. And it's one

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Speaker 1: of my biggest regrets. Yeah, that was a crime against education.

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Speaker 1: And I am angry on your behalf, I know. And

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Speaker 1: I used to take so I went to m Georgia Tech,

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Speaker 1: which is really big STEM school, and I would take

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Speaker 1: physics classes like on the d L like I didn't

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Speaker 1: want people to know about it or something. And now

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Speaker 1: it makes me so angry. Right yeah, shadow happy boys

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Speaker 1: never liked me in school because then that wasn't a

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Speaker 1: consideration and I could just be the know it all

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Speaker 1: three ahead in math class and like I didn't care. Yes,

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Speaker 1: I mean I was one of two women in my

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Speaker 1: calculus class, a class there was there were a lot

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Speaker 1: of guys in there and this wasn't long ago. Yeah, yeah,

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Speaker 1: my gosh. So when we were discussing what we were

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Speaker 1: going to talk about, I was very so excited because

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Speaker 1: this is um A, We're doing a female first and

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Speaker 1: we're doing it on one of my heroes, Grace Hopper. Yes, absolutely,

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Speaker 1: I mean Grace Hopper. We like to call her Admiral Hopper.

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Speaker 1: We want to give her the rank that she earned.

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Speaker 1: She admiral. Yeah, she actually had a lot of firsts. Yes,

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Speaker 1: she did a hundred percent. She was the first woman

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Speaker 1: to ever earn a PhD in mathematics from Yale. You

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Speaker 1: know she was. She was so one of the first

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Speaker 1: ever computer programmers, and she invented the first compiler, which

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Speaker 1: we will explain She was integral to the invention of

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Speaker 1: a computer programming language that no small fee is still

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Speaker 1: used today, and when you think about how far we've

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Speaker 1: come technologically, it's really impressive. Yeah, she was called amazing

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Speaker 1: Grace by her subordinates, and I can see why. So

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Speaker 1: we are psyched to share her. We actually call her

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Speaker 1: the patron saint of our podcast. We have Our whole

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Speaker 1: first episode was about her and a fantastic book we

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Speaker 1: found on her history, and there's so much of her

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Speaker 1: life in her background that is consistent with this idea

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Speaker 1: of interdisciplinary work and kind of pulling in unexpected interests

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Speaker 1: that that we thought we had to share her with you, right. Actually,

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Speaker 1: our first our first social media handle was at Admiral

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Speaker 1: Hopper quite some time, about three and a half years.

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Speaker 1: People would, yeah, people would like tag us in comments

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Speaker 1: about her, and we're like, oh, we appreciate the shout out,

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Speaker 1: but like, we're not actually Admiral Hopper. Just to clarify

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Speaker 1: that if we were to be mistaken for anybody, yeah,

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Speaker 1: we'd be okay with that. We'd be okay with that.

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Speaker 1: I mean, so here's the deal. Grace Hopper was the

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Speaker 1: like one of the pioneers of the computing history, and

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Speaker 1: her impact spanned like four decades and counting, like she

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Speaker 1: was pretty impressive. That's right, Yeah, she's she's still going

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Speaker 1: and I like to think we'll go on forever. So Christina,

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Speaker 1: I think let's start with the compiler, which is the

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Speaker 1: first computer jargoning word that we just oh yeah for sure. Okay,

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Speaker 1: so what's the pilots one? That's right, So, a compiler

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Speaker 1: allows computer code to be written in a programming language,

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Speaker 1: written in actual words rather than machine language, which can

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Speaker 1: be kind of the zeros and ones. So if you

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Speaker 1: think about bits and bytes, um, it can also be

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Speaker 1: just like really complicated, uh kind of terms that the

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Speaker 1: computer will understand, but is super clunky and makes it

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Speaker 1: it's not just like you're writing in a foreign language.

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Speaker 1: You're you're literally trying to speak machine. So it makes

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Speaker 1: it really hard to pick up programming if the bar

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Speaker 1: was you have to write in machine language, right exactly.

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Speaker 1: What what's so cool about that and impactful is that

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Speaker 1: the ability to have computer code be written in a

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Speaker 1: programming language makes it more accessible to more people. So

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Speaker 1: if you can just learn that programming language, you don't

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Speaker 1: have to turn your brain into a machine. You can

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Speaker 1: you can write the code, that's right. And so Greece

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Speaker 1: Hopper was super against this idea of the high priest

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Speaker 1: genius ethos that I only special people can write code.

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Speaker 1: She's kind of like, yeah, I mean, I actually think

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Speaker 1: that anyone can learn how to write code. It's a

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Speaker 1: way of thinking and a language. And by insisting on

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Speaker 1: kind of moving us toward writing code in English and

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Speaker 1: use it a compiler to translate it for the machine,

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Speaker 1: it democratized access to computers. Like this is a big deal.

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Speaker 1: This is we're not talking like ten years ago, we're

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Speaker 1: talking back in like the fifties. This was a foundational

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Speaker 1: concept to the beginning of computer programming that she believed

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Speaker 1: anyone could learn. Yeah, so she really was this champion

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Speaker 1: for inclusion, this idea that everyone could learn how to

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Speaker 1: use a computer and could learn a language around it.

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Speaker 1: And you know, it's so significant to even thinking about

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Speaker 1: the story that you just told Annie, because there's continues

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Speaker 1: to be the sort of proliferation right in math and

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Speaker 1: stum fields where it's like there is one way to

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Speaker 1: do it and you don't understand that way, Too bad

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Speaker 1: for you, right, And so Adderal Hopper was really saying no, no, no,

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Speaker 1: it's a language that's learnable and you can learn it,

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Speaker 1: and I'm gonna invent this thing that's gonna make that

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Speaker 1: really possible. Absolutely. So I also think Christina that we

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Speaker 1: should know that at the time that the compiler was

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Speaker 1: invented by Hopper, this was like the late nineteen forties

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Speaker 1: and early nine and fifties, so computers took up entire rooms, right,

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Speaker 1: Like we've all seen hidden figures and the Harvard Mark

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Speaker 1: one computer of which Hopper became one of the first programmers.

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Speaker 1: Just some data on that. It was fifty one ft long,

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Speaker 1: eight feet tall, and two feet deep, so if you're

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Speaker 1: doing some quick, quick mental math, that's eight hundred and

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Speaker 1: sixteen cubic feet. It was just like daunting to even

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Speaker 1: look at right, like it was already very sort of

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Speaker 1: like how do I touch that? Like how I learned

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Speaker 1: how to do that? But it also weighed like ten

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Speaker 1: tho pounds. This is a pretty gigantic not gonna put

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Speaker 1: it in your laptop bag. So okay, back to the compiler, which,

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Speaker 1: to recap Hopper invented the compiler to allow computer code

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Speaker 1: to be written in accessible programming language. So there were

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Speaker 1: several other It's important to note rudimentary programming languages being

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Speaker 1: developed at the time. But here's the thing. Each one

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Speaker 1: was specific to one brand of computer, right, so that

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Speaker 1: you could learn one language, but you could only use

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Speaker 1: it on one kind of computer. You'd have to go find,

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Speaker 1: like your ten thousand pound computer that you could use

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Speaker 1: that language on. Um And so programs weren't portable, as

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Speaker 1: we already have talked about, neither our computers. So a

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Speaker 1: universal programming language was needed, right, And you know what

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Speaker 1: did Annual Hopper do. She helped create one for her.

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Speaker 1: That's not one must make one, that's right. So the

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Speaker 1: language that she really spearheaded the development of is known

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Speaker 1: as cobal Um not to be mistaken with a cabal,

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Speaker 1: but cobal which stands for common. We used to see

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Speaker 1: amnio in the word common common business oriented language. It's

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Speaker 1: a flexible, accessible, is for you know, words that you

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Speaker 1: could really sum up other parts of Hoppers work with.

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Speaker 1: Language that's still in use today, which is we talked about,

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Speaker 1: is really extraordinary. I mean, so this is what was

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Speaker 1: so big about it. Cobal was a program that could

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Speaker 1: be used. The whole design of it was that it

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Speaker 1: could be used across all these different brands of computers,

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Speaker 1: and there weren't like standard specs from one computer maker

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Speaker 1: to the next the way there is today. So until

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Speaker 1: this point, you would have to translate a program from

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Speaker 1: one computer to another, and that would cost like hundreds

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Speaker 1: of thousands of dollars. So they needed something that could

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Speaker 1: work on all hardware. And and this was what was

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Speaker 1: so powerful about the language that could be you know,

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Speaker 1: applicable and useful across banking, insurance, utilities, manufacturing, inventory control, healthcare, government,

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Speaker 1: the military. Right, there are all these different industries that

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Speaker 1: needed the power of computing, but they took in data

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Speaker 1: in different structures. They needed to process that data very differently.

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Speaker 1: And to write one simple English programming language that was

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Speaker 1: flexible enough across all of these industries and open enough

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Speaker 1: for kind of continuous development as these industries progressed and

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Speaker 1: could be learned by kind of a broad pool of people.

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Speaker 1: Like this was a really ambitious goal. Yeah, you know,

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Speaker 1: and time where all these industries even more today, right,

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Speaker 1: so much more we're really siloed, like they were really

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Speaker 1: independing of each other, Like we are just feeling the

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Speaker 1: industrial revolution, right, like we're feeling the effects years later,

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Speaker 1: still very strong. And this is so profound because it's like,

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Speaker 1: let's find a way to bridge the divide, like computers

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Speaker 1: for all literally here here and it worked, so okay,

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Speaker 1: fun fact, I think we can all remember the ease

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Speaker 1: of Y two K. I think about K I remember

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Speaker 1: the water bottles, everybody holding their breath. This is like,

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Speaker 1: is just like wrapping up in full force. Right at

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Speaker 1: that time, nearly eight percent of all computer code worldwide

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Speaker 1: was written in COBAL, including of all finance and insurance programs.

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Speaker 1: So it's just absolutely in effect still at that point.

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Speaker 1: And then do we all remember the Y two K

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Speaker 1: fear around resetting numbers of the new millennium? Right? It

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Speaker 1: was like, wait, what happens? Are we all resetting to zero?

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Speaker 1: And everything? They crashed like automatic doors will never open again. Right,

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Speaker 1: So we can also thank KOBAL for that. Yeah, you know,

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Speaker 1: the committee created COBAL and there are many things that

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Speaker 1: it did right. But one of the things that maybe

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Speaker 1: was a bit shortsighted was that it shortened years to

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Speaker 1: just two digits instead of nine. They did this to

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Speaker 1: save memory, and I guess it's probably pretty understandable that

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Speaker 1: in the mid kind of nineteen fifties. The thought that

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Speaker 1: this language would still be in use half a century later,

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Speaker 1: is not that the reasonable to be like, it's not

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Speaker 1: going to be an issue. Um so uh. You know, Luckily,

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Speaker 1: an entire jet narration of coball programmers were able to

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Speaker 1: come out of retirement in like to create these like

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Speaker 1: patches and workarounds to solve the problem. And it ended

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Speaker 1: up being kind of a bust. But it's not because

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Speaker 1: it wasn't a big problem. It's because we still had

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Speaker 1: enough living people who knew how to write cobal that

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Speaker 1: they could like fix it the last second. That's funny.

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Speaker 1: I thought it was right. They were doing a whole

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Speaker 1: like yeah, mess around and trying to gather everyone. I

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Speaker 1: do remember that being like, yeah, oh my god, we're

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Speaker 1: gonna go by Teddy ro. Yeah. I love that though,

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Speaker 1: because I feel like that's like the sign of the

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Speaker 1: successful operation of any kind that most people think, Oh

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Speaker 1: it was just fine. Yeah it didn't come Yeah, I

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Speaker 1: mean tweet like, can you imagine live tweeting the end

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Speaker 1: of the millennium and likes it happening? It's happening, didn't happen.

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Speaker 1: Things to work forward to. But no, you know what

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Speaker 1: I love about that too, is is that it was

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Speaker 1: so great that all of these sort of retirees came

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Speaker 1: out and there was this just whole you know, not

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Speaker 1: only did Hopper inspire the democratization of computers, but longevity

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Speaker 1: in the workforce. And we'll talk about that in a

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Speaker 1: little bit about how she was like the poster child

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Speaker 1: if you can work is what you want to I

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Speaker 1: was going to say, I could just imagine them going, Okay,

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Speaker 1: you kids. I feel like that's the movie we need

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Speaker 1: right now, a superhero team. They're coming together like a symbol.

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Speaker 1: I'm retired, what's wrong with you? So incredible about her

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Speaker 1: leadership style because you can imagine designing a programming language

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Speaker 1: by committee to work in you know, all of these

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Speaker 1: different worlds that could have been a total disaster um

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Speaker 1: but instead she really spearheaded this. And you know a

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Speaker 1: lot of the people interviewed about what this experience was

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Speaker 1: like afterwards said working with her was interesting because she

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Speaker 1: served as a conductor of invention rather than a dictator.

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Speaker 1: She she really allowed information to flow between her company,

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Speaker 1: Remington Rand, and all the other collaborators at other corporations,

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Speaker 1: and she one of her like famous quotes was You

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Speaker 1: don't manage people, you manage things. You lead people. And

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Speaker 1: I thought this was a really notable difference between her

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Speaker 1: and a lot of the men that led the early

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Speaker 1: computer programming efforts. This was a very distinct leadership style

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Speaker 1: that you know, I think we can see the effects

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Speaker 1: of how how well it worked. Yeah, it was really intuitive, right,

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Speaker 1: and it was really sort of human driven, and I

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Speaker 1: think that's something when we think about this sort of

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Speaker 1: separation between us and you know, technology, it's always like, well,

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Speaker 1: how do we bring it sort of to us? And

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Speaker 1: Admiral Hopper was so great. A's saying, yeah, this is

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Speaker 1: only valuable if we know how to talk about it

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Speaker 1: and we use it, and we knew how to share

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Speaker 1: it together. And that was just such an inclusive worldview

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Speaker 1: and one that came from her own life right of

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Speaker 1: really bridging different fields and its being such a pioneer.

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Speaker 1: So we have more on Grace Popper story, and we're

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Speaker 1: gonna get into that, but first we're going to get

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Speaker 1: into a quick break for work from responsors and we're back,

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Speaker 1: Thank you, sponsor. She sound a way to live her

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Speaker 1: vision of creating a common language for technically savvy people

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Speaker 1: and people who didn't identify as tech savvy. So basically

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Speaker 1: she was able to hang out with everybody, and in

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Speaker 1: terms she got everybody to hang out to We also

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Speaker 1: need to mention, okay, in nine she was awarded the

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Speaker 1: Data Processing Management Association's Man of the Year award. No

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Speaker 1: one saw the irony thing. Also, this is a crucial

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Speaker 1: fact about her. She invented the term bug is in

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Speaker 1: computer b Yeah, and and she invented it after an

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Speaker 1: actual bug. This is true. It was a moth was

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Speaker 1: found in a computer that was having issues, and she

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Speaker 1: took that moth and she taped it in her notebook

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Speaker 1: along with her notes from the day. And we still

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Speaker 1: use that to describe a problem that prevents a program

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Speaker 1: from running successfully. So I love that she saw a

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Speaker 1: problem and just sort of embraced it as Yeah, that's

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Speaker 1: what that is, and here it is completely in our vernacular.

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Speaker 1: The only thing that weirds me out about this is

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Speaker 1: having the dead bug taped in the notebook. So, yeah, Christina,

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Speaker 1: you and dead bugs? Right, Yeah, I'm just I'm a

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Speaker 1: little squeaming was fair enough. I love stories like that

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Speaker 1: where you're like, oh, I use this word every day,

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Speaker 1: you don't really think about it. And then when you

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Speaker 1: have an inkling of why do we call it that?

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Speaker 1: And then there's this amazing story behind it. I love

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Speaker 1: it when that happens. I would love to, like every

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Speaker 1: time I heard someone say that there was a bug

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Speaker 1: in the system, somehow just this like hologram of Grace

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Speaker 1: Hopper appears just to like take credit for it or

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Speaker 1: some kind of like Grace hopper trademark just shows up.

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Speaker 1: I kind of like a little a little belter ring gun, right,

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Speaker 1: like every time a bell rings, an angel gets its wing, right,

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Speaker 1: a Hopper gets her credit, but exactly a woman gets

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Speaker 1: the credit. She Okay, so we have to talk about

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Speaker 1: her background because she obviously was a badass. But how

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Speaker 1: did Admiral Hopper become such a badass? Right? Yeah, well

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Speaker 1: it all started, you could say, on December nine in

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Speaker 1: New York City when Admiral Hopper, then Grace Brewster Murray

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Speaker 1: was her growth name was born Grace start. Let's fast

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Speaker 1: forward just a little bit. Yeah. I think that's a

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Speaker 1: great idea. So, you know, it was clear looking at

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Speaker 1: her upbringing that education was important in her family. Her

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Speaker 1: dad actually went to Yale, where she would eventually go.

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Speaker 1: She went to private schools, and in ninety eight she

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Speaker 1: graduated Phi Beta Kappa from Vassar College with two degrees,

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Speaker 1: both in math and in physics. And then in nineteen

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Speaker 1: thirty she got her master's degree in mathematics from Yale

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Speaker 1: and the next year began teaching mathematics at Vassar while

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Speaker 1: finishing her doctorate in Yale in mathematics and mathematical physics.

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Speaker 1: She finished that in nineteen thirty four, So no flouch

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Speaker 1: working while finishing the doctorate, right, And you know, there's

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Speaker 1: actually an interesting historical fact here that a relatively high

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Speaker 1: number of women were receiving doctorates in the nineteen twenties

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Speaker 1: and the nineteen thirties, and the number of women who

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Speaker 1: were receiving doctorates at that time wouldn't be matched again

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Speaker 1: until the late nineties eighties. Just kind of crazy to

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Speaker 1: think about, right, Like, there was this uptick and women

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Speaker 1: getting doctorates, and then there was sort of this valley

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Speaker 1: and then it upticked again. Um, you know, like fifty

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Speaker 1: sixty years later. So you know, it sort of seems

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Speaker 1: in the post war kind of leave it to Beaver era,

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Speaker 1: Americans suddenly decided that women should focus on being mothers

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Speaker 1: and housewives and you know, there was a war going on.

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Speaker 1: There were things that needed to happen during that war

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Speaker 1: in jobs that needed to be had. But it's it's

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Speaker 1: also important to know that this wasn't always the case.

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Speaker 1: Like I love going back to history and signing moments

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Speaker 1: when women had a lot more power than than they

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Speaker 1: didn't have for a while. To nobody, it was there,

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Speaker 1: right and to go what happened exactly, And you know,

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Speaker 1: in either case, her success in a male dominated field

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Speaker 1: and in male dominated organizations like the military, it was

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Speaker 1: truly exceptional. Right. So after she got her PhD, she

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Speaker 1: became a full fledged math professor at Vasser, and she

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Speaker 1: became very sort of famous at Vasser. And what she

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Speaker 1: was known for was bringing in content from other disciplines

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Speaker 1: in her math courses. So her classes became some of

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Speaker 1: the most popular of the department. People would just show

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Speaker 1: up to hear her lecture. I love this so much

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Speaker 1: because basically they gave her a lot of the craft courses,

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Speaker 1: and she had a lot of them. In one semester

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Speaker 1: she was teaching I think like six courses. So she

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Speaker 1: was she was given kind of the rent workload, and

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Speaker 1: she said, you know what, fine, I'm going to make

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Speaker 1: these classes fascinating. And she took what came naturally to her,

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Speaker 1: this interdisciplinary interest, and she applied it to her pedagogy.

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Speaker 1: So while she was a professor, she had the rights

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Speaker 1: to audit classes advassasor she could sit in on anything

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Speaker 1: she wanted, and she attended classes in get this list.

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Speaker 1: This is an insane list, astronomy, physics, chemistry, geology, biology, zoology, economics, architecture, philosophy,

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Speaker 1: and the history of scientific thought. So she she was

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Speaker 1: a curious and she she found like newfound knowledge and

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Speaker 1: applications of mathematics in those fields, and she brought those

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Speaker 1: applications into her courses, which helped make math relevant for

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Speaker 1: students from a wide variety of majors. And that was

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Speaker 1: so crucial that she wasn't teaching these concepts in a vacuum.

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Speaker 1: She was showing them how does this apply to your

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Speaker 1: world and why does it matter? Right? I just love

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Speaker 1: thinking about that student who's like showing up for this

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Speaker 1: I don't know statistics class and it's like this is

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Speaker 1: just going to be a snooze fest. However, is just

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Speaker 1: up in the front and just completely breaking down whatever

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Speaker 1: expectations all of those students came in with, you know,

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Speaker 1: and that was just another one of her superpowers, was

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Speaker 1: this connector. And another thing that she did along these lines,

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Speaker 1: which is so fascinating and profound, is that she incorporated

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Speaker 1: writing in her math courses. There were essay assignments, and

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Speaker 1: her students sometimes complained that this was a math course,

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Speaker 1: not an English course, which Hopper replied that there's no

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Speaker 1: use trying to learn math unless you can communicate it

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Speaker 1: with other people. So, if we haven't yet mentioned it,

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Speaker 1: she was known as irreverence, sharp tongued, and brilliant. She

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Speaker 1: was Queen of the comeback. But this was so crucial

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Speaker 1: to her later success. Right, Her ability to connect ideas

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Speaker 1: but also talk about her work really truly communicated in

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Speaker 1: English is what made her so successful. Okay, so she's

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Speaker 1: teaching a assort. She's a star professor, and then Pearl

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Speaker 1: Harbor happens, and Hopper, like a lot of other people

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Speaker 1: in her family and her community, they wanted to serve

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Speaker 1: their country. But she thirty four years old, which the

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Speaker 1: military deemed too old. As someone who is past thirty

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Speaker 1: four currently, I'm assaulted by this right there with you.

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Speaker 1: He also was considered fifteen pounds underweight for her height.

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Speaker 1: I do not suffer from that problem. I don't know that.

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Speaker 1: She was too old, too small, too old, too small,

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Speaker 1: And so the military was like, no thanks, and then

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Speaker 1: they reconsidered because her mathematical skills were pretty in demand. Uh,

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Speaker 1: and so after like petitioning for a waiver, she in

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Speaker 1: December nineteen she was able to join the Navy, which

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Speaker 1: was super exciting. Yes, and I'm actually just going to

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Speaker 1: jump ahead here because this was one of those moments

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Speaker 1: and we'll jump right back one of more than one

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Speaker 1: times that the Navy was like we're done with you

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Speaker 1: or we don't need you, and then they were like,

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Speaker 1: oh wait, you're actually too valuable. Because when Hopper was sixties,

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Speaker 1: she was forced to retire from the Navy due to

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Speaker 1: her age, but then a mere seven months later, didn't

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Speaker 1: even take a year, the Navy called her back to

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Speaker 1: active service at the age of sixty, which, if we're

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Speaker 1: doing this math, it's now twenty six years past when

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Speaker 1: they told her she was too old. She's now called

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Speaker 1: back to active duty because literally no one could do

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Speaker 1: what she could do. And then she remained on active

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Speaker 1: duty for nineteen more years and became the oldest serving

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Speaker 1: officer in the U. S. Armed Forces. So studying that ages,

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Speaker 1: I can't imagine getting that call and been like mm hmm,

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Speaker 1: what what what are you going to give me? What

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Speaker 1: did you say you need me? What do you need

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Speaker 1: about She's like hetty, and I'd be like, I don't

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Speaker 1: know when we're talking about comeback, She's like clapp back.

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Speaker 1: She's like, oh, tell me that again, however nicely. I mean,

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Speaker 1: I'm a petty bitch, and I'd say great, but my

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Speaker 1: price has just double. She couldn't do that and didn't

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Speaker 1: do that. But I'm just saying, probably they have no

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Speaker 1: acknowledge that you are the only person who can do this.

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Speaker 1: Her price has gone up, right, absolutely, and your team

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00:27:12,960 --> 00:27:17,720
Speaker 1: of people to keep you as hopefully so. So going

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Speaker 1: back to right in the wake of Pearl Harbor, right,

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Speaker 1: she's gotten the waiver. She was able to join the Navy.

428
00:27:24,359 --> 00:27:27,320
Speaker 1: At this point, she had been married for fifteen years

429
00:27:27,359 --> 00:27:31,600
Speaker 1: to a fellow academic, and that marriage was ending. Happer

430
00:27:31,680 --> 00:27:34,800
Speaker 1: got divorced, and so at that time she was really

431
00:27:35,080 --> 00:27:37,640
Speaker 1: eager for a new path. But she'd been in academia

432
00:27:37,800 --> 00:27:41,080
Speaker 1: and she's like Jones Ngum for a new challenge. And

433
00:27:41,080 --> 00:27:43,440
Speaker 1: in case you're wondering about her personal life, she never

434
00:27:43,480 --> 00:27:46,480
Speaker 1: had children and she never remarried after her divorce, but

435
00:27:46,560 --> 00:27:51,200
Speaker 1: she did date other people we've read, Yeah, she still

436
00:27:51,240 --> 00:27:54,240
Speaker 1: had a life. I love it. That's right, that's okay.

437
00:27:54,320 --> 00:27:58,320
Speaker 1: So Happer joins the Navy. She goes into basic training,

438
00:27:58,400 --> 00:28:00,600
Speaker 1: and she thinks that she's going to be signed to

439
00:28:00,840 --> 00:28:05,600
Speaker 1: the cryptography unit to help decode communications. But literally, in

440
00:28:05,680 --> 00:28:09,080
Speaker 1: like the weeks that she's in basic training, plans change

441
00:28:09,480 --> 00:28:12,080
Speaker 1: and she gets sent up to Harvard to become the

442
00:28:12,440 --> 00:28:16,440
Speaker 1: literally the third ever computer programmer of the world's first computer,

443
00:28:16,520 --> 00:28:20,400
Speaker 1: the Mark one. So that's what, like, you know, things

444
00:28:20,480 --> 00:28:24,800
Speaker 1: were changing fast and furiously in the moment, but she, uh,

445
00:28:25,119 --> 00:28:28,680
Speaker 1: you know, she they could see how and why her

446
00:28:28,720 --> 00:28:32,040
Speaker 1: skills were relevant. And I think that that one little,

447
00:28:32,080 --> 00:28:34,080
Speaker 1: you know, decision to send her up to Harvard really

448
00:28:34,160 --> 00:28:39,640
Speaker 1: change the course of all of human technology. Yeah. Yeah,

449
00:28:39,680 --> 00:28:42,320
Speaker 1: it's it's so true. And and I love the fact

450
00:28:42,360 --> 00:28:46,600
Speaker 1: that after the war, Hopper was actually offered a full

451
00:28:46,680 --> 00:28:51,040
Speaker 1: professorship at vasser So I feel like the department Vassor

452
00:28:51,240 --> 00:28:54,280
Speaker 1: was like, oh, we had you teaching those terrible classes,

453
00:28:54,280 --> 00:28:59,240
Speaker 1: but come back and like um, and she said no.

454
00:28:59,760 --> 00:29:02,800
Speaker 1: She was like, I'm not going to come back because

455
00:29:02,800 --> 00:29:04,840
Speaker 1: she wanted to stay at Harvard. She wanted to keep

456
00:29:04,880 --> 00:29:08,040
Speaker 1: working on the Mark one. And during this time she

457
00:29:08,120 --> 00:29:12,000
Speaker 1: became a research fellow in Engineering Sciences and applied physics,

458
00:29:12,320 --> 00:29:15,239
Speaker 1: and she helped develop the Mark two computer and then

459
00:29:15,240 --> 00:29:20,440
Speaker 1: the Mark three computer. And then even though she did

460
00:29:20,480 --> 00:29:22,920
Speaker 1: all of this, eventually it became clear that she wasn't

461
00:29:22,960 --> 00:29:26,120
Speaker 1: going to be promoted or granted tenure at Harvard, which

462
00:29:26,160 --> 00:29:28,520
Speaker 1: is just like who was running the place? Taking my

463
00:29:28,560 --> 00:29:33,320
Speaker 1: head over here? My why? So she was like, alright, academia,

464
00:29:33,680 --> 00:29:36,440
Speaker 1: I'm done with you, and she went into the private sector,

465
00:29:36,760 --> 00:29:40,160
Speaker 1: which she brought us the Compiler and Cobal. Like, you know,

466
00:29:40,440 --> 00:29:43,840
Speaker 1: it wasn't a terrible outcome for humanity, but it's frustrating

467
00:29:43,960 --> 00:29:48,360
Speaker 1: on her behalf that like it still wasn't good enough.

468
00:29:49,400 --> 00:29:51,640
Speaker 1: But you know what, it's okay because she ended up

469
00:29:51,680 --> 00:29:55,600
Speaker 1: being the recipient of more than forty honorary degrees. She

470
00:29:55,720 --> 00:30:02,440
Speaker 1: got scholarships and professorships and awards. In President Obama posthumous

471
00:30:02,520 --> 00:30:05,520
Speaker 1: Lee gave her the Presidential Medal of Freedom, which is

472
00:30:05,560 --> 00:30:20,560
Speaker 1: the nation's highest civilian honor. So Harvard Harvard sucking basically, yes, yeah,

473
00:30:20,600 --> 00:30:23,720
Speaker 1: I mean amazing grace indeed, right, and and and while

474
00:30:23,960 --> 00:30:26,920
Speaker 1: there's always this aggravation that I feel when these awards

475
00:30:26,960 --> 00:30:30,200
Speaker 1: come later and then many of them after this act,

476
00:30:31,600 --> 00:30:36,360
Speaker 1: it's right, right, it's nice to know that many people

477
00:30:36,520 --> 00:30:40,560
Speaker 1: at least finally become to the Harper Party, right, I mean,

478
00:30:40,640 --> 00:30:43,640
Speaker 1: she has like the world's largest conference for women in

479
00:30:43,680 --> 00:30:48,040
Speaker 1: computing named so there's at least like the legacy is there.

480
00:30:48,040 --> 00:30:51,760
Speaker 1: At least people know who she is. And you know

481
00:30:51,920 --> 00:30:55,280
Speaker 1: that actually wasn't a guarantee. She was kind of in

482
00:30:55,360 --> 00:30:59,080
Speaker 1: obscurity for a very long time until was it sixty

483
00:30:59,120 --> 00:31:02,920
Speaker 1: minutes did a special on her, right, and all of

484
00:31:02,960 --> 00:31:07,920
Speaker 1: a sudden people are like, wait, what yeah, which is

485
00:31:07,960 --> 00:31:10,960
Speaker 1: disheartening in some ways because if she has done she

486
00:31:11,040 --> 00:31:13,880
Speaker 1: did so much and we still use so much of

487
00:31:13,920 --> 00:31:17,959
Speaker 1: what she built without crediting her aout crediting her is

488
00:31:18,240 --> 00:31:21,640
Speaker 1: very disheartening. Yeah, So I'm glad she's starting to get

489
00:31:22,120 --> 00:31:28,520
Speaker 1: the recognition that whether or not you want to know who,

490
00:31:29,160 --> 00:31:32,600
Speaker 1: who are all the other stories of women and people

491
00:31:32,600 --> 00:31:37,520
Speaker 1: of color who contributed to these revolutions right to our

492
00:31:37,560 --> 00:31:40,320
Speaker 1: knowledge base, and you know, get one month out of

493
00:31:40,320 --> 00:31:42,120
Speaker 1: the year that we tell their history, and we tell

494
00:31:42,160 --> 00:31:45,040
Speaker 1: the same five stories over and over again, right, and

495
00:31:45,120 --> 00:31:47,400
Speaker 1: I just I want there to be so many more

496
00:31:47,520 --> 00:31:51,920
Speaker 1: of these these incredible stories told, because you know they

497
00:31:51,960 --> 00:31:55,320
Speaker 1: were there, we just don't know their names exactly. Yeah,

498
00:31:55,560 --> 00:31:58,320
Speaker 1: that's right. You know, we've sort of already talked about

499
00:31:58,360 --> 00:32:01,560
Speaker 1: the premise of our podcast and gets very clear why

500
00:32:01,640 --> 00:32:06,400
Speaker 1: Admiral Hopper is our unofficial patron saint, you know. But

501
00:32:06,760 --> 00:32:10,880
Speaker 1: what what is so important about her too, is is

502
00:32:10,920 --> 00:32:13,920
Speaker 1: that she probably if you asked her, right, she was like, oh,

503
00:32:13,920 --> 00:32:15,520
Speaker 1: I'm too busy working on this thing. I can't talk

504
00:32:15,560 --> 00:32:18,520
Speaker 1: about whatever you want to ask, or you know, like

505
00:32:18,560 --> 00:32:20,800
Speaker 1: she was just like head down in work, and she

506
00:32:20,920 --> 00:32:25,320
Speaker 1: was just you could tell, so passionate about cracking open

507
00:32:25,400 --> 00:32:29,000
Speaker 1: things that should never be closed. Like she was like, yes,

508
00:32:29,440 --> 00:32:33,880
Speaker 1: why wouldn't writing and math go together, or why wouldn't

509
00:32:34,000 --> 00:32:38,280
Speaker 1: we in these different industries use the same language, Like

510
00:32:38,400 --> 00:32:42,440
Speaker 1: she was just by showing up and living what she

511
00:32:42,560 --> 00:32:48,200
Speaker 1: was passionate about. She was an innovator, right, And I

512
00:32:48,240 --> 00:32:52,280
Speaker 1: think you know, on our show, we we talk with

513
00:32:52,360 --> 00:32:56,320
Speaker 1: different innovators, and we really like to have conversations with

514
00:32:56,360 --> 00:33:00,520
Speaker 1: guests who are uh you know, really bridging differ it feels,

515
00:33:00,560 --> 00:33:03,600
Speaker 1: and saying no to silos. And it's not surprising to

516
00:33:03,720 --> 00:33:06,959
Speaker 1: us that it's really easy for us to find female

517
00:33:07,000 --> 00:33:11,640
Speaker 1: identifying guests for our show. Like this doesn't seem coincidental

518
00:33:11,720 --> 00:33:15,600
Speaker 1: at all. Yeah, I mean it reminds me of Cindy Gallup,

519
00:33:15,640 --> 00:33:19,360
Speaker 1: who's a fabulous woman um in the advertising industry and

520
00:33:19,400 --> 00:33:23,520
Speaker 1: in technology, who has a famous saying. She says, women

521
00:33:23,600 --> 00:33:28,719
Speaker 1: are often disruptors because they're never the default, like the

522
00:33:28,760 --> 00:33:32,040
Speaker 1: world was not designed for women, and so as a result,

523
00:33:32,200 --> 00:33:36,760
Speaker 1: they see where the friction is or where things could

524
00:33:36,800 --> 00:33:40,600
Speaker 1: be designed better, and that makes it, um maybe not

525
00:33:40,800 --> 00:33:43,600
Speaker 1: easier for them to innovate, but it certainly gives them

526
00:33:43,640 --> 00:33:47,560
Speaker 1: a perspective that is outside of this is how things

527
00:33:47,560 --> 00:33:50,600
Speaker 1: have always been done, or this just works because it works.

528
00:33:51,120 --> 00:33:53,520
Speaker 1: And so I think we see that very clearly with

529
00:33:53,600 --> 00:33:56,160
Speaker 1: Admiral Hopper as well as so many of the guests

530
00:33:56,160 --> 00:33:58,560
Speaker 1: on our show, where they say that didn't make any

531
00:33:58,560 --> 00:34:00,440
Speaker 1: sense to me, and so I tried to see if

532
00:34:00,440 --> 00:34:05,920
Speaker 1: there was a different way to go about doing it. Yeah, yeah, absolutely,

533
00:34:06,000 --> 00:34:10,040
Speaker 1: I think you know, Hopper is such an incredible example

534
00:34:10,120 --> 00:34:13,759
Speaker 1: of this. Just Steely resilience of She was like, I'm

535
00:34:13,800 --> 00:34:15,640
Speaker 1: too small and old for the Navy. We'll let me

536
00:34:15,680 --> 00:34:19,600
Speaker 1: go find something somewhere arounds here, right, Like, I'm just

537
00:34:19,719 --> 00:34:23,759
Speaker 1: going to be in spaces that weren't built for me,

538
00:34:23,920 --> 00:34:27,120
Speaker 1: but I will, I will shift the spaces in order

539
00:34:27,160 --> 00:34:28,960
Speaker 1: to do the work that I want to do. And

540
00:34:28,960 --> 00:34:31,680
Speaker 1: the fact that she was able to do that so

541
00:34:31,719 --> 00:34:33,920
Speaker 1: many times in the span of her life at a

542
00:34:34,000 --> 00:34:37,000
Speaker 1: time when um, you know, I mean, my gosh, it

543
00:34:37,040 --> 00:34:39,080
Speaker 1: continues to be difficult, right, but the fact that she

544
00:34:39,160 --> 00:34:41,480
Speaker 1: was able to just bust through so many doors just

545
00:34:42,120 --> 00:34:45,600
Speaker 1: iconic and badass and so inspiring. I just love so

546
00:34:45,680 --> 00:34:47,360
Speaker 1: much that she went and got a waiver, like she

547
00:34:47,440 --> 00:34:51,719
Speaker 1: didn't have take Now you know, this is such an

548
00:34:51,719 --> 00:34:54,560
Speaker 1: important thing. I think. I say this to my mentees

549
00:34:54,560 --> 00:34:56,320
Speaker 1: all the time, where they're like, well I'm not qualified

550
00:34:56,320 --> 00:34:58,840
Speaker 1: for that thing, or like I don't meet the whatever

551
00:34:59,040 --> 00:35:01,239
Speaker 1: the minimum rules. I'm like, but did you ask if

552
00:35:01,239 --> 00:35:03,160
Speaker 1: they'd make an exception? And they're like, well, why would

553
00:35:03,200 --> 00:35:05,400
Speaker 1: they make an exception for me? And I'm like, do

554
00:35:05,440 --> 00:35:08,759
Speaker 1: you think they don't make exceptions? Ever? Like, go ask,

555
00:35:09,400 --> 00:35:12,520
Speaker 1: and more often than not, there are waivers to be found,

556
00:35:12,719 --> 00:35:15,759
Speaker 1: exactly you've probably just hurt my light bulb, which is

557
00:35:16,160 --> 00:35:18,840
Speaker 1: I feel like getting a waiver is like going to

558
00:35:18,920 --> 00:35:22,879
Speaker 1: be my new comeback to anything, like, well, I'm gonna

559
00:35:22,920 --> 00:35:26,600
Speaker 1: go get a waiver and I'll do we do this,

560
00:35:26,960 --> 00:35:31,479
Speaker 1: so expect the waiver. Yes, so we're gonna come back

561
00:35:31,640 --> 00:35:33,920
Speaker 1: with a little more from Kate and Christina. But we

562
00:35:34,000 --> 00:35:50,640
Speaker 1: have one more break more sponsor, and we're back. Thank

563
00:35:50,680 --> 00:35:52,800
Speaker 1: you sponsor. Here we go, let's get into it again.

564
00:35:53,719 --> 00:35:57,520
Speaker 1: So much of what she did, like the math and writing,

565
00:35:57,600 --> 00:35:59,160
Speaker 1: is a great example to me because so many times

566
00:35:59,200 --> 00:36:01,520
Speaker 1: have you, if any of us, think back to when

567
00:36:01,560 --> 00:36:04,839
Speaker 1: you were in school, somebody I guarantee set in math,

568
00:36:04,960 --> 00:36:08,160
Speaker 1: like well, this isn't going to be useful for my future.

569
00:36:08,960 --> 00:36:15,480
Speaker 1: And it's so helpful to remember that, yes, if you

570
00:36:15,560 --> 00:36:20,200
Speaker 1: do math, then it's not a traditional liberal arts communication thing.

571
00:36:20,280 --> 00:36:22,560
Speaker 1: But like she said, if you can't talk about it,

572
00:36:22,920 --> 00:36:26,640
Speaker 1: if you can't work in the fields that rely on

573
00:36:26,880 --> 00:36:30,319
Speaker 1: communication and other skills, how are you gonna Then you're

574
00:36:30,360 --> 00:36:36,279
Speaker 1: hindering yourself. You're you're basically setting yourself back. And I

575
00:36:36,400 --> 00:36:40,560
Speaker 1: just think that's so useful to realize all these things

576
00:36:40,640 --> 00:36:43,960
Speaker 1: can touch each other, and all of these skills can

577
00:36:44,040 --> 00:36:48,160
Speaker 1: kind of weave together, and it's just it's why not

578
00:36:48,280 --> 00:36:53,560
Speaker 1: pick up another skill? That's right, And I think to

579
00:36:53,960 --> 00:36:57,160
Speaker 1: the way that math is presented is often just oh,

580
00:36:57,200 --> 00:36:59,520
Speaker 1: this is what it is and get on board or

581
00:36:59,880 --> 00:37:02,520
Speaker 1: the wrong with you? And what was just so like

582
00:37:02,680 --> 00:37:06,839
Speaker 1: script flipping about Hopper's work was like, no, actually, Matt

583
00:37:06,960 --> 00:37:10,520
Speaker 1: should be understood. There should be a why here, and

584
00:37:10,719 --> 00:37:14,120
Speaker 1: you can also write about that and create that for yourself,

585
00:37:14,320 --> 00:37:18,040
Speaker 1: like just saying there is nothing wrong with you. We

586
00:37:18,160 --> 00:37:22,359
Speaker 1: just have to break this open and enter ownership, enter

587
00:37:22,719 --> 00:37:25,520
Speaker 1: looking at it in different ways like enter just bringing

588
00:37:25,560 --> 00:37:27,879
Speaker 1: other skills to the party. So it's just all part

589
00:37:27,960 --> 00:37:36,600
Speaker 1: of her course. You know, absolutely we salute you. I know,

590
00:37:36,680 --> 00:37:38,279
Speaker 1: I feel like I need a drink now. I want

591
00:37:38,280 --> 00:37:44,640
Speaker 1: to like, yeah, give our a little pour out for sure. Yeah.

592
00:37:44,680 --> 00:37:46,480
Speaker 1: I feel like she would just have a whiskey meat

593
00:37:46,680 --> 00:37:52,480
Speaker 1: or something just like very just like power just completely

594
00:37:52,480 --> 00:37:54,400
Speaker 1: can't be going forward. That's one of my favorite parts,

595
00:37:54,400 --> 00:37:56,399
Speaker 1: like about the waiver and so I just like, Okay,

596
00:37:56,440 --> 00:37:59,680
Speaker 1: I got rejection or I've gotten pushed down, pushed aside.

597
00:38:00,080 --> 00:38:03,279
Speaker 1: I'm just gonna keep going. Screw all, y'all. Let me

598
00:38:03,360 --> 00:38:05,600
Speaker 1: just keep going and I'm gonna make it forward. I

599
00:38:05,719 --> 00:38:09,880
Speaker 1: might not see the credit ever, obviously, but one day

600
00:38:10,239 --> 00:38:11,640
Speaker 1: this is what I've done for the world at leads

601
00:38:11,719 --> 00:38:14,880
Speaker 1: up changed humanity in some sort of way. M h Yeah.

602
00:38:15,320 --> 00:38:17,920
Speaker 1: And when we look at online spaces or even just

603
00:38:17,960 --> 00:38:22,719
Speaker 1: the way people speak, women and people of color are

604
00:38:22,800 --> 00:38:26,319
Speaker 1: largely the ones pushing it along because they do see

605
00:38:26,600 --> 00:38:29,799
Speaker 1: they see these places where we need to improve or

606
00:38:30,000 --> 00:38:33,560
Speaker 1: you're not serving me, right, So I guess I'll go

607
00:38:34,280 --> 00:38:37,560
Speaker 1: and right, yeah, which is kind of that edge, just

608
00:38:37,680 --> 00:38:40,000
Speaker 1: like there's no space for me, I'll make space watch

609
00:38:40,040 --> 00:38:45,000
Speaker 1: me right right, exactly, absolutely right. And that can be

610
00:38:45,280 --> 00:38:48,160
Speaker 1: you know, my gosh, that can be exhausting. That can

611
00:38:48,160 --> 00:38:51,160
Speaker 1: be full of the things. And just remembering that, like

612
00:38:51,480 --> 00:38:55,160
Speaker 1: being able to just do your work and doing whatever

613
00:38:55,200 --> 00:38:57,439
Speaker 1: it takes to do that and being like, I don't

614
00:38:57,480 --> 00:38:59,120
Speaker 1: care if you're not on board, but I'm going to

615
00:38:59,160 --> 00:39:02,280
Speaker 1: make this happen. Is just you know, such a skill

616
00:39:02,440 --> 00:39:05,080
Speaker 1: that that I know, I look at hop the story

617
00:39:05,080 --> 00:39:07,080
Speaker 1: and go, yeah, I can I can do more that

618
00:39:07,360 --> 00:39:10,080
Speaker 1: right now, even I'm not gonna lie, that's exhausted me.

619
00:39:10,160 --> 00:39:12,680
Speaker 1: Because in the like she has to go overboard to

620
00:39:12,680 --> 00:39:15,200
Speaker 1: prove ourselves not only and I do what you're asking

621
00:39:15,239 --> 00:39:17,000
Speaker 1: me to do, but I'm going to go beyond so

622
00:39:17,040 --> 00:39:21,000
Speaker 1: I can prove that could do that in order to

623
00:39:21,000 --> 00:39:23,120
Speaker 1: prove my birth. Not only do I have to go

624
00:39:23,440 --> 00:39:26,759
Speaker 1: do what I'm supposed to do, but unlike a white man,

625
00:39:26,840 --> 00:39:29,480
Speaker 1: I have to go beyond that to show you I'm

626
00:39:29,520 --> 00:39:33,440
Speaker 1: more qualified than that bus that they are doing that.

627
00:39:35,840 --> 00:39:39,120
Speaker 1: So it is exhausting, but if you can bring, you know,

628
00:39:39,200 --> 00:39:42,680
Speaker 1: others like you along behind you, you know, leave the

629
00:39:42,719 --> 00:39:46,000
Speaker 1: door open. One of the things that Hopper was known

630
00:39:46,040 --> 00:39:49,640
Speaker 1: for not just in believing that anyone could code, but

631
00:39:49,719 --> 00:39:54,680
Speaker 1: she specifically went out and recruited young women and and

632
00:39:54,719 --> 00:39:56,560
Speaker 1: taught them because She's like, if I can teach them

633
00:39:56,560 --> 00:39:58,560
Speaker 1: how to do it, then everyone will see that everyone

634
00:39:58,600 --> 00:40:00,839
Speaker 1: can learn how to do it. So she didn't just

635
00:40:00,960 --> 00:40:05,600
Speaker 1: like allow herself to be the exception. She really designed

636
00:40:05,600 --> 00:40:08,400
Speaker 1: a world and brought people along with her that said,

637
00:40:08,760 --> 00:40:11,280
Speaker 1: I'm not the only one. I'm just the first because

638
00:40:11,320 --> 00:40:13,480
Speaker 1: I'm the first one to ask for the waiver, or

639
00:40:13,480 --> 00:40:16,279
Speaker 1: I'm the first one that had the stamina to put

640
00:40:16,360 --> 00:40:19,400
Speaker 1: up with all of this crap. But there are lots

641
00:40:19,600 --> 00:40:21,960
Speaker 1: more like me, and I'm going to make sure that

642
00:40:22,000 --> 00:40:24,160
Speaker 1: I bring them along with me to ensure that we

643
00:40:24,200 --> 00:40:28,319
Speaker 1: stay yeah, which is that's right, and and she just

644
00:40:28,440 --> 00:40:33,279
Speaker 1: created this undeniable force from doing that, which, as we

645
00:40:33,360 --> 00:40:36,440
Speaker 1: saw more than one time in history, a bunch of

646
00:40:36,520 --> 00:40:42,200
Speaker 1: dudes were like, oh wait, yeah, we need you know,

647
00:40:42,520 --> 00:40:46,840
Speaker 1: and she's like, yeah, yeah, I'm I'm you know, throwing

648
00:40:46,880 --> 00:40:49,839
Speaker 1: this proverb real party over here with a lot of

649
00:40:49,920 --> 00:40:55,080
Speaker 1: badass women and humans get on board or I'm another

650
00:40:55,080 --> 00:41:02,040
Speaker 1: train off. Yeah. And that also going back to what

651
00:41:02,080 --> 00:41:04,719
Speaker 1: you said earlier about the context of how many first

652
00:41:04,760 --> 00:41:09,200
Speaker 1: have we lost? Yeah, So I'm glad these stories are

653
00:41:09,280 --> 00:41:11,680
Speaker 1: starting to come out and always important to have that

654
00:41:11,800 --> 00:41:15,360
Speaker 1: context of there were people doing these things that were

655
00:41:15,440 --> 00:41:19,160
Speaker 1: not recognized and have not been yet right, but hopefully,

656
00:41:19,440 --> 00:41:21,759
Speaker 1: hopefully and then we can keep searching because there are

657
00:41:21,800 --> 00:41:24,279
Speaker 1: those stories, and that's kind of been the theme today, ye,

658
00:41:25,640 --> 00:41:28,960
Speaker 1: rediscovering stories of this was done first. We just didn't

659
00:41:28,960 --> 00:41:31,359
Speaker 1: know about it until we had to put in all

660
00:41:31,440 --> 00:41:34,560
Speaker 1: this research. And there are many more who we didn't

661
00:41:34,640 --> 00:41:37,000
Speaker 1: know about that's slowly coming out, and hopefully we can

662
00:41:37,040 --> 00:41:40,040
Speaker 1: continue to find that, and I'm sure we will continue

663
00:41:40,080 --> 00:41:42,920
Speaker 1: to find that and dig deeper, which is phenomenal and

664
00:41:42,960 --> 00:41:46,920
Speaker 1: it's great because that means we can keep learning. Well,

665
00:41:47,160 --> 00:41:50,759
Speaker 1: like big old shiny gold stars to you too, or

666
00:41:51,000 --> 00:41:56,400
Speaker 1: having first and inspiring us to really deep dive a

667
00:41:56,440 --> 00:41:59,359
Speaker 1: woman in history who we love and just I mean

668
00:41:59,600 --> 00:42:02,239
Speaker 1: I feel my gosh, b just every time I look

669
00:42:02,280 --> 00:42:04,120
Speaker 1: at her story, it was just more to more to

670
00:42:04,239 --> 00:42:07,840
Speaker 1: mine and more to love, right, yes, um, and gold

671
00:42:07,840 --> 00:42:12,440
Speaker 1: stars to you as well. I mean when you said,

672
00:42:12,640 --> 00:42:14,319
Speaker 1: like it was the first pitch you said, and I

673
00:42:14,360 --> 00:42:20,879
Speaker 1: was like, yes, yes, I love her. I actually, um,

674
00:42:20,920 --> 00:42:23,040
Speaker 1: I used to be the producer of this show and

675
00:42:23,080 --> 00:42:25,279
Speaker 1: we used to have a video series, and one of

676
00:42:25,320 --> 00:42:29,000
Speaker 1: the video series was called Her Story, and we did

677
00:42:29,080 --> 00:42:31,800
Speaker 1: one on Grace Hopper. So for anyone who wants to

678
00:42:31,800 --> 00:42:36,440
Speaker 1: see a super goofy, you know, it's like wacky PBS

679
00:42:36,520 --> 00:42:44,840
Speaker 1: interview with Grace Hopper, I suggest watch this immediately and

680
00:42:45,000 --> 00:42:49,360
Speaker 1: love curiously googling on the fender. Yes, yeah, I just

681
00:42:49,440 --> 00:42:51,440
Speaker 1: love that you guys have a whole series based on

682
00:42:51,560 --> 00:42:53,600
Speaker 1: the human ven diagram because I think it's important to

683
00:42:53,600 --> 00:42:56,040
Speaker 1: show every aspect that is not just one or the other,

684
00:42:56,600 --> 00:43:01,719
Speaker 1: and people or complex beautiful and we need that desperately,

685
00:43:01,800 --> 00:43:07,760
Speaker 1: especially today's humanity. I love being negative. That's my social

686
00:43:07,760 --> 00:43:13,200
Speaker 1: work side of me. Everything's running everything that every episode

687
00:43:13,200 --> 00:43:14,520
Speaker 1: I do. I try to put it that every now

688
00:43:14,520 --> 00:43:21,040
Speaker 1: and again. Okay, well, things okay, things are okay, Okay,

689
00:43:21,120 --> 00:43:25,840
Speaker 1: thanks absolutely, things are okay, and we all contain multitude

690
00:43:26,800 --> 00:43:29,680
Speaker 1: perfect perfect way to end. Is there anything else you

691
00:43:29,719 --> 00:43:32,400
Speaker 1: wanted to add before we get to some shout outs?

692
00:43:34,400 --> 00:43:39,840
Speaker 1: Not at all. I salute you at absolutely, Admiral Hopper,

693
00:43:39,880 --> 00:43:44,120
Speaker 1: and we salute you Annie and Samantha, well, thank you,

694
00:43:44,239 --> 00:43:47,840
Speaker 1: Kate Christina coming on with yes, I feel like we

695
00:43:47,880 --> 00:43:51,640
Speaker 1: are truly e friends. So what we are I don't

696
00:43:51,640 --> 00:43:53,439
Speaker 1: know what this is when you don't see them face

697
00:43:53,520 --> 00:43:55,640
Speaker 1: to face. Yeah. I don't think there's a good word

698
00:43:55,640 --> 00:43:58,320
Speaker 1: for that yet, cyber friends. That sounds creepy. I'm gonna

699
00:43:58,320 --> 00:44:02,440
Speaker 1: say that anymore. Yeah, maybe a lot harder. Yeah, language

700
00:44:03,400 --> 00:44:06,040
Speaker 1: she she said, bug for something. Well, we need to

701
00:44:06,040 --> 00:44:09,200
Speaker 1: find the language for this one works out. Um, let's

702
00:44:09,239 --> 00:44:14,400
Speaker 1: start an email thread. I love it perfect. So where

703
00:44:14,640 --> 00:44:17,359
Speaker 1: can the listeners find you both? You can find us

704
00:44:17,360 --> 00:44:20,560
Speaker 1: on the internet at t L d any podcast dot com.

705
00:44:20,680 --> 00:44:23,319
Speaker 1: That's the limit does not exist. L d any and

706
00:44:23,400 --> 00:44:26,840
Speaker 1: on Twitter and Instagram at t L d any pod

707
00:44:27,480 --> 00:44:31,880
Speaker 1: or of course, you know iTunes, Apple podcasts, the I

708
00:44:32,120 --> 00:44:35,520
Speaker 1: heart listening apps. Did you're all the places you get

709
00:44:35,560 --> 00:44:39,879
Speaker 1: your podcasts? Yea. We have faith in the listeners. They're

710
00:44:40,040 --> 00:44:44,359
Speaker 1: very they can find it. We do. We trust that

711
00:44:44,440 --> 00:44:48,719
Speaker 1: what you're all as smart, if not smarter than we are.

712
00:44:50,120 --> 00:44:51,759
Speaker 1: And if you want to find us, if you want

713
00:44:51,760 --> 00:44:55,280
Speaker 1: to email us, you can. Our email is Stuff Media,

714
00:44:55,360 --> 00:44:58,080
Speaker 1: mom Stuff at iHeart media dot com. You can also

715
00:44:58,120 --> 00:45:00,040
Speaker 1: find us on Instagram at stuff I've Never Told You

716
00:45:00,280 --> 00:45:03,440
Speaker 1: or on Twitter app Mom Stuff Podcast. Thanks as always

717
00:45:03,440 --> 00:45:07,560
Speaker 1: to our super producer Andrew Howard. Thanks to you for listening.

718
00:45:07,920 --> 00:45:10,320
Speaker 1: Stuff i'man Ever Told You's a production of I Heart Radio.

719
00:45:10,480 --> 00:45:12,560
Speaker 1: For more podcast from i heeart Radio is the iHeart

720
00:45:12,600 --> 00:45:14,839
Speaker 1: radio app, Apple podcast, or wherever you listen to your

721
00:45:14,840 --> 00:45:15,440
Speaker 1: favorite shows,