The Robot Factory

Daila: What?

what were you doing?

Caleb?

Caleb: Oh, I have club soda to clean
out the peanut butter in my mouth.

Jonathan: Why you soda
to clean peanut butter?

Do you not use milk like a normal

Caleb: milk

Daila: Oh,

no.

milk?

would just coat it.

Caleb: just

makes it all gummy.

Jonathan: what,

Caleb: So this

wasn't, I didn't plan this.

I just had some club soda in the fridge,

but I was like, that'll work well

cause it's like, yeah.

I

Daila: I feel like the
bubbles like would lift it and

Caleb: it would

break up the peanut butter.

Daila: like the scrubbing bubbles.

Yeah.

Jonathan: right.

All right.

There's a little borax mixed in there
and just like clean as a whistle.

Daila: foaming at the mouth.

Caleb: there's potassium bicarbonate,
which I think is basic, maybe.

Jonathan: Yeah.

I mean, you supposed to use
club soda to get certain things

out of certain stains out.

Right.

Caleb: Yeah.

Peanut butter's like a stain in my mouth.

Daila: That doesn't sound appetizing.

Jonathan: Somebody do an intro.

Caleb: Um, no.

Jonathan: Okay.

right.

Well welcome back to The Robot Factory.

Uh, a podcast where we
deal with insubordination

and . Dirty peanut butter mouths

Caleb: mutiny, mutiny, mutiny,

Daila: I was gonna say, this is gonna be
the episode where Caleb gets fired for

Jonathan: Yeah.

Caleb: Where I get fired?

No, this is the episode
where I fire everyone else.

Jonathan: Ah, he's taken

Daila: a second.

Jonathan: He has taken over.

Caleb: I am the The C E O O,

Jonathan: What's that?

Caleb: chief Executive Officer.

Officer

Daila: The bubbles have gone to your head.

Caleb

Caleb: Yeah.

Jonathan: in the morning.

Caleb.

I think it makes you, uh, sort
of a weird energy when you're

bubbly and peanut buttery.

Caleb: Yeah.

Oh, well.

Jonathan: I've tried making
a peanut butter soda.

Caleb: Yeah,

Jonathan: Um, it's not,
yeah, it's not good.

No, it's not good.

I make, I make , I make a peanut butter
syrup, um, that is delicious in coffee.

I like drizzle it on coffee and
stuff, uh, or on, uh, lattes.

It's super tasty and I tried making
it, I tried pouring it into some

carbonated water and it was gross.

Daila: Yeah.

Jonathan: not very good.

Daila: doesn't seem right.

Jonathan: But you know what, like I have
this theory that um, there's probably some

flavor combinations that exist that are
delicious, but we don't think to do it.

And um, it actually stems from when as
a kid I was always marveled at people

who liked tomatoes cuz I hated them.

I thought they were so gross.

And people would like eat
a tomato, like an apple.

Like who does that?

Why are you eating just plain tomato?

That's so disgusting.

But, I just eat tomatoes regularly,
just in case I flip a switch in my

brain and start liking tomatoes.

And I don't wanna miss out.

I don't

Daila: You still don't like them?

You don't

like tomatoes?

Jonathan: I, mean, they're okay.

Like, I'll eat them, but
they're not my favorite thing.

Like, they're not like, Ugh, tomato.

Mm, give me more tomato.

Like, that's not,

Daila: Not even like those
little ones that you just like

pop in your mouth like candy.

Caleb: ones

Jonathan: no.

Like, I don't think of them as candy.

They, they have like,
it's like, I don't know.

It's, no, they're not.

They're not great.

They're

Caleb: felt that way about a
lot of fruits and vegetables,

Daila: well it's cuz you're

Caleb: because I learned I was

allergic to

Daila: everything.

You

Caleb: like, why do people like cherries?

They, they make your mouth all
itchy because of the way they are.

Jonathan: Well, welcome back.

We have skipped recording several times,
but also I've skipped publishing several

times, so, um, we're still . We're

Daila: all works out.

in the grand scheme of things.

Jonathan: does work out.

I think the last episode,
uh, so the, the, Hmm.

Actually, what is the last episode?

I haven't, I haven't edited it yet.

I haven't even downloaded it.

I don't know what we
talked about last time.

We recorded.

I think we were talking about recruit
radar, app that we have built.

Daila: Yes.

Jonathan: To help people
hire values-based hiring.

Um, I know we talked about at some
point, we talked about the, uh,

conversation we had with, uh, my
friend Chad about our process.

Um, he has since, uh, sort of
emailed, um, some feedback.

Just like some random thoughts
that were really, really great and

you Daila have finished it, right?

Well, you

Daila: Oh,

Clark did, but I'll take the credit.

What Clark?

Yeah, no.

Um, yeah, Clark.

Clark finished it up.

Um, I did my homework.

I have a meeting with an HR person today

Jonathan: yes.

Awesome.

Daila: to, to show them recruit radar.

Um, they're very intrigued
by my one sentence

Jonathan: What was your

Daila: of it.

It was,

Jonathan: Because if it's that
intriguing, maybe we should

Daila: Well, maybe it's not,
maybe she's just being nice to me

cuz she's a lovely person.

I, I just said that, you know,
we have a unique way of hiring.

We're very values focused
and we anonymize applicants.

She liked that.

Jonathan: Oh, okay.

That's, yeah, that's

Daila: So that's not really a sentence
that's like bullet points and,

and I can't remember what I wrote,

but, but she agreed to meet with me.

Jonathan: Yeah.

Sweet.

I also have meetings, so I have,
I haven't completed the homework.

I have like, started the homework.

I've got a meeting with my HR person.

I've got a meeting with, uh, Sadie.

So Sadie, she helped us define our values.

Way, way, way, way back.

She's got a lot of understanding
about values and how that all works.

Um, yeah, so she's, her and I
are gonna go for, uh, go for, I

don't know, coffee this afternoon.

Um, she's got

also things she wants to tell me.

I've got things I wanna tell her.

We're gonna complain
to each other, I think.

Daila: Oh, it's

Jonathan: I've, I've titled the meeting,
uh, complaint department and we're

gonna file our complaints at each other.

Um, yeah, no, I'm excited, I'm excited
to, to feel this out a little bit more.

Um, cuz you've got, you've got
something that's ready, essentially.

Right.

Daila: it's, it's like beta testable.

Jonathan: Could we, so

Daila: get some users.

Jonathan: who, how, if, if someone
were to like, We don't have, we don't

have it up on, on a like public,
publicly accessible website yet.

But if we did, um, we had a domain
name for it, which we don't.

Um, but if somebody were to like,
stumble upon it and say, oh my

gosh, yes, please gimme this.

Could they, could they sign
up and go through something or

Daila: Yes, they could.

Jonathan: Really?

Daila: Yeah.

So I mean, all we really need is a
landing page and a domain name, which

this is gonna be my goal for today.

we'll just throw something,
something quick together for a

landing page and ba boom, BA bing.

Jonathan: Boom.

But bing

Caleb: It has a name, right?

Daila: Well, it's called Recruit Radar,
but we haven't bought the domain name yet,

but I believe we picked that
name because it didn't exist.

I'll figure it out.

Jonathan: Do you know how to
buy domains through our thing?

Daila: Nope, and I usually
just send a message and Caleb

does it, or somebody does.

I know how to delegate

to.

Jonathan: you know, how to send on Slack.

Please buy this domain name

Daila: exactly.

I don't know if I have access to that.

I

Jonathan: I don't know if you do or not.

Daila: yeah, I'll

Caleb: speaking of access,

Daila: access.

Caleb: I need the company card.

Jonathan: Why?

Daila: Okay.

because he is the C E O O

Jonathan: Oh, right.

You're the c e o now

Caleb: No, no, no, no.

Because,

Jonathan: money on new

Caleb: because I,

Jonathan: video

Caleb: I, I, no, I need to
spend money on food and gas.

Jonathan: Oh.

Daila: Oh, cuz we're shipping you out.

Caleb: Yeah,

Daila: That's right.

Caleb: was not the avenue to discuss

Jonathan: No, it is not.

It is not.

How, how

Caleb: but it just kind of popped

into my head.

Jonathan: Um, I mean, share an update.

Like, what are you doing?

Where are you going, Caleb?

Caleb: Well, have we

have we talked about, no, I'm not going
anywhere and I'm not doing anything.

Sorry.

Jonathan: Um, we can
say you're going Yeah.

Going on a trip.

Yeah, you're going, you're going

Caleb: favorite rocket ship,

Daila: I

was just gonna say that Caleb, my

favorite R Do you not
know the little Einsteins?

Caleb: He

doesn't know

Jonathan: dunno what

Daila: on a trip in my
favorite rocket ship.

Soaring through the sky.

Little Einstein's.

Let's go aio.

I really didn't like that show
when my kids were little, but

Caleb: Oh,

Daila: stuck in my head forever.

Caleb: do you not like music?

Daila: Well, it just got
annoying after a while.

Like they were flaunting that I didn't
understand what they were talking about.

I don't know what adagio means.

Caleb: I mean, slowly.

Daila: that makes

sense.

Why they would tap slowly

Caleb: Italian, but in music?

In music Italian.

Jonathan: What?

All right, let's get

Caleb: think it is normal Italian.

Jonathan: can't publish any
of this, cuz this is, this

Caleb: Well,

no, we haven't actually mentioned

anything.

Jonathan: know, I know, I know.

But I gotta delete all of this
because none of it makes sense.

Daila: Yes.

Caleb: doesn't have to

make sense.

Jonathan: the thread of this whole thing.

Caleb: Well, my mom listened to the
podcast just to hear my voice, so

Jonathan: Well, you can talk in a minute.

Caleb: Okay,

Daila: but recruit radar.

Ba ba.

Jonathan: You're gonna get,
you're gonna get the domain name,

Daila: Yep.

Jonathan: uh, put together a bit, bit more
on the landing page and it's ready to go.

Like we could have somebody
in there trying it out.

Daila: Yes.

Jonathan: Sweet.

Okay.

What, tell me what it does.

What are the features?

Describe it.

Daila: Let me describe it, man.

Maybe we should have had Clark on here.

Um, but what does it do?

Okay, so starting at the very
beginning, you can, you can sign

up as a somebody who wants to hire

Jonathan: Right.

An employer,

Daila: an

employer.

Yes.

Um, sign up.

Jonathan: employees?

Daila: you can add employees.

So you sign up as as an employer.

Um, and then you can create questions.

They can be value-based.

They could really be any kind of

question you want.

It doesn't have to be around values.

That's just what we use it for.

So you create a job posting and
you say, I want applicants to

answer these three questions.

Or four questions or
five, whatever you want.

And it creates a unique URL that
you can then send to applicants,

um, the applicants access, go
through that URL to access a page

where it shows 'em these questions.

They type in their answer.

Um, and submit it.

No where do we attach their
identifying information that's visible.

Um, this is where you,
the employer invites their

employees, their hiring team,

um, how whoever they want to
invite and they see the responses.

Again, no identifying
information is attached

to these answers.

You read them and then you
rate them one to five on what.

Like how you feel that
answer, um, was and yeah.

And then once it's all
done, you randomly do it.

Everybody get, every answer gets,
uh, three ratings right now.

Um, and then there's an average
that spits out and a hierarchy of.

Jonathan: Then do you get to see, like,
how do you, how do you get in touch with

Daila: uh, only the employer like can

see.

it.

None of the employees can ever
see any of this information.

There is an option then for the
employer, uh, to view all applications.

Jonathan: Sorry, when I said employee, I
meant like a potential candidate, but y

Daila: Oh, sorry.

Jonathan: employees No, I, I, I misspoke.

I think the correct term
would be the candidate.

Um, but yeah, you can add, you
can add your own team to do

Theri to, to do the rankings.

Uh, okay, I see.

I

Daila: so that

it's not just one person.

Um, which I really like about how we do it

at our company is everybody
randomly gets, sign some answers and

Jonathan: Nice.

Okay.

Okay.

That feels like this
feels like a proper mvp,

Daila: Yeah, right?

Jonathan: Nice.

Okay.

Very, very exciting.

Daila: so it is very exciting.

It feels nice, like Clark
did an amazing job of.

Of stepping back because I feel
like in the beginning we learned

a lot of building the wrong thing

at the wrong pace.

Jonathan: What was wrong?

Daila: Um, I don't know.

We built features that none of us
had talked about, but I think made

assumptions that, hey, that could be cool,

Jonathan: Mm mm-hmm.

. Mm-hmm.

Daila: wasn't mvp.

Um, so huge kudos to Clark.

We stepped back, we peeled it, peeled
it, peeled it, and came up with a.

Jonathan: Peeled.

What?

What is the,

Daila: all the features that

didn't make sense

right.

Now.

Jonathan: what, what,
thing are you peeling?

What is this?

Daila: What did we peel?

Jonathan: for me.

Finish the metaphor.

Daila: We peeled?

the the banana layers

of, uh, Like there, there was a feature
where you could invite, um, people

not from your team to rate answers.

If you say

had an HR consultant or something like

that, which mvp, we didn't need that,

so we.

Peeled that layer off of the onion.

Onion would be better.

Bananas have one layer, right?

Jonathan: it.

Okay, so let's get the landing page.

Let's get, uh, the domain.

Can we have another goal for, I
mean, we're gonna talk with the

people that we're gonna talk with
and get some of that feedback.

But, um, what else can we do?

Like, like, we've got this,
we've got this thing here that we

could, we could do something with.

That's more than just having
a conversation with some

of our friends about it.

Um, what else?

What else could we do?

Can

Daila: Well next I think we, we set
our little marketing guru off on it.

So, so at at Little Robot we have
defined our process of, of, uh, create

something and then Nick goes and talks
to however many people to see what they

think and try and get some, some users

on there.

Jonathan: Yeah.

Daila: So that's where, where we're at.

But if any listeners know people.

Jonathan: Yeah,

Daila: send them

to us.

Jonathan: Good call.

We gotta, we've got this
massive, massive audience that

we have yet to really leverage

Daila: We have another listener,

Jonathan: What,

Daila: Toby.

I don't know Toby yet.

I'm gonna gonna meet him tomorrow.

Um, anyways, he's a new listener, but,
but yes, we, we also need, uh, valuable

feedback from one of our listeners.

We need a.

an email address posted with this
podcast so that they can contact us.

Jonathan: Oh, okay.

Daila: So info@twostoryrobot.com

works.

Jonathan: Get in touch.

We want to hear all your hairy
hiring stories and how values-based

hiring might be able to help you.

Daila: Yes.

Jonathan: Um, or if you're
just like curious, what the

heck does that even mean?

Um, we're happy to chat about it.

I think.

I think one of the interesting
things that came out of chatting

with Chad and one of the ideas was,
um, community around this, right?

If there are other, other organizations
that are like us that really don't

know what they're doing, like
we don't know what we're doing.

We don't have an HR person.

Um, we, we have some consultants that are
a consultant, actually two two consultants

cuz um, they're a team, um, that we use
occasionally when we've got questions,

but we don't have somebody on staff.

and, uh, lot, I think there's lots
of other organizations like us

that don't really know what to do,
and maybe we can get together and

say, Hey, here's what's working.

Here's what's not working.

Let's do this.

Uh, and maybe we could sort of be
a steward of that community, which

would be, which would be pretty cool.

Um, so I'm excited.

Daila: like

that.

Jonathan: excited to
kind of explore that too.

Okay, cool.

We've got, we've got some,
we've got some, uh, some action

items, some things to get done.

Excellent.

Very

Daila: yes.

Jonathan: Okay.

Caleb, your turn?

Caleb: turn.

Guess what I built?

Daila: What?

Jonathan: Oh, yeah.

D doesn't know.

Daila: I don't

know.

Jonathan: wait, is it, is it
the same thing that we're, Is

Caleb: I built sentient.

Jonathan: it the brain?

Caleb: I built a brain,

Jonathan: Yes.

Daila: Oh,

Caleb: digital brain.

Daila: excellent.

Jonathan: Tell Della about it.

Daila: I am edge of my seat.

Caleb: Well,

Daila: but I could

Caleb: The most important part
is that I've lost the tab.

Jonathan: That's the most important
part is it's he's done all this work,

forgot to hit save, and now it's gone.

Caleb: Oh,

I found it.

Oh,

okay.

No, the second most important part.

Brain

Jonathan: wait.

Now tell, tell the whole thing.

What is,

Caleb: No, that's not important.

Brain is

an acronym.

Jonathan: but like what is
the name of the product?

It's not

Caleb: I Okay, but I'm not
gonna, oh, it's called two.

Two Story

Brain,

Jonathan: Brain.

Caleb: because

there's two stories.

Jonathan: Uh, just whatever.

Okay.

Caleb: Anyway, it's an acronym,

but I'm not gonna tell you what it is yet.

Jonathan: Okay.

Daila: do I have to like guess because

this is gonna be a bad game.

Okay.

Caleb: no,

Jonathan: he's not gonna tell us at all.

this is

Caleb: I'm not gonna tell you.

Jonathan: twisted sense of humor.

Daila: love it.

Jonathan: this is Gen Z

Caleb: Yeah.

Jonathan: in action.

Caleb: No one will

Jonathan: What is the brain?

Caleb, what is it?

Caleb: The brain is, um, essentially,
this isn't really what it is right now,

but it's hopefully what it will be is
it's, is it's like a place where you

can ask questions about past projects
that Two Story Robot has worked on

and get answers that are hopefully
correct and not made up which, well,

yeah, we'll see, see about that.

But essentially we'll feed it like a big
list of, of documents that just document

past projects that we've worked on.

It's using a tool called LangChain, and
it's using open AI's API to generate

embeddings for these documents.

So basically it's like, it's just like
a bunch of numbers that represents

like the meaning of the document.

Um, it's kind of tricky to explain,

Jonathan: Yeah, it is.

I'm, I'm listening to it
like, yes, it is, it's

Caleb: yeah, it's hard to understand
But, No, I don't think I know enough

about it to give like a analogy.

Jonathan: we're, we're,
we're playing with fire here,

Caleb: Yeah.

But basically, basically what it'll do is
because, because stuff like, like GPT has,

it has what's called a, a context size,
which is basically just the amount of

text that you can, you can feed into it.

And it's, it's like a
relatively short limit.

It's based on tokens, so
like little chunks of words.

And I think it's 8,000.

Jonathan: Mm-hmm.

Caleb: Um,

Jonathan: tokens.

Caleb: so it's quite short.

So like our, our list of all our
projects and all that documentation

would be much longer than that.

So you have to use strategies
to kind of split it up and then

smartly search for that and then
send only the relevant information,

um, to the large language model.

Um, So that's kind of what
the embeddings are for.

So you can have like a search
query and then generate the

embedding for that search query.

And then because it's like a number
that represents the meaning of the text,

more or less, you can find the chunk of
the document that's like the closest.

So if you imagine there's like a 3D
space where you have all these like dots

around where each dot is a document and
the distance from each other is like how

close, how close they relate semantically.

So like.

maybe if you had a chunk of document
that was about like golden retrievers

and another chunk of documents
that was about poodles, they

would be close together in space.

Um, and then you can, it's basically
just like you finding the closest

document by like how far away it is.

That's kind of interesting.

Um, it's not actually in 3D space,
it's in 1,536 dimension space,

so you can't really perceive that, but.

It kind of works.

The problem is when you have information
that spans lots of documents.

So there's some strategies for that
that I'm gonna explore around, like,

you could, you can do this in like an
iterative way where you, you get an

answer for each of the chunks and then
you combine those and get the, the large

language model to summarize those together
and give you like a combined answer.

but basically, You just ask it
things about Two Story Robot and

it should give you answers that it
otherwise wouldn't know because GPT

wasn't trained on data about us,

unfortunately.

Daila: That is so cool.

Caleb: Oh, and brain.

It says for Brilliant Robotic
Assistant with Infinite Nonsense.

Daila: Boom.

Love it.

Caleb: And I didn't come up with that.

ChatGPT did

Daila: Oh,

Jonathan: Yeah, it's, it's.

. I mean, it's not , the functionality
of, it's not that impressive cuz it

actually is kind of, uh, disappointing
in in the answers that it gives . Um,

but it has been, I think it's been
really helpful to understand more

about where the limitations are and
what the capabilities are and what

we can do with it or without it.

Um, That was one of the goals is just
to like, get, get some eyeballs on

Caleb: Yeah.

Jonathan: stuff cuz it's, it's really, uh,
we might be in a hype cycle, who knows?

It's hard to,

Caleb: we're definitely in a hype cycle,

Jonathan: but who knows if we're at the
top of it or if it's still more to go.

Um, but there's definitely a lot of,
a lot of people experimenting and

people excited about this stuff and
we don't really know anything about

it despite, despite having some really
smart people and somebody who is a

computational neuroscientist on our team.

So we just don't, we don't.

Do enough of this stuff or talk about it.

So, um, Caleb's been playing around, um,
but with a specific goal of how do we,

can we answer, can we scratch our own
itch, which is even for me, and I have

the entire context of this company in my
head, I don't have every little detail.

. Um, but for somebody new like Daila,
do you, do you know all of the projects

that we've worked on and some of
the weird things that have happened?

No, of course not.

But to be able to like ask some
of that would be, I think, pretty

powerful if we could make that work.

Especially for some of the
BizDev stuff like Nick.

On our team.

Um, he's, he's, he's quite new, like he's
only been here for a month and a bit.

Um, he has no clue on some of the
projects that we've worked on.

And so he'll get a request in saying
like, Hey, we're working on this, uh,

this project that looks like this.

And he might ask somebody, Hey, have
we done anything like this before?

And the answer is probably yes.

And it's like this, these kinds of things.

Um, but that takes, like, that
takes a lot of conversation and

it's kind of, it's kind of slow.

Um, So our hope is that we can build
this brain to feed it all of our

data and have it be able to respond
to anybody's, anybody on our team.

We don't, we're not gonna
make this a publicly

Daila: I was gonna say, are
we putting it on our website?

And

Jonathan: No,

Daila: can ask

questions.

Jonathan: I don't think so.

Cuz I, there's some things
we can't share, right?

Like we've got some

Daila: yeah.

Jonathan: can't, we can't talk
about, we've got some projects

that we have non-disclosures on.

Um, so we want to keep our notes
so that we know like, oh, somebody

wants to do a project having
to do with, uh, well, what's a.

Somebody wants to, have you ever
done any avalanche weather stuff?

And we can say, yeah, in fact we have,
uh, here's what it was and here's how

it worked, and here's some of the fun
things we learned and blah, blah, blah.

Um, kind of thing would be, um, ideal.

, this is maybe a good strategy,
um, but I'm trying to like, Do

many things at once with this.

So not just like learn about the AI tools
and skills and, and developing our skills

and that building this document thing
for ourselves, this brain for ourselves.

But if we can figure that out, I have a
few product ideas that I think we could

leap off, uh, leapfrog from this project
onto another project, um, that I'm.

of, kind of excited about.

So, um, if, if this works and, or we can
figure out a path that we can make it

work, um, there's a few, there's a few
other things that I think I want to try,

um, product-wise that we can definitely
talk about on this podcast at some point.

Actually, I'll hint about it.

Right now, I want to build a, uh, a
tool for teachers to help them keep

track of notes about students to make
report card writing a lot easier.

Um,

Daila: Oh.

Jonathan: I don't think that's a
huge money maker, , cuz teachers

shouldn't be buying this kind of stuff.

Like stop making our teachers pay out
of their own pocket for things, please.

Um, but it's a huge problem.

I am sick and tired of all the teachers
in my lives having to take time away

from their families or even from work.

Like, call in sick and do report cards.

Uh mm-hmm.

No, I don't wanna do that.

So I wanna like, I wanna
try and solve some of that.

Um, and I think, I don't know, I think,
uh, some of these large language models

might help with that and, uh, yeah.

Daila: Very cool, intriguing,

Jonathan: intriguing.

Anyways, it's very exciting.

The work that Caleb has been doing so
far has been fun and hilarious to watch

cuz it just, it will just make stuff up.

Caleb: Yeah.

Yeah.

That's probably the biggest reason why
we never make it publicly accessible.

He would just lie about stuff like
Chu would ever work with nasa?

Well, yes.

We helped NASA develop their
Saturn five rocket in 1969.

Daila: We did.

Caleb: It's so confident too.

Jonathan: Uh,

Caleb: Surprise

Jonathan: yep.

Yeah,

Caleb: bet.

it's cool.

Helps to see through both the hype
and my own cynicism around it.

Jonathan: Oh, yeah.

I, I

Caleb: I'm very cynical, I mean, of things
in general, but especially things that.

Are in a hype cycle, like, well, it
has hype, which means it's not useful

at all and it's completely stupid,
but I think it has some use I still

don't think I'm entirely wrong.

I think it's, it's capabilities are
super overstated, but it's been helpful

to look at it more, more objectively

than just my own opinions that don't
really have any basis in reality.

Jonathan: Sweet.

Caleb: So, yeah.

You've been listening
to The Robot Factory.

I've been your host, Caleb Sharp,

Daila: I've been Daila Duford.

Jonathan: And I'm Jonathan Bowers.

Caleb: and this entire
episode is written by ai.

Goodbye.

Jonathan: Bye-bye.

Daila: It's fantastic.

Oh, that, I thought that was him.

That

Jonathan: Oh.

Daila: Look at your
foley work is incredible.

Jonathan

Jonathan: Yep.

Caleb: imagine.

Daila: Now do a horse galloping.

Jonathan: Uh, clap, clap, clap.

Clap, clap.

Daila: Oh my.

It's so real.

Jonathan: That's a, that's the sound.

The horse.

That's the sound the horse
makes in, uh, this Dr.

Seuss book.

Uh, Mr.

Brown can moo, can you?

Uh,

Daila: Oh, I know

Jonathan: Clap, clap, clap.

Clap,

Daila: clap, clap.

clap, clap,

Jonathan: make the sound of horse.

Feet.

Clap.

Clap, clap.

Clap.

Caleb: clap, clap,

clap, Clap.

Clap.