Join us as we gather around the hedge,
where we dig into technology,
business, and culture with the finest minds in
computer networking.
Well, hello, Tom.
Hello, Russ.
You are there. Your plant is there. Your
whiteboard looks far too neat. I think I
need to call your wife and get your
kids in there to do something about that
whiteboard.
I need to do some artwork.
Yeah.
Be creative.
Yeah. Exactly.
Be creative.
What is the problem?
Hello, Yvonne. You heard Yvonne there.
I'm here.
In the she shed, you moved the frog
lamp. I wanna understand the story behind moving
the frog lamp.
Well, I was actually,
working on some recordings
for my day job, and I just thought
it would be I would I'd I'd love
the frog lamp to have a more prominent
place. He was behind me. Mhmm. He's sort
of a joke between me and a a
a friend of mine. And so I figured
I'd put him a little make him a
little more visible because he's fun. Yeah. He's
really fun. He's cool. That's cool. Great.
Yeah. I always rearrange the stuff behind me
from time to time. I take everything down,
dust the shelves, and then I, like, wipe
down the shelves, and then everything ends up
going back in a different place for the
most part.
We don't we don't need to talk about
how frequently
I dust or vacuum the shed. It's not
a topic for conversation.
Oh, yes. So there you go.
So it's time for a roundtable. Let's talk
about a couple of articles that came off
Mind Matters,
which, by the way, if you're not following,
it's kind of a cool blog.
They do a lot of stuff on artificial
intelligence and technology and stuff. It's not really
a technology blog. It's more of AI and
culture stuff, but it's pretty interesting.
So, anyway,
the one is the slow decline of a
key aspect of creativity,
and the author begins by talking about serendipity.
Okay. Do we need to define this word
for
all of our listeners? Are we gonna let
them go look it up in the dictionary
because that would be
they might discover the words around it and
have some serendipity.
I'd like to hear your definition of serendipity,
Chris.
When you're searching for one thing and you
run across another that's really interesting.
I've always thought but think of the rom
com, Serendipity, that came out in the nineties,
and I have a hard time hearing the
word without thinking about
Cusack and I don't remember.
Kate Beckinsale?
Yeah.
I have no idea. Yeah. It's yeah. I
yeah. Sorry. That that word is forever tied
with that movie for me. So
I I think I think of it more
I don't know if I have the definition
right, but I think of it more as,
running into as happening to people, maybe ideas,
but people, and running into somebody which
in a way that turned out to be,
the root of something really important.
Alright. So just extend that to ideas.
It's the same thing. Just extend it to
ideas.
And I also think it hints at surprise
and delight. Like, it's it's it's you come
up upon something unexpected
that's delightful
or interesting
or sparks
curiosity,
imagination.
Right? So that's,
that's why the word's important for this conversation.
Yeah.
So this author talks about serendipity experiences
online.
You're clicking through a chain of links, and
you suddenly discover something
that you weren't
looking for, but is really important or interesting
or fascinating or sparks you to go do
something else.
I can give you an example, you know,
I probably my grandfather kept bees. I probably
never would have thought of keeping bees, but
there's a couple of people that I follow
on various social media sites who do, in
fact, keep bees.
And so there's, like, this serendipitous,
oh,
I could do that too. Like, that that
gives me an idea
of something I could do. I can't do
it here, but I can do it at
the new house whenever we build that. I
could do that too. Like, that's that would
be a cool thing to do is to
is to keep bees, to get five or
six hives and
and keep some bees.
So but I never would have thought about
it without running into somebody who did it
or does it and talks about their their
stuff.
Another one
I'm trying to think of others, like, in
the technology world, more closely related to the
technology world,
but there have been many times when I've
been working on a problem.
And in the middle of working on the
problem, I realized I've solved another problem that
I wasn't trying to fix.
Right?
That's Well, there there are all kinds of
examples of that when it comes to,
like, medication,
you know, like or a a great one
is the adhesive that's used for sticky notes.
Like, the three m were making one thing
and then that it didn't work out quite
right.
And and then they realized they could use
that adhesive
for sticky notes. Or Scotchgard was a similar
kind of discovery. Somebody dripped it on their
shoe and realized that that chemical and then
realized that that spot on their shoe never
got dirty and Scotchgard was born. So there
are all these interesting,
you know, serendipitous
discoveries. You're trying to do one thing, and
then you stumble upon something else.
Yeah.
So
in this article that that the author,
Eric J Larson, says
that something else is a surprise because we
weren't looking for it or at least didn't
think we were, and yet it turns out
to be exactly what we wanted or a
more compelling or more compelling than we originally
sought.
So
I think the thing is is that in
the modern world, instead of serendipity,
we have doomscrolling.
I I think that's a that's a problem
with our attention spans and the way we
do things.
That's not what this article is talking about,
but I think it's absolutely true that
we tend to think of doomscrolling
as serendipity.
Like, we're gonna discover something. I might find
a new cat video.
I might find a new duck video or
whatever it is,
or some person falling off the trampoline in
a new and interesting way. I don't know.
That's not really
what this is talking about, because those might
be fun to watch, but they aren't they
don't lead to productive places.
And I think one of the points of
serendipity is
that where you end up is something,
I I hesitate to say useful,
but maybe useful is the right word.
I don't know. So it's something more than
another laugh.
I think that think go ahead, Tom.
The
the just thinking about doomscrolling,
the money is a lot easier,
if you feed engagement with rage
and and, fighting with your neighbors and stuff.
Like, it's just a lot easier to get
people involved when they're mad. And so, like,
I I don't know. I guess it's the
cynical part of me, but
the monetization
of of our attention
online,
it's just easier to get people going when
they're when they're upset about something. And so
serendipity never felt to me like a negative
thing. It never felt like,
you know, I went from feeling bad to
worse sort of experience. It always felt like
the meeting of good ideas or the meeting
of good people or whatever. Yes. And so
it's kind of its antithesis is ruling everything
because it's probably a lot more profitable,
to to doom scroll than it is to
have these,
the the these positive connections.
So go ahead. It's certainly more predictable. And,
and I think too,
a lot of a lot more of our
online experience is curated than we want to
believe. Right? The the algorithms
really
are finely tuned
to detect our interests
and then defeat us the things that they
know
we want, which is the opposite of serendipity.
Right? If it's predetermined.
And so often,
you know,
I've
in my mind believed I was thinking about
doing a thing, and then all of a
sudden I start seeing those kinds of things
on my feed without
intentionally explicitly searching for it. Right? But clearly,
there was something in my behavior
that indicated to the algorithm.
Whether it's like, you know, buying a new
car, going on a vacation,
or getting a new pair of shoes, like,
things like that that I don't think I've
told the algorithm about.
But suddenly,
it's feeding me things and it's it's not
because it's reading my mind. It's because it's
doing behavioral analysis enough to determine what it
believes I'm gonna do next. And it's
shockingly right. It often knows when I'm getting
into the car to go to the Taco
Bell. Yeah. Right?
And and
those experiences,
they they can be interesting. They can be
troubling, but
they they may have some of the same
markers of serendipity, but they're really not. Right?
They're they're they're more curated than we want
to believe. Yep. So I was just gonna
say, Tom, in relation to what you were
saying, I actually don't think
I think that we have two ideas of
what sells in the world. The of them
is is is sex sells.
The other of which is is anger sells.
I actually think that what really sells is
fear.
And if if you go back to
a lot of the anger based stuff that
you see on social media, it's actually fear.
It's undergirded by fear.
And,
you know, I'll say the same thing about
sex selling. Right?
Okay. You see that you see the girl
or the guy or whatever, but in reality,
you don't wanna be with that person. You
wanna be that person.
You fear missing out.
You fear not
being with that being with that person or
being that person.
So I really I really think a lot
of it comes down to fear.
And if we just let go of our
if we had a better
focus
on self forgetfulness
and go get things done instead of worrying
about
what other people are doing or whatever, it
probably eliminate a lot of that
pathway in our brains. But anyway, that's outside
the topic of serendipity. It's just something that
occurs to me. I liked,
where the article went with I'd never heard
of these three phases of creativity before.
Phase one generation, chaotic, undirected process of preparing
for ideas to germinate
permission where you step aside and let an
idea surface, and then translation where you where
you make it something usable. I I had
never heard this particular model of creativity.
You know, the author's thesis is that these
things are disappearing. But,
I don't know. My experience is that I
I feel like I have experienced these things
even recently.
I don't know. What what about you all?
Do you feel like you've had creative moments
in the last week or two?
Sometimes. Yes. I think one of the
and it would be an interesting topic to
explore further. The
the the the permission
component.
I find, especially,
you know, and and Jonathan Haidt, I've not
read the book, but I've I've read some
synopsis of it. His his book, The Anxious
Generation,
and talking about
I I feel like a lot of and
even the young people in my life,
they want their they wanna know what's next.
Right? And they get really uncomfortable,
like, when the plans aren't fully set or
when there's
they they can't map out exactly what's gonna
happen next.
Part of creativity
and this permission phase
is
being comfortable that you don't know what's gonna
emerge.
Right? And and, you know, leaning into,
mystery. Even that word is here. It's a
mystery even to those experiencing it That
that the thing that's coming next, you don't
know what it is,
but you're open to experiencing it. And it
could be good. It could be, you know,
something that's surprising and delightful. And I I
think that that's also something that's kind of
been lost in our very safe,
very curated,
both physical and online experiences. Yeah. We we
just want everything mapped out. And what that
does is it eliminates
the potential for
curiosity, creativity, and all those all the things
we've been talking about. And and we want
permission.
We want to know that somebody approves of
us doing this before we do it. And
the problem is you don't have to have
permission.
You can just go do things.
I mean, really, you can just go do
things. Like,
you can
you can go build a chicken coop.
You can build
beehives. You can
start a newsletter. You can start a pot.
You can just go do things.
And if they fail, well, they failed.
And that's okay.
But this permission phase is part of that
too, I think, Yvonne.
So the point the author is making with
all of this talk about serendipity and permission
and
translation and everything else
is that using Chat GPT,
whatever the other ones are, Copilot,
I don't know what they all are,
is actually harmful
to your creative process.
And it's harmful at every stage
that you mentioned there, Tom.
It's badly harmful
in all three stages.
So for instance, if we think about
generation,
if we allow an AI to write a
presentation for us,
we're not doing the generation.
We're not doing the work.
The brainstorming
disappears
because
why would I bother?
The AI is doing it for me.
So
I don't know if you if either of
you experienced that
thing. I know I have where I've been
working with an LOM for some reason, and,
I don't
I don't think that I need to spend
as much time creating
or thinking about what I'm creating because they
all, is feeding me ideas. I become the
filter.
And the l l becomes the fill.
Yeah. I never I guess I just never
use it in the two phases. Like, I
just never considered that. It's it's when it
gets to, okay. Here's something that I know
about what the shape it needs to be.
Like,
writing code for little little home automation projects
is what my my latest, use of this.
Like, I know what framework I wanna use.
I know this is the thing I'm trying
to accomplish, but I don't even tell the
LOM that. I just say this is the
this is the particular technical outcome.
And, you know, there's lots of ways to
use OOMs, and I'm not mine's not the
only way, but I just never even considered
that it would have anything to offer. I
don't want a regurgitation
of any fool on the Internet who had
something to say. I'd rather just I figured
my ideas are more valuable than that. Maybe
that's being, you know, maybe that's hubris. But
for me, the translation part of it, I
think there is there is work for LMS
to do there.
But the two, I just never used it
for.
Well and and I'm reminded of an example
in a book, and I cannot remember the
the the book that I that I read
this in. But it was talking about,
it may have been the book range,
but it was talking about
Nobel Prize winners
and how many of them
had broad experience across multiple domains. And there's
this school of thought that you take children
when they're very young, you choose a discipline
for them, and then you drill it. Like,
whether it's violin or chess or piano. STEM.
But
but but what the data says is that
often, it's best
for children to have lots of multiple experiences
across different disciplines and then let them specialize
as they get older.
The the other thing that that they found
was that, like, with Nobel Prize winners, those
that were,
the the vast majority of them had expertise
across multiple domains
and had maybe a hobby
that didn't align with their field of study.
So,
yeah. I mean, and it could be something
like gardening or studying philosophy or reading literature,
but but but their brains
were able to take the data from all
these different disciplines and combine them creatively.
And they were able to see connections
that other folks that are so narrowly
focused in just one discipline
can't see. And I think that that, like,
is is a super important thing for us
not to lose.
And and it it may also give us
permission to lean into a hobby or lean
into a thing that we are interested in
just for its own sake.
And those things can spark creativity and actually
make us more successful in our primary field
of study
because we have these broad interests. And I
think all of that becomes part of the
conversation about creativity.
Yeah. Oh, yeah. Definitely. I
you know, you talk about the breadth of
knowledge. Another way to me
that LLMs and relying on LLMs
causes us problems
is we now think
we get to the point when we're using
LLMs, like vibe coders. Right? Vibe coding just
sounds really dangerous to me, the whole vibe
coding concept.
Right?
So I don't need to know about this
field.
I can treat the LLM as an expert
in the field for me,
and therefore, I never really learned it myself.
Like, I think there's a real danger
in something like vibe coding where the LLM
is writing code that you can't
read. That, like, you're missing an essential component
of the breadth of knowledge that you need
to be creative
when you do that.
So
that again but that's more in the translation
phase, but still,
that is, I think, part of the problem
to me with this. It's interesting that you
say that. I had a had a conversation
with one of our,
one a developer I know at work who's
been doing a lot of development with LLMs
and code assistive technologies. And in talking to
him,
like and, of course, he is
a long term software architect and has been
writing code for years. But but what I've
kinda surmised
is that ultimately,
you still have to have a certain degree
of expertise to effectively use the tools.
And I think the further we go, the
more
we're going to find that. That the tools
can only get you so far
because
they need
human intelligence to guide them,
which which might be a good segue for
us. But at the same time,
you know, like, that requires human experience and
creativity
and all the all the things that, you
know, I think make us innately
human. I think the bottom line of this
article is
we
it is possible for us to lose our
creativity
by outsourcing
the whole creative process to something like an
LLM.
Yes. And it actually reduces us
as humans. Like, you can't get rid of
us being humans. Like, that's a that's a
fundamental part of what we are and what
we do. But you can narrow the scope
of humanness
by doing this to some degree, and I
think that's
a dangerous outcome.
So the other thing we're gonna talk about,
like you said, segue, is
the same place Mind Matters. They have an
article by Gary Smith talked about
a large language models, a lack of progress
report.
It's very interesting because there's a guy,
and I can't seem like I can't I
can't I don't know if he's in mind
matters or if he's over in ACM,
who every six months or so, he just
takes German words, German sentences,
and he has all the different LLMs translate
them.
And he says it's actually getting worse.
Like, their translations are getting worse, not better.
And he's very disturbed by this.
So
I don't know. There's this whole
thing
about LLMs
and
are they actually making progress?
Are they
becoming
better at what they do?
So, Tom, I don't know. You may use
it more than I do. You use it
more for code than I do. Do you
think it's getting better? Do you think they're
learning or do you think it's
fairly stable?
Like it doesn't like it's not really giving
you more clever solutions or it's not really
doing more. It's just
the same thing you've used it for.
Yeah. And I haven't used it a ton
for code, but I think
whenever I have seen it, I I didn't
I did not detect
that it was getting worse. I
I I guess I detected that,
I didn't understand
how to tell it anything useful.
But but that said, even even with my
limited vocabulary,
I was I've been able to, you know,
give it tasks that it could make me
make make me go faster. I have I
have yet to find something where,
I gave it instructions, and it revolutionized the
way I thought about a problem. It accelerated
mostly.
But do you think it's getting better at
accelerating or do you think it's I think
it's getting better. You think it's just staying
steady?
I would in my limited experience, I would
say staying steady, but I don't know. There's
probably people use it every day and they
would they would know. Well, I know it's
getting better at graphics. I know it's getting
better at
moving like picture, like videos and stuff like
that. I know it's getting better at those
things,
but it's solving what I would consider to
be real world problems.
Not not just the I'm gonna make a
dog cry type of videos, you know, or
I'm gonna do whatever. I'm gonna have this
put this person in this movie that wasn't
there before in this picture or whatever it
is.
I know it's getting better at that stuff,
but is it getting better at real world
problems?
I haven't it's not not apparent to me.
It's not obvious.
I think, you know,
for the eighteen months of AI,
the models
were improving so rapidly
that I think
I I think people got a false impression
of what the
trajectory
for improvement was gonna be over time. I
think they're improving, but they're not improving at
the same hockey stick rate
as they were as far as the pure
models.
I think the thing that's happening now
is the entire marketplace is figuring out where
do they fit and how do we use
them. So now instead of doing code complete,
you have tools that are that,
for software development
that
are feeding
the, you know,
they're more interactive.
And so a developer writes code,
the,
the model generates some code, and then there's
this inter iterative
and interactive process with the developer
that's much less,
it's more automated than it was,
before. And so that's
increasing the ability to use them. I also
think, like, we're still figuring out how to
integrate
LLMs
into
larger systems.
I think, you know,
and
and that's where we're gonna see, I think,
the next set of advancements
is how are we gonna practically plug these
tools in
to something to make a bigger thing. I
mean,
the the models are truly fascinating,
the when when when you see them and
experience them. But the more you play with
them,
the the more
you can detect patterns and and and maybe
I've just played with them enough that I'm
not as astounded.
But I think there's a lot of work
still yet to happen
to integrate them in ways that have really
meaningful
impact. And that is gonna take a lot
more work and frankly, a lot more skill
than
I think the the it's it's not a
magic bullet. Like, the market has been like,
oh, these things are, and they are gonna
change everything.
I firmly believe that at the end of
the day,
AI is gonna be integrated into our world
like light switches.
Like, something that was at one point in
time this
phenomenal amazing technology. You flip a switch and
in the middle of the night, an entire
room is illuminated and that's amazing.
I think
LLMs are gonna be more integrated into the
fabric of our lives, but it's going to
be that sort of thing. I think, ultimately,
it's gonna be the more boring stuff,
that that they're gonna do.
So there's this great quote by Andreessen, Mark
Andreessen, that,
Andreessen, I guess it is,
that this article points
to where Mark is saying,
we've really slowed down. This is in November
2024.
We've really slowed down in terms of the
amount of improvement.
We are increasing GPUs,
but we're not getting the intelligence
improvement you might expect
from increasing the GPUs.
And I think that's
I think that's really the thrust of the
focus of this particular article is yeah. But
the other thing he says is very interesting
in this article
is
LLMs are getting better at fact giving factually
correct answers
because
thousands of experts with specialized knowledge
are doing extensive post training
to correct the errors.
Right?
That is absolutely true.
And what that means is is that
if you ask the LOM to do something
that it's never been asked to do before,
it has never been trained to find the
correct answer,
and the answer is gonna be unreliable.
That's an interesting
the author says,
elements cannot be trusted to answer prompts that
experts have not anticipated
nor to generate answers that require specific timely
details
and involve uncertainty.
And that's probably
always going to be true.
I think that's just that's gonna be a
limit.
I saw,
an article the other day,
called generative I a hey. Sorry. Generative AI
runs on gambling addiction. Just one more prompt,
bro. And it talks about how Oh my
goodness.
It is actually really interest it's pretty entertaining.
It's really interesting. It talks it's mostly around
software developers use of, LLMs, but,
it talks about how it it works the
same way with the same reward system that
the gambling addiction kind of feeds into and
the devil knew path and everything.
And basically talks about uncertainty
and how, you know, as a developer using
it, at it's, like, so awesome. It's like
winning when you give it an instruction and
it comes back and tells you what you
need to do. But then a little while
in, you get you you get information that's
not useful from it anymore. And then that's
kind of like kind of like losing, you
know, in the whole in the whole addiction
cycle. And so you're constantly looking for a
win. And so, really, what it comes down
to is can I prompt it the right
way? And,
so if I just if I just try
another time, just one more prompt, maybe I'll
maybe I'll be able to say it in
a way that the machine understands.
And,
it it it's really compelling, actually. I
I thought I was quite and and the
the thing that he focused on was it's
the uncertainty of the outcome that drives the
cycle. And so, you know, I I don't
know if this is that they're trying to,
you know, ring a warning bell or anything,
but, absolutely, our our brains are wired in
a way that if we're not careful on
how we use the tools,
I think we could come out with some
outcomes we were definitely not wanting here. There's
a great older book called Addicted by Design.
It's all about gambling, and it talks about
this exact thing that it's not it's not
that the reward cycle is random.
That's what drives you that's what addicts you
to the process. The reward pro the reward
cycle is random.
You would think if you wanted to get
somebody
addicted to something, you would give them positive
resort resort rewards and negative rewards.
Yeah. But you don't give them positive rewards
all the time.
You actually randomize
the rewards,
and that is what makes it addictive.
I I think the reason this belongs in
our current discussion is that the human factor
because we talk about what can LMs do,
what can they do. Well, they can only
do what you prompt them to do. And
it the human running it and and prompting
it, if the human is is a is
a is an AI addict,
like, the human's interaction with the machine is
gonna be highly variable.
And,
I don't know, but I think that plays
into how effective these things are. And it
also goes back to creativity.
Addiction does not breed creativity, man.
If you're addicted to gambling or computer game
or whatever it is,
you're not being creative. And, actually, this author
makes this point. Right? Is that,
LLMs are enormously expensive in terms of the
resources devoted to chips, coolers, and other machinery.
The energy needed to train and run them,
and the human talent diverted from more useful
pursuits.
Sir. Right?
Sure. That's When the early days of of
LLMs,
people were having these gotcha moments where they're
trying to ask the LLM to do math.
Right?
And it's it's just the wrong tool to
do math.
If you wanna do math, you need to
throw the math problem at a CPU
in a very clear and deterministic way and
let it just do the math.
And I think,
like,
what are the right questions and tools or
or,
projects or
experiments
for the LLM
versus other mechanisms. One of the things that
I found
is that people start talking about AI and
they have all these ideas about, well, it
could do this or it could do that.
And half the time, the scenarios that they
envision
are just automation.
Yeah.
Right? Yeah. They're not actually large language models,
or you don't even need one. It's like,
oh, yeah. That's a process that that can
be automated with a set of if then
statements. And so I think we're still trying
to figure out
where they're fit for purpose and where they're
not because as the author points out, it's
very expensive.
And
and having an LLM do math,
that that's a layer of abstraction you don't
need because it's gonna be less reliable, and
it's gonna require way more compute than just
doing the math problem. And so we gotta
figure out where the where the right places
are.
And and we're gonna do a little fumbling
and bumbling in the process too, I think.
Yeah. I think so.
Well, I think that's a good place to
wrap up. I don't have any other articles
to cover, and
I I think we've covered these two pretty
good. Any last comments, Tom, before we
go away and
you can say LinkedIn really fast?
Yeah. But that's for later. That's for later.
Okay. Yeah. Yeah. What what about you, Yvonne?
Did you use an LLM to reposition your
frog lamp?
I did not. I did that with my
own
sense of creativity
and and spatial Serendipity.
Serendipity. Yeah. Yeah. I just was like, man,
nobody I should no longer put the frog
lamp in the corner.
Like, it should be visible.
And I have an outlet right behind the
frog lamp that was visible that I didn't
like the way it looks. I was like,
how can I cover that up? Frog lamp
is the answer.
It was serendipity. That's right. You solved two
problems with one with one frog lamp. With
one frog lamp.
Oh, boy. We're getting deep now. Now I'm
gonna have to go ask Midjourney to create
an image of a frog lamp and see
what it makes.
Yeah. Okay. Good.
Alright. Yvonne, if people
if people wanna follow you or keep up
with what you're doing, I know you have
a different podcast, your own podcast now.
Yeah. Not that the hedge isn't yours too.
I mean, you're a ghost, so whatever.
I mean, to put it that way, but
talk to us. Where where can people find
you?
Yeah. So, you know, I've been a little
quieter on social media lately because the day
job is keeping me busy. But you can
still find me
on Twitter x at Sharp Network, and I'm
on LinkedIn.
The other place you'll find me is with
the Cloud Gambit. And one of the big
things that we have coming up in the
July,
Our podcast will be moving and joining the
Packet Pushers Network. So Oh. You'll be able
to find the Cloud Gambit over at Packet
Pushers,
mid July. That's really cool. I thought about
bringing the hedge over to Packet Pushers, but
every time I think about it, I get
dissuaded by various people.
Because Russ likes to be independent.
Oh, I don't know. I get wrong with
that. I get in I get all nervous
about the whole
advertisers thing
and all that stuff. So, anyway
alright.
Tom, where can people find you?
We can. See.
That's, like, the shortest part of the whole
episode. Alright.
You didn't need an LLM to tell you
he was gonna say that. That's right. No.
I didn't.
Alright. I'm Russ White. You can find me
here at the hedge. You can find me
at rule eleven at tech on I do
log in to x Twitter every now and
again. I'm on LinkedIn a lot more. So
if you wanna DM me or give me
ideas or if you wanna become a guest
on the hedge,
let one of us know. We would be
glad to listen to you as long as
you're not talking about worms eating plastic.
It's a real thing. Don't even start.
Anyway Hey. And maybe plastic eating worms will
save the save the world, but it's just
not the right Yeah. It's not it's not
a networking problem. For us. Yeah. That's right.
It's not a networking podcast thing, so cool.
Alright. Awesome.
We thank you for listening all the way
to the bitter end of this episode of
The Hedge. We know that attention is what
drives our economy right now, and we thank
you for giving us your attention
as we talk about silly LLMs and creativity.
Thanks for listening, and we will catch you
next time.