Build and Learn

In this episode, Colin and CJ discuss using the new GPT tools for work and play.
  • Open AI
  • Large language models
  • Generative AI
  • How do we use it, and is it going to be doom & gloom?
How We Use It
  • Brainstorming & riffing on ideas 
    • I was building something in React and hadn’t touched React in a few years. I had all this iteration going on in my components to generate dynamic content. Still, I wanted the topmost component to be able to have references to all these components to download them all as images in a zip file.
  • Assistance with math-based things like animation or rendering graphics 
    • Example: I had to display a bunch of circles in a 2nd plane in a random pattern
    • Computers are pretty good at that
  • Learning how to do something new faster 
    • Explain this to me like I’m 5
  • Summarizing
  • We use Descript for editing this podcast
  • Shifting the tone of writing, building a writing style
  • Writing video scripts
  • Finding obscure information
  • Story arcs
  • Brushes to write tailwind classes
  • Jokes
  • DnD character sheets
  • Bing to find esoteric star wars character that’s buried on Wookiepedia
Doom and gloom or boon?
  • What does this mean for the future?
  • Privacy & security concerns 
    • Voice clones and scams (create a safe word!)
  • Productivity + time 
    • Are we just going to be expected to be more productive with more output because cApItaLIsM
  • Constitutional AI (Anthropic) 
ChatGPT coauthored a PR to a major open-source project
  • via Ruby Weekly: How long is it until ChatGPT makes a pull request to Rails? Oh, it (sort of) just happened. Ultimately it's still under the name of Rails core team member Guillermo Iguaran, however.
Everything in this pull request, including the code, tests, changelog, commit message, pull request title and description has been created by ChatGPT with some guidance. If you believe ChatGPT is infringing your copyright please let me know.
People to follow
(Written by Notion AI)
Here are 5 social media influencers to follow to stay up to date about AI:
  • Rachel Woods (@the.rachel.woods on TikTok, @rachel_l_woods on Twitter)
  • Logan.GPT (dev advocate for OpenAI)
  • Swyx (shares a lot of thoughts on AI)
  • Lex Fridman (MIT researcher and AI podcaster)
  • Siraj Raval (AI educator and YouTuber)
Github Copilot
  • Copilot X for VS Code & VS Extension
  • Copilot Docs
  • Copilot CLI
  • Copilot Voice
  • Copilot for PRs

Creators & Guests

CJ Avilla
Developer Advocate @StripeDev. Veteran. 📽 Building with Ruby, Rails, JavaScript
Colin Loretz
I like to build software and communities. Building software at @orbitmodel 🪐 Coworking at @renocollective 🎙Sharing software learnings on @buildandlearn_

What is Build and Learn?

A podcast about software development and developing ourselves as software engineers. Hosted by CJ Avilla and Colin Loretz.

Colin: Welcome to Build and Learn.

My name is Colin.

CJ: And m cj, and today we're
talking about large language models

and all this AI stuff that has
happened in the last four months and.

We were just talking about how the
episode we just released, we recorded

four months ago, and so it'll be
like entering a time warp and we're

gonna just fast forward four months
and an incredible amount has changed,

Colin: Yeah, little how
the sausage is made.

I was doing the editing on that last
episode and, I was listening to it.

I'm like, oh my goodness.

I know this was one of our
pre-canned episodes that we

recorded so that we had some buffer.

And I'm just listening
to, this was in November.

Things like Twitter getting bought by
Elon and ChatGPT came out and we're

like, yeah, we use it to summarize stuff.

So what it's, it's cool, but so much
has happened in four months and I think

there's a big conversation going on
about whether or not this replaces jobs

or does it just increase productivity?

And, we've seen these similar cycles,
back when the calculator came out and

the computer and the digital camera.

And so I think having a chat
today about how we use these

tools, will be pretty fun to do.

We can dive in.

CJ: Totally.

GPT-3 was pretty good at generating
text, but it is nowhere near as good as

GPT-4 and I think when we recorded, maybe
chat, GPT hadn't been released or, I

can't remember if it had or hadn't, but

Colin: was like the release

CJ: Okay.

Colin: November.

CJ: Got it.

Got it.

so yeah, I think the leap between
three and four has just been like mind

blowing to everyone who touches it.

and yeah, I think also
like when you asked.

GPT-3 things.

Sometimes the content you got
back was like, okay, whatever.

Maybe that was like a, a response
that you would expect from a

third grader, fourth grader.

And the stuff that you get back
from GPT-4, I feel like is,

senior in high school with a
professor editing the responses.

And it just makes it so much more useful.

Colin: And the difference between
them being that four has been

trained on more data, right?

More language, more data.

And so you can see what happens when you
add more layers and More just data sets to

it, that you get a more complete answer.

we'll leave a link to, the Wolf from Alpha
website about how ChatGPT works under the

hood, and it's pretty amazing that it's
literally like statistically detecting

the next most probabilistic word.

And so it's just guessing word by word.

And when you have more words to pull
from and more examples of those words

in sentences, Papers and things like
that, it gets really interesting.

The thing that I'm most excited to see
is what happens as this gets trained on

proprietary data sets or like specific
verticals like legal docs, constitution,

all these like different things like
court precedents and stuff like that.

CJ: I think companies are starting to
just think about how they can use it to

do weird things that you wouldn't expect.

It's not just generating texts,
but it's, yeah, making informed

decisions about things and.

I don't know.

It's crazy, but I'm, it's, I'm excited
to hear how you're using it cuz every

time I talk to someone who is in
tech, who is using it for their job

or just using it to improve their
life, I pick up something, I'm like,

okay, I'm gonna do that for sure.

So yeah.

what maybe what is like a
recent thing that you're like,

okay, this is useful and.

Colin: Yeah, so I've mostly
been using it for brainstorming

and kind of riffing on ideas.

if I have, I don't know, like I
think when you name something, you

always have to go check a bunch of
domains to see if they're available.

I don't think ChatGPT can
go check like whether or.

something's available for domain
registration yet, but that should

be a thing it should be able to do.

we need a plugin for that.

But if you're coming up with a name for
a project, you're always like looking

for what's a good name, what's available
as it as, dot dev, whatever.

and then you, there just might be
words like, I'll go to the thesaurus

or dictionary, but now I can
just be like, all right, ChatGPT.

Let's have a little back
and forth whiteboard session

on this is the app idea.

These are a bunch of root words.

Give me a bunch of tangential things.

and it comes back with some stuff
that you just wouldn't have found.

In Code I, I use GitHub co-pilot, so I've
that's become a little too normal for me.

And I'm really excited to see
what comes in the new, like

when it's backed by a four.

Cuz I think right now
it's just three, 3.5.

but the most specific thing that
I was blown away by was in React

when I was trying to do something.

It was like a component with
a bunch of child components.

And I write in Ruby most days.

So I'm not just like I don.

Know all the ways that you would
use react off the top of my head.

And so I just explained
what I was trying to do.

I was like, I want that thing to
know about all these other things.

And it gave me such a good example where
I was like, okay, that makes sense.

Why didn't I think of that?

And I was like, you know what, we're just
gonna do something very similar to that.

And it was like one of those Spotify
style, Spotify wrapped, we generate a

bunch of images and we want to be able
to download those images as a zip file.

And so I needed a reference to all of
the images in order to loop over them and

download them and put them into a zip.

And it was able to do
a lot of that for me.

So it was pretty, pretty cool.

CJ: That's super cool.

Brainstorming ideas has definitely
been a huge part for me too.

one, a project I was tasked with recently
was like, help prepare for a presentation

that's gonna happen at a conference
and tell a story that fits in lots of

different Stripe products into the story.

And so I was able to just
give it a bunch of context.

The presentation and say, it should
highlight X, Y, and Z features,

create a company that will illustrate
how you can use all these different

features to do these things.

And it spit out these story arcs with
company names and, character names

of the, the employees and the CEO
and how they came up with the story.

And like all of this stuff that
you're able to really just like

flesh out into this beautiful story.

And you could even tell it, or I was.

I don't know what the
common story arcs are.

So it start out by saying what
are the five common story arcs?

And then say okay, what is a
good common, what is a good

story arc for a presentation?

And then it tells you, one, you're like,
okay, write a story about x where it

includes these different characteristics.

And it just, yeah, it helped so much.

The brainstorming piece is really cool.

When approaching a blog post that I
need to write, I might come up with an

outline that has three or four steps and.

I'll say okay, this is what I
think I'm gonna write for the

outline, and then I'll just go ask
ChatGPT, write the same outline.

And it will sometimes think of other
steps or like it will have different

arguments for or against something or
different pros and cons that I hadn't

thought of that you might come up with
if you're brainstorming with a group

of 5, 6, 7 people with lots of diverse
experience, from different areas and like

really interesting diverse perspectives.

And Yeah, it definitely helps
fill in the gaps as just a single

person who's creating content,

Colin: Yeah, and I think that's the
thing I keep hearing is, Especially

now that companies are trying to do
more with less and things like that.

Is that it?

It's the Ironman suit.

that you're, that I think GitHub
co-pilot was designed and why they

called it GitHub co-pilot was that
they still want the human in the seat.

And that, that you're a director of sorts.

So you're producing what the story
is you're doing the Hero's journey

of the Stripe Conference, right?

what does that look.

And, that you're giving it inputs and
it's able to go look at all those kinds

of things without replacing a person.

And I think there's a lot of people,
I think the media has mostly picked

up on this of I think my mom called
me and was like, oh, aren't you

worried you're gonna get, your
job's gonna get replaced by ChatGPT?

And it's whew, we got a
long way to go, thankfully.

CJ: I think the fact that it can
generate code is pretty, impressive.

And one trend that I've noticed that's
really interesting, if you go in discord,

in the Stripe discord in particular,
people will drop in there and say I don't

understand why my code isn't working.

Can you help me like figure out how
to integrate this API or whatever.

And then we'll say okay,
can you show us your code?

And they'll show it to us.

And we're like, That is super weird.

Like, where did you find this?

That is like not correct.

or it's like odd in a certain weird
way, and they're like, oh yeah.

I was just like using GPT like
chat, GPT to write all of this.

We're like, oh my God.

like a lot of people are already
just using it to write a ton

of code and they don't fully
understand all the details, but.

That's like proof though that I,
that you still need humans involved

to check, check to make sure
that it's behaving as you expect,

Colin: Yeah, I am excited to see what
the new GitHub co-pilot X stuff does,

because it seems to take that, like
the idea that we could point ChatGPT

at the Stripe Docs intentionally.

And say, answer these things for us
means that it's not making things

up from the rest of the internet.

It's making it up based
on what's in the docs.

and it has context like even now,
like when you talk, when you go

to the ChatGPT website, it doesn't
have the context of your code unless

you copy and paste it in there.

And so being able to highlight something
and say, explain this specific thing,

or, Iterate over all of these things
and add them up together, things

like that becomes really powerful.

but you still need to
know what you want to do.

Like that part is still important.

It's not go build 10, dog walking
apps in 10 different languages.

Because there's gonna be a whole lot of
inputs that, I'd be curious to see what

those apps do, generate from scratch.

It's okay, do you assume user login?

Do you know what a dog is?

Do you

CJ: Yeah, there's still like leaps
that it needs to make in understanding

in order to connect dots between
different systems and things like that.

one of the things I was using it for
early on was helping to write video

scripts for videos where we're teaching
people how to use stripe basically.

My normal process was start from
the Stripe documentation and then go

through, build out a demo, and then
walk through and write out by hand what

I, or like roughly what I plan to say.

Now I can just take the whole
thing, give it to ChatGPT and say

write me a video script that helps
teach this in X, y, and Z ways.

But I just saw this really mind
blowing demo by Siraj Raval on YouTube.

So we'll link to it.

But he basically built an entire
YouTube channel with an AI influencer

and so it like will go on Twitter and
find trending AI books, then it will.

write a script that will pitch the book.

Then it will like generate video.

Of a fake person saying the script,
and it will u it'll generate audio.

It like generates their voice,
it generates animations of

the concepts in the video.

And, all of this isn't just like a one.

He couldn't just say ChatGPT make
me a automated YouTube channel.

Instead, he needed to know
okay, here are the like 25 tools

throughout the video, he uses like
tons and tons of different tools.

And Python libraries and, APIs.

So he is using like the shot stack API
to compose all these different pieces

so that they show up the right way.

But at the end of the day,
he was able to create these.

These, videos that are being automatically
published to YouTube and are based on

trending books that are on Twitter.

And the idea was like, okay,
now I want to take my affiliate

link for those books on Amazon.

Put them in the description
so that you can like maybe

build up this passive thing.

So from my little idea of help
me write a script for a video.

It's like people are able to use these
tools in different ways to combine

them and build like these really wild
automations and systems and, yeah, I

don't know, like basically like the,
I feel like the way that I'm using it

is still very much just scratching the
surface of what you can do with even

just the stuff that's available today.

Colin: I'll say, I haven't seen the
one that you just talked about, but

I will say that the difference it
sounds is that you use the tool like

a research assistant, and then you're
still the person who's doing the video.

And I have to wonder, we talk about
this a lot with our show, like people

wanna follow people and I feel like
computers and AI are not gonna make this.

It's actually gonna make it more valuable.

Real conversations by real people.

Do I.

And trust a book review from
an AI robot that's just meant

to be an affiliate link farm.

Or do I want to hear what c?

Thanks about this book.

And you can still use this to do summaries
and to do research and, come up with a

bunch of other books that we should read.

But, it's similar to when, wizards
of the Coast and Dungeons and Dragons

was talking about, they're already
seeing people using their IP and NFTs.

And AI dungeon mastering and things.

And a lot of people were worried that
they're gonna focus on creating some

sort of AI DM and replace dms when
I think people forget why we played

Dungeons and Dragons in the first place.

I think there's been a meme going
around, we're doing all this work and

we're giving AI the ability to do art
and music and things like that, right?

And it's we should be doing the art
and music and let the AI do the work.

we should flip that around.

I think there's really cool things
that, there was a really good blog

post about someone who had chat, GPT
Run a Dungeons and Dragons game for

them, and it was cool, but I still
don't think I would necessarily be

threatened by that or feel like I'm
replacing my hobby with an AI for that.

CJ: I guess we're like slipping into
doom and gloom territory a little bit,

but like I, yeah, I'm so conflicted.

Okay, so this is coming from,
my perspective as a parent.

my kids, they're super into D&D
and I am really conflicted about

letting them use ChatGPT, because
I'm worried it will take away their.

And they'll start to lean on it as like
entertainment and they'll lose their

creativity like, before seeing ChatGPT,
they would sit down and have piles of

books across the dining room table that
they borrowed from the library and were

building their own character sheets.

And they have like dozens of these.

They're floating around the house.

Many of them make it to the recycle bin.

But, we sat down and then, I was
like, okay, let's, let me just show

you some of this ChatGPT stuff.

We did jokes, we did poems, lots of fart
stuff, lots of Mario Brothers stuff.

But then we like, we generated a D&D
character sheet and we like gave it

some really specific things and my
son was like, oh yeah, I wanted to

have these cool weapons and I want
to have this and that, whatever.

And it generated this really impressive
character sheet with like custom.

Weapons that have, like cus I dunno,
it's like damage or something.

I don't remember like all the details
of what they do, but it was like super,

super customized and really comprehensive
and complete and so I think that's cool.

But I also am like, okay, I want them
to be able to use it and get ahead

by using it, but I also don't want
it to steal their creativity and I

don't know what the answer is to that.

I also like when it comes to art and
all like the creative pieces of my work.

at work, right?

Like when I'm doing my day
job, I actually don't mind if

it's doing the creative work.

Like I don't wanna have to think
up images and I don't wanna have

to think up like these story arcs
and have to have all the details.

Like I'd rather the, AI do that for me.

And then on the weekends
or like when I'm not.

Working for money, then I can spend
my time thinking about my creative

stuff or drawing, or how do I want
to have a different creative outlet.

And so just the tension between those
two things right now is really, yeah.

It's something that I know like I,
I'm having an internal battle about.

Colin: Yeah, I think this is the, old man
yells at cloud segment of the podcast,


Where, we both are like, we can
see how it can be used for good

and see how it can be used for bad.

I think that the other side of
that would be, it could make you

more creative because you get.

To iterate through lots of
different versions really quickly.

I do feel for artists who have
their work has been trained.

Into these systems where it's if
someone was gonna hire someone for

a portrait before, the odds that is
gonna happen in the future, go down.

and I, there was an article going
around from someone who couldn't,

the time to create a character in
a game went from weeks to days and

they don't feel as creative anymore.

But also now that means that you can.

A whole bunch, like AI can create a whole
bunch of crappy characters, but it still

needs some input like we're talking about.

And so you might not get, have
enough time or skill to pull off

something 10 different ways, but with
this you might be able to, and then

pick the best one, improve on it.

So you get a lot of that iteration on it.

I think the thing that I've been
using it for the most, other than the

brainstorming has been like having
it teach me And the challenge there

is the hallucinations are real in ai.

So you can't take it at its word
all the time, but if you keep your

curiosity and you keep asking questions,

it's very interesting how it behaves.

So like I gave it, a prompt in 3.5 and
in four just to see the difference.

And I highly recommend sometimes
running the exact same prompt through

to see what the difference is.

But I had it create an API in Ruby
in 3.5 and in four, and with 3.5 you

could see how it got to where it got,
you could fill in the missing gaps.

But there were definitely gaps.

if you ran this, it would not work
by, just by the steps it gave me.

but there were a few things missing.



Like first try, it had a bunch of gems
that it didn't need and wasn't using.

So like I would ask it like, oh,
what is, why'd you include this?

And they're like, oh good.

my bad.

Obviously that's not being used.

And it was like, okay.

So you're clearly looking
at, most apps that include.

this gem also included this
gem, so that was the next

probabilistic word, and it was fine.

It was able to figure out, and that,
I think that's the thing that still

messes with my brain is that even
though I know it's probabilistically

detecting words, how does it unders
it doesn't understand, is the point.

But like, when you ask it about a thing,
it knows what the thing is because

I'll tell it to remove that gym and
it will rewrite the code without that.

CJ: Yeah.


can I take a swag at trying my
best to explain stuff that I

don't actually understand yet?

Colin: do it.

CJ: I th yeah, I th when we
were in college, one of the

projects that we did was building
markoff chains, which was cool.

You take all this Shakespearean text
and you dump it in, and then you

just break up the text into lots of,
Element, tuples, basically thrus, I

dunno if it's actually a throuple.

and then you like, look at, you look at
a throuple and you say okay, the last

two words in this three word array.

Now randomly grab one of the next arrays
that also start with those two words.

And that gives you your third word.

So then you build this chain.

So that's like whatever,
a dumb markoff chain.

But you could have it spit out like
what looked like Shakespeare and this

was like 2006 or something, 2008.

If you imagine every
single word or concept.

Being mapped onto like a 2D grid, right?

So we have the X coordinate and the
Y coordinate, and we're just gonna

throw every word and they're gonna
splatter somewhere on that 2D grid.

Now we can figure out exactly where
each word lands on that 2D grid, and

each word will have a vector that
has like the, the length and the x

y coordinates, or like the x and y
that will get us to that point in 2d.

each of these models has a certain size,
so there's like a certain number of

dimensions on the model, and I think GPT-3
was like 2048 or something like that.

And if you look at these new, the
newer models, they're much bigger.

I think they go up to 30,000 or something.

So if you take that concept of
having every word mapped out in

this space and you exploded out to.

2000 D basically, or a thousand D, then
you have a lot more different dimensions.

So it's not just X and
Y, it's not X, y, Z.

It's like X, Y, Z, a, B, abc,
all the way up to 2000 of those.

And in that space, words that
are semantically similar.

Will be close together.

So like hotdog and burger are gonna be
like, if you look at this like giant

space, those are gonna be close together
in terms of like where their vector lands.

And so like you can use that piece of
information to get a better idea of what

like the next word in the sequence is.

And so that's I don't know
if that makes any sense.

And explaining over audio is tough, but
my understanding is that's like you can

use this semantic similarity between these
concepts and even between like sentences

or paragraphs or like entire blobs of text
to figure out what is this thing like?

And then use that as part of your chain.

Colin: Yeah, and then we can ask it.

The question of is a
hot dog a sandwich with

CJ: Yeah, exactly.

So one of the things that I think is
in interesting is using these, part

of these tools called embeddings,
where you can say here is a concept

or a sentence or a paragraph, and you
can send that to open AI in their api

and it will give you back a vector.

That is just literally like a giant
array of numbers that represents

that concept for that given model.

So say you're using Da Vinci,
whatever the Da Vinci model for

GPT-3 and you give it hotdog, it'll
give you back something that's like

an array that's 2048 elements long.

That's a bunch of numbers.

And those numbers, if you like,
map them out through 2048 space.

That's like where the hotdog lands.

And there are, yeah.

one of the things I was trying to do
was take all my blog posts on my site

and then run them through and build
up these vectors and stick them into

Pine Cone, which is like a vector
database, and then use pine cones like

querying language to have it give me
back like search results for my website.

I think this is like the bits and
pieces that were, that I know, like I'm

trying to like, learn about for sure.

And build, I don't know.

Th there's so much that I think
will be built on top of this that,

it definitely feels like, early,
early days of the internet in

terms of accessibility, tooling.

like all these crazy new
concepts they have to pick up.

Colin: Yeah.

And you have to eventually learn
how, like I, I've been learning

how to give it prompts and
that's like a skill in of itself.

And some of them, especially these
image ones, you can, the prompts

become these paragraphs of describing
the mood, the scene, the reflections.

I'll put it in the show notes
too, but a friend of mine is a

photographer and he is using these.

As well, but he generated something
that I would not have guessed was not

shot on his camera, up in Tahoe Fair.

I'll have to find out what his prompt
was to see what it was, but it was like

he's a nature photographer and he's
over here generating from scratch the

same thing and isn't really threatened
by it as much as trying to explore

and push and see what he can do there.

I think the challenge is
going to be whether or not.

for some of the applications that
I've seen, they're not things

that I was gonna do today anyway.

it's cool, but like we went through
a bot, a hype cycle a few years ago.

then we went through
the crypto hype cycle.

And I have to wonder, I think that
this feels like magic because it

understands what, it remembers.

if you're in a ChatGPT session, you
can say oh, what does that mean?

And it knows what the thing above
was that you were talking about.

So that shared memory
is super interesting.

But I think people have to
remember that these are still.

Models like you just gave us the
whole pitch on how they work and

not another person on the other end.

Cuz there's starting to be some very
scary stories about people falling for

these, chat sessions as real people.

you have some notes here about some
of the doom and gloom, or Boone

type of things where, there have
been, scams where someone will call.

Record your voice and then use that
voice to do a voice clone, and use

these models to be able to then call.

A significant other or a child, and
pose as you, and, they won't, especially

on a phone where it doesn't have high
fidelity, it can sound a little goofy or

distressed or, say I've been, kidnapped
as was the example that I hurt and please

send money to this location type of thing.

And it's whew.

Like how do we get to a world where
that exists and how do we protect our.

CJ: And the same tools that these
scammers are using, we can, we're

also already using that for this
podcast, so one of the tools we use

is called Descript, and like right
after the podcast, we drop it in there.

It transcribes everything and has this
feature called Overdub where we can train.

Descript and say this is, I think it's
four, it needs 45 minutes or something.

So I can say here's 45 minutes of Colin
talking and here's 45 minutes of me

talking and okay, so this was a word
that we, one of us, stumbled over,

or we said the incorrect word here.

Then you can just edit the word as text
as if you're just editing a Google doc

and say, overdub, and it will replace
that word with that person's actual voice.

And that becomes really useful
in scenarios where you're doing

something like podcasting.

You don't need to like, jump back
on the mic and tr do a bunch of

different takes to say the word again
and then splice it in or whatever.

but yeah, it's definitely a huge risk.

And one of the recommendations I heard
from, Rachel Woods on TikTok was that

to avoid these voice scams is to create
some sort of safe word with your family

so that if anyone calls and they're like
pretending to be someone, you, yeah.

You have a way to, I don't know,
crack their scam or whatever.

Colin: that's your, safety
tip from Feld and learn this

CJ: Yeah,

Colin: go create a safe word for sure.

and I would say definitely don't
be feeding these things, any of

your private or secured data.

anything that you like.

I think I've been hearing people
putting in there like medical

records and tests and stuff into this
thing, and it is an impressively.

Able to understand
protein chains and things.

And but that's probably because there's
a bunch of research out there on protein

chains and they show up in a certain way
and they show up on the speced, map the

same way that you were describing earlier.

And so I am hopeful for the things
that it's going to be able to teach us.

Like it's better at spotting things
on in radiology than a human.


That doesn't mean it replaces the
radiologist, it just means that now they

can detect and look at things faster.

but I have to wonder, and this happens,
happened ever since the industrial

revolution, but as we get more productive
with these tools, does that mean that

now we're just trying to churn out
more stuff and in 40 hours a week, or

do we start to head towards that four
day work week, three day work week?

CJ: Oh, I hope that three day
work week comes, I think it's

right around the corner . So going
back to could it be a hype cycle?

Is it a fad?

I think when you, reflect about
search and Google in general and

the job to be done there is I want
to find an answer to a question,

The way that I search is I'll like
type in my search query and then I open

like the first nine links , just like
command, click all of them and then

dig through the tabs as fast as I can.

Scanning and my, my brain has been
trained over the last 15 years,

how to just try to extract maybe a
right answer out of these things.

And now you can just get the
right answer, almost immediately.


Have you had a chance to try out
the Bing, like the Bing client?

Colin: I haven't.

CJ: So what were we doing?

We were looking for a type
of Star Wars character.

It was like a class of Star Wars
character that's like this super

esoteric character type that is only
documented like on one page on Wikipedia,

buried somewhere in the archives.

And so we went on Bing
and used like the Bing.

GPT search or whatever, and
it spit out the right answer.

It was like, I can't even remember
what it was, but it was like some type

of, dark sister or something that,
that had, yeah, it was like a force,

I don't know, force sensitive thing.

but yeah, anyways, like there's lots
of different interfaces to this and

I think the jobs to be done about
just getting an answer quickly.

If we can start to trust and rely
on the models, which, I think one

of the co-founders, Greg, one of the
co-founders of OpenAI, said that coming

soon, the answers that you get back
from the, these models will be accurate

and not be hallucinating, which, when
that starts to happen, I think that's,

that becomes a huge game changer.

But yeah, until then, we all
have to be skeptical and take

every answer with a grain.

Colin: Yeah, there's actually a good
book on this that's called Invisible

Women, data Bias in a World Designed
For Men, which I think is also

something we have to keep in mind is
there's been a bunch of conversation

about ethics around all of this.

And to be honest, most of the
worlD&Data sets are very much

skewed for as perspective.

It tends to be, people who are
building these things tend to be.

white men and or, a few
different, types of backgrounds.

And so that we need to have
representation in both the data

sets and the people working on this.

because otherwise, I even think
about simple run of the mill

things like recommendations.


Like it's harmless to ask for a pizza
recommendation in a certain town.

Now, if it's generative AI though,
is it based on Yelp reviews?

Is it based on just like it
knows what a pizza shop is and

it gives you the first one?

is it the closest one?

there's a whole bunch of stuff
there that we don't yet know.

And so if you're asking for actual
answers to things, it does seem to

also have this sense of the way that
it writes in chat, GPT specifically,

it tries to agree with you.

like the way that it presents things
is very pleasing and agreeable.

And what I'd be curious to see, and
I've tried a little bit with poking some

conspiracy theories and things at it, is
wh how it replies because it can also.

Reinforce things that you tell it.

And so if you have a belief whether
or not that belief is true or not,

and you give it to it, and you have,
you create this little, reality

distortion field for yourself.

And there's a whole bunch of books
in sci-fi that would love to, to

take us all for a ride on that.

so we only have to look at our
whole body of science fiction to,

to see like where this could go.

But, I'm excited to see where it goes.

It is impressive and every time I
use it, I'm like amazed and, not

concerned for the future as much as,
we gotta take a measured approach

to safety and ethics here and figure
out how we can use it for the better.

CJ: Anthropic is one of the
main competitors to open AI and.

this is again, gonna be stuff that I
learned from Rachel Woods, but the,

the team at Anthropic was a bunch of
former open AI researchers who wanted

to break away and make something
less corporate and, they have this

blog post that we can link to in the
show notes about their views on AI

safety, and it's my understanding
that open AI is using humans to create

the guardrails around the models.

And Anthropic is taking a different
approach called constitutional ai, where

it's like giving it these principles.

That are, these like things that
basically say, behave or please behave

nicely based on these set of values.

And then it goes and like trains
itself and then tries to make

sure that its decisions are
consistent with those values.

And, I, yeah, it's it's a really
interesting approach and it also

made me think about, this is gonna
take a little bit of a turn, but the

way that our current constitution
is, resulting in less than ideal.

Country like formation, right?

Like here we are realizing that
the Second Amendment is actually

not the best formation of, what
the founding fathers expected.

And we have all of this gun
violence all over the United States.

And so there is a chance that you
write a Constitution with values that

are consistent with what you believe.

Correct and good today, but that
doesn't stand up to the test of time.

So figuring out how to evolve those
values and principles in a way

that's measured and safe and ah,
it's gonna be really interesting.


Colin: it's that interpretation
and context that's key.

And even some of this reminds
me of the three laws of.

Robotics too, which are very
dated and from sci-fi, but they're

something we should look at.

CJ: Yeah.

it is, sci-fi is like
basically here today.

It's wild.

Colin: It's very similar to some
people have said, with all of this,

the last 5%, the last 1% is so hard and
hardware is gonna be a limiting factor.

But also like when we think about full
self-driving, if say we're at 90% of

the way there today, like there's still
a lot that's gotta happen before you'll

let ChatGPT take the wheel literally.

It's like obviously large language models
are not what's powering full self-driving,

but if we look at it similarly to the
vectors of what's a person in a crosswalk

and what's someone backing up when they're
supposed to be going forward, right?

Things like that, that a human can make
those decisions, but if it doesn't have

enough examples and it doesn't have.

To reason around it's
gonna be hard to get there.

And then can you have enough
of that on hardware that's in

your pocket or in your car?

we've got a long ways to go and
it might be that hardware is a

limiting factor for a little while.

I think to take it back to the build
and learn side of things, we've got

two things that we can leave you with.

I think one is a funny, thing I saw
come across Ruby Weekly, which was that

chat, GPT co-authored at PR to Rails.

and this was a Link baity title, but
one of the rails core team members,

went ahead and gave it a prompt,
created a poll, request, code, test,

change, log everything, and then
submitted it with, Disclaimer that it

was created with guidance by ChatGPT.

and so we'll leave a link
to that in the show notes.

You can read the pr, you can
read the whole conversation.

And, it was interesting to see
like if you believe ChatGPT is

infringing on your copyright,
please let me know type of thing.

and then you have a list here.

What do we have down below?

CJ: Oh yeah, so this
was, I used Notion ai.

So we plan all of our shows
inside of Notion and I just, you

just do slash AI or something.

And then I said, tell me five social
media influencers that people should

follow to stay up to date about ai.

And it's spit out these five Rachel Woods.

Logan, GPT, swyx, Lex
Friedman, and Siraj Raval.

So we'll have to do a little bit
of, vetting and research about who

we're gonna put in the show notes,
but it's just I don't know, it's

wild that, that it can do this.

Colin: Absolutely, and I think
that is where we'll leave it.

We've got a whole bunch of links
that we'll put in the show notes to

a lot of tools, a lot of different,
models that we talked about today.

And if you want to hear more
about this, please tweet at us.

@buildandlearn_ on Twitter.

you can find all that in
the show notes as well.

Find us, and we'll see you next week.

CJ: All right.

Bye friends.