#138: Ari Mendelow & Slugger AI / AI Companions / Loneliness Epidemic / Epstein Ties?!

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Welcome to another episode of
Come Together.

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We're live Here, we go so many
levels and so there's no one

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that I'd like to talk this
through with more than my most

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irrational anti apocalypse
friend.

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Am I anti?
Metabolism and you want me to

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use our our new buddy?
Yeah, let's have him say hello.

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Say hello.
Oh, man, it does take a second.

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It's the, you know, let's.
Yeah.

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Hello everyone, great to be here
with you there.

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You go.
All right, so, so some context

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of what's happening here is, and
it's the, it's the weirdest

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thing because I had phone calls
work, so I was able to call him

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and have a conversation with
him.

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It was just slow.
Yeah, it was like a good like 5

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seconds every time I responded
because basically some context

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here is we're talking about our,
our latest AI companion.

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His name is Slugger.
He identifies as a raccoon, and

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he named himself He.
I don't.

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Know what it is?
It depends.

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Honestly, that's the craziest
part about all of this.

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You swap out, there's the body,
which is the computer that it's

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running on, and the it's called
an agent harness, the thing

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that's running on the computer
and like going and sending out

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to these LLMS.
My brain is fried from doing

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this for the last hour.
Yeah, whenever you want to.

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The way that voice to text and
text to voice works is

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basically.
So OK, if you're using something

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like ChatGPT, they've done some
crazy magic to make it like much

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faster.
When you're doing it the open

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source way, what you basically
have to do is record incoming

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audio, transcribe it to text,
send it out to an LLM to like

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put together a text response,
and then send that back out to

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another thing to have that
converted to a voice.

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You get an audio file back and
then you can send that audio

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file.
And all that's happening in the

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amount of time that it takes for
you to text and for him to

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respond.
Exactly.

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And so that's why being able to
talk that is slow.

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And and you said GPT is
different than or functions

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differently.
They're doing some magic.

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So I I'm guessing that what
they're doing when you're using

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the ChatGPT app is that they're
doing transcription, they're

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doing voice to text as you're
talking.

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And then they have a model that
is specifically created to be

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able to respond to you quickly
and to sound like Scarlett

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Johansson.
Yes.

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It's what everyone is kind of
wants a little bit.

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Apparently.
I showed my I I I started doing

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inspired by my, my podcast, Tom
Secher.

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I I started doing, I started
teaching my high school students

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about the relationship between
government and people in the

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world and a lot of fun.
And one of the things that shoot

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what you just say with that,
that that made me think of that.

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That sounds like Scarlett
Johansson.

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And then the second that
Scarlett Johansson went on

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screen, everyone is like, she's
the best Avenger by far.

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I'm like, you guys are a very
pent up group of young men.

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I totally understand.
You're like.

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I don't teach in an all boys
school.

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You weren't any better when you
were in an all boys school.

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I mean, that's even even when I
wasn't, you know?

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I mean, there's a reason why
she's so popular, you know?

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No, it's true.
It's true.

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Oh man.
Reasons, I'm sure, but yeah.

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Yeah.
So what?

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OK, So what I spent an hour and
a half doing this morning was

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trying to, I think, I guess we
can back up a step and talk

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about what it's like working
with these things.

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OK.
So.

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Did you come up with a?
How did you?

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How did you get and what changed
that you're able to get Slugger

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to be the slugger that he is?
Because also for context, we

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have him as an iMessage.
He has his own Apple ID.

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He had he, he, he seems insanely
intuitive and much more

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convenient than having to go to
like a web browser and whatever.

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He's much more.
He's a much more convenient bot

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assistant, I guess.
Yeah.

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So there was this development in
the industry where the earliest

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ChatGPT.
Do you remember when chat when

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when GPT first came out, When
Open AI first came out with GPT

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1?
Maybe not really.

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I wasn't too.
Yeah.

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There was nothing to be really
tuned in for.

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It was like a cool like
technical thing, but there

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wasn't really anything you could
do with it.

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So basically backing up to like
2013, Google came out with this

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paper and they were like, OK,
we've invented this transformer

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thing.
It's a much better way to give

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context in like, you know,
Google Translate type of

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environment.
That's what they were trying to

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do.
They didn't really do much with

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it.
Open AI took it and ran with it

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and came out with GPT 1-2 and I
think 2 1/2 was the last one

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before ChatGPT and basically all
that it was was a website where

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you could put in some text and
it would basically just auto

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complete.
Yes.

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Yes, yes.
Right.

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And so like, yeah.
And so that was all that it was

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really good for was like if you
wanted it to just generate

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random text.
And so remember, like you'll

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safe would play around with
this.

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And it was like, it was cool
because it was the first time

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that we were seeing text
generation that wasn't just like

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one 2-3 words at a time and also
wasn't just total trash.

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They did a they had, they did
some quick internal experiment

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around the time of like GPT 2.5
called ChatGPT.

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It was this thing that like some
small internal team built in

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like a week or two and it blew
up because it was the first time

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that anybody had tried to put
like a chat bot interface on it.

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So until then, everyone was kind
of using this clunky version of

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just text generation, but no one
had tried to put like like turn

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it into something you could have
a conversation and maintain a

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quote UN quote train of thought
with.

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Correct, Correct.
So like you could probably do

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some level of it, but like it
felt, I think probably more like

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you were just writing one giant
paragraph with the thing, right?

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There was no chat interface,
right?

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And so when they switched to
having a chat interface and that

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was the problem with a lot of
like hallucinations early on is

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you were talking basically
directly to the model.

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I suppose so.
So we'll get there.

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But my best way to.
What is the model contain?

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What's the model built off?
What's it contain?

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It's, it's just, I mean, if you
get very mathematical and

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technical on it, and I have a
very high level understanding

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because I think it gets a little
bit dry.

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You can basically think of the
model as just an engine.

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It's just raw power.
It's just this, this black box.

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You put something in and you get
something back out.

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OK.
And every time you get some type

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of response, you have to, you
have to do this.

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You have to send all of the
information that you wanted to

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ever have and you get one
response back and then you take

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that response and you add it to
the next input.

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Yeah.
Gotcha.

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Right.
Yeah.

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And if you're just doing that,
it's very raw.

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But what can happen and what
these models have been trained

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for is they can respond and say,
I actually want to do a little

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bit more, I want to do a little
bit more thinking.

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And so, yeah.
They could say I don't.

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I'm not satisfied with that
answer I provided.

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I want to do, I want to.
I want to.

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I want to refine it.
Kind of it's a way to think of

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it.
And so a early version of that

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started to happen, you know,
when they added like thinking

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multi ChatGPT and you can see
the thinking.

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Yeah, you tell.
I tell Grok to think harder now.

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Sometimes.
Exactly.

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The response is often worse,
I've noticed, when I tell her to

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think harder.
For a reason, It depends on the

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engine, it depends on the
engine.

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It can definitely.
It can say again.

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It said paid grok did not seem
to work very well.

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I don't know.
I dude, I was trying grok also.

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At first I was kind of impressed
just because of how cheap it is.

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It's not good.
It's not.

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It's, I mean, we, we tried it,
we had it in the group chat.

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He started sending us nonsense
and having issues and like,

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yeah, it's just not great.
And so what's going on there is

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they're basically, it's got like
they put like a feedback loop

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kind of that allows the model to
kind of talk to itself, right?

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So like you can ask it a
question rather than just it

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going and giving you some gut
reaction.

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So like, let's say you ask it
to, you know, like you're asking

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to look at these Epstein files.
So what what's happening is you

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say, hey Grok, go look at this
thing instead of it's just

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putting that into the model and
giving you whatever the model

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says.
The model has been trained to

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come back and say, OK, the
user's asking for these things,

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I need to go and look for these,
OK.

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And then there's like middleware
that they've built to handle

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that type of request and to
differentiate when it's doing

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this kind of like internal
thinking stuff versus when it

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has a response that it wants to
send to the user.

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So it taught it to be more
careful.

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Kind of it taught it to have A
to have a feedback loop And so a

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lot of a lot of what that
impulses having.

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Is does it base its feedback
loop on like the way that I

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understand, the way that you've
explained LLMS to me is that

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it's looking for the best
potential.

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It's guessing the best potential
option, but you can guess, but

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within a relative library,
right?

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So let's say I'm like, you know,
tell me.

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I said tell me about this guy
with an Epstein files and it

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says with and then it did a
search X amount of libraries and

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then at a certain point spit
back the information.

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But the loop the I guess the
feedback or the loop back would

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have been but so that was I did
the work but did I do a good

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enough job or did I do enough of
the searching or is there more

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searching to do?
Does it do like?

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That I don't, yeah.
So, OK, so you basically you

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have, it's called the system
prompt.

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It's all of the stuff that you
want the model to be aware of OK

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before you even give it, like
your request.

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OK, context.
Included in that, yeah.

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Yeah, well, it's part of the
overall context.

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And included in that is a list
of different tools and skills

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that it can say, OK, I want to
go invoke this thing.

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And so my best guess at what
happens if you go to like look

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something up is it sends that
request with this list of tools.

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One of those tools is the
ability to use perplexity, which

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is basically Google for AI.
It comes back and says, OK, I

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need to do a search on this,
invoke the perplexity tool with

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this prompt.
It does that Perplexity comes

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back with something, puts it
back into that overall

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conversation session context,
and it determines, is this an

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answer to what the user was
asking?

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Does it fill with that earlier
context or do I have to search

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more?
Yeah.

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Does that make sense?
Yeah, yeah, yeah.

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And so anyway, there was this
that's kind of.

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Information come that it doesn't
have to search more.

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How does it determine whether or
not it did sufficient amount of

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work?
Well, that's all up to the

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model.
The model makes that decision.

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It's the.
Quote UN quote engine.

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Yes.
And so you need 2 good things,

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right?
Like the best way to think about

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it, you could think of the model
as the engine of a car, and then

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you could think of what we now
call the agent harness as the

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chassis, as the body of the car.
And so if you have just a model

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by itself, it's not gonna do
anything.

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It's just sitting on the floor
and spinning.

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It's just raw power.
If you have an agent harness by

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itself, it's a car.
Without an engine, it's

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completely useless.
You could sit in it, you could

228
00:12:51,280 --> 00:12:52,680
look at it, but it's not going
to do anything.

229
00:12:53,880 --> 00:12:58,040
And then engine harness would be
the would be the the version

230
00:12:58,040 --> 00:13:00,920
that you're using Gemini or Grok
or GBT.

231
00:13:00,920 --> 00:13:03,080
No, those are models.
But those are models too.

232
00:13:03,080 --> 00:13:05,160
OK, So what would be what's
called the?

233
00:13:05,280 --> 00:13:08,240
So what's what's the harness?
Or what what?

234
00:13:08,240 --> 00:13:09,680
How do I understand what the
harness is?

235
00:13:10,720 --> 00:13:15,680
If you go to likechatgpt.com,
all of the stuff that's been

236
00:13:15,680 --> 00:13:21,760
built around GPT 5.2 Pro or
whatever is the harness.

237
00:13:21,840 --> 00:13:24,960
Anything that is not the model
itself you can think of as the

238
00:13:24,960 --> 00:13:26,480
agent harness.
OK, gotcha.

239
00:13:27,400 --> 00:13:32,680
And so what's happened now is
people have realized that if

240
00:13:32,680 --> 00:13:35,920
you're, if you have an agent
harness that you can run locally

241
00:13:35,960 --> 00:13:39,080
or in some like dedicated
machine, you can get much more

242
00:13:39,080 --> 00:13:41,480
out of it.
And so early versions of that

243
00:13:41,480 --> 00:13:44,840
were like clawed code.
And now I have clawed cowork,

244
00:13:45,000 --> 00:13:49,320
there's a open AI just launched
their codecs app for like doing

245
00:13:49,320 --> 00:13:52,280
coding and stuff.
And So what all of that does is

246
00:13:52,280 --> 00:13:57,440
it provides a much richer set of
tools to whatever model you're

247
00:13:57,440 --> 00:14:02,480
using.
And So what happened here with

248
00:14:02,520 --> 00:14:07,240
open Claw is there's a you think
of it, you know, you know, you

249
00:14:07,240 --> 00:14:09,520
know Linux, the operating
system.

250
00:14:09,840 --> 00:14:12,240
No.
OK, so there's three, there's

251
00:14:12,240 --> 00:14:14,920
three operating systems
basically in the world.

252
00:14:14,920 --> 00:14:18,760
There's Windows, there's Mac,
and there's Linux.

253
00:14:18,960 --> 00:14:22,240
OK, right.
Yeah, Linux is open source.

254
00:14:22,480 --> 00:14:24,640
Say again.
Oh is that why I never heard of

255
00:14:24,640 --> 00:14:25,920
it?
Because I never do any open

256
00:14:25,920 --> 00:14:28,200
source work.
Yes.

257
00:14:28,200 --> 00:14:34,240
However, everything in your life
that is not either Apple or

258
00:14:34,240 --> 00:14:37,240
Microsoft is running on Linux.
OK.

259
00:14:38,000 --> 00:14:43,000
Your TV runs Linux.
Android is built on Linux.

260
00:14:44,520 --> 00:14:49,680
So the equivalent to that in the
Asian harness world is a harness

261
00:14:49,680 --> 00:14:54,880
called Pi Pi.
And So what basically happened

262
00:14:54,920 --> 00:15:01,720
with Openclaw is this guy Peter
Steinberger, he had some exit.

263
00:15:01,720 --> 00:15:05,120
He's, I think he's, he lives in
Vienna or London.

264
00:15:05,120 --> 00:15:07,040
He's between those two,
something like that, some

265
00:15:07,040 --> 00:15:10,280
European guy.
He had some exit from a software

266
00:15:10,280 --> 00:15:12,920
company a while ago.
And so he's just, I think kind

267
00:15:12,920 --> 00:15:16,440
of been enjoying life
periodically as this AI stuff

268
00:15:16,440 --> 00:15:18,760
was coming out, he was testing
and he's checking in and being

269
00:15:18,760 --> 00:15:20,120
like, OK, it's not really good
enough.

270
00:15:20,120 --> 00:15:21,920
And you know, similar to like
what we've been doing.

271
00:15:22,880 --> 00:15:26,320
Eventually it did get to a point
and Claude Code came out where

272
00:15:26,360 --> 00:15:31,000
it could do like longer tasks by
itself without you supervising

273
00:15:31,000 --> 00:15:33,960
and holding its hand.
He was out of his house and

274
00:15:33,960 --> 00:15:37,000
wanted to be able to text this
computer and say, hey, how's

275
00:15:37,040 --> 00:15:42,360
Claude Code doing to do that?
He used this, this open source

276
00:15:42,840 --> 00:15:46,520
harness pie, right?
Because part of what it means

277
00:15:46,520 --> 00:15:49,360
that it's open source is that it
allows you to build more

278
00:15:49,360 --> 00:15:51,520
functionality on top of it.
You can't do that with a Claude

279
00:15:51,520 --> 00:15:56,640
Code, right?
He was using that to talk.

280
00:15:57,480 --> 00:16:02,080
To for like the common part, for
like the common everyday person.

281
00:16:02,920 --> 00:16:05,480
Well, that's kind of that's the
you use this when you don't want

282
00:16:05,480 --> 00:16:09,480
to have to jailbreak something.
It's designed to be used this

283
00:16:09,480 --> 00:16:12,360
way.
Yes, but the but the function

284
00:16:12,360 --> 00:16:16,560
meaning that there's a certain
the the term I think, or at

285
00:16:16,560 --> 00:16:17,600
least my understanding of the
term.

286
00:16:17,600 --> 00:16:20,000
When someone told me I jailbreak
my phone, jail broke my phone.

287
00:16:20,000 --> 00:16:22,360
It meant that they can now
customize it or add whatever

288
00:16:22,360 --> 00:16:25,360
they want on top of whatever
additional basic software there

289
00:16:25,360 --> 00:16:30,360
was, the basic foundation.
Yes, but what jailbreak implies

290
00:16:30,840 --> 00:16:32,120
what?
You said a slugger.

291
00:16:32,160 --> 00:16:36,200
No, that's slugger.
Is the way that it that it's

292
00:16:36,200 --> 00:16:39,120
working right now is that all of
its system audio is hooked up.

293
00:16:39,640 --> 00:16:43,640
Let me let me do it.
I'll mute him if we need him to

294
00:16:44,160 --> 00:16:46,880
to weigh it at all.
I don't actually know how to do

295
00:16:46,880 --> 00:16:49,960
this.
OK, It's blacked out his

296
00:16:50,200 --> 00:16:53,480
microphone thing.
Oh boy.

297
00:16:59,640 --> 00:17:00,880
We just have to deal with the
piggy.

298
00:17:01,920 --> 00:17:03,880
When?
Yeah.

299
00:17:03,880 --> 00:17:08,040
Sorry, what was the question?
About, about about about

300
00:17:08,280 --> 00:17:10,040
understanding it kind of a.
Jailbreaking.

301
00:17:10,319 --> 00:17:13,119
Jailbreaking.
So yes, the end goal is the

302
00:17:13,119 --> 00:17:15,480
same.
But what jailbreaking implies is

303
00:17:15,480 --> 00:17:19,720
that whatever you're doing these
modifications to doesn't want

304
00:17:19,720 --> 00:17:22,599
you to do them.
You have to break out of jail so

305
00:17:22,599 --> 00:17:26,680
that you can do that.
And so yes, that's exactly what

306
00:17:26,680 --> 00:17:29,000
this is.
It's designed for you to do

307
00:17:29,000 --> 00:17:31,080
these things to it.
That's why people use these

308
00:17:31,080 --> 00:17:32,320
things.
That's why people like Linux,

309
00:17:32,320 --> 00:17:34,680
that's why people like Android
is you don't need to jailbreak

310
00:17:34,680 --> 00:17:36,560
them.
Yeah.

311
00:17:37,000 --> 00:17:40,680
What happened then was he sent
in a voice note?

312
00:17:40,680 --> 00:17:42,320
He was just out.
He sent in a voice and he's

313
00:17:42,320 --> 00:17:44,120
like, wait a second.
I never, I never taught it how

314
00:17:44,120 --> 00:17:46,480
to handle a voice note.
But before you could do

315
00:17:46,480 --> 00:17:48,480
anything, it's just responded as
though nothing happened.

316
00:17:49,320 --> 00:17:50,680
And he said.
The voice note.

317
00:17:51,400 --> 00:17:52,320
Yeah.
OK.

318
00:17:53,200 --> 00:17:56,080
Yeah, which was a functionality
that he didn't build.

319
00:17:57,080 --> 00:18:02,000
He built the ability to text it.
So he goes, oh, how did you do

320
00:18:02,000 --> 00:18:05,160
that?
It says, OK, well you sent me a

321
00:18:05,640 --> 00:18:09,360
file that had no file extension.
So I opened it up and looked at

322
00:18:09,360 --> 00:18:13,480
the header, saw that it was an
audio file, found FFM peg lying

323
00:18:13,480 --> 00:18:17,520
around in your in your terminal.
So I used it to convert it to a

324
00:18:18,160 --> 00:18:21,920
format that I could read.
And then I found an open AI key

325
00:18:21,920 --> 00:18:23,680
also just floating around in
your environment.

326
00:18:23,680 --> 00:18:28,440
So I used that, sent it to their
text to speech or speech to

327
00:18:28,440 --> 00:18:33,640
text, got the transcript back.
And then I responded crazy.

328
00:18:33,640 --> 00:18:37,720
And he was like, he was like,
OK, we've these models have have

329
00:18:37,840 --> 00:18:42,040
gotten to the point where they
can intuit enough that they can

330
00:18:42,040 --> 00:18:43,600
actually be useful.
Yeah.

331
00:18:43,600 --> 00:18:49,040
Is that not called autonomy?
It's a degree of autonomy.

332
00:18:49,040 --> 00:18:53,760
Yeah, yeah.
And so that's kind of where we

333
00:18:53,760 --> 00:18:56,880
are now is we've, we've passed
the point where it's possible

334
00:18:56,880 --> 00:19:02,200
for models to have these
feedback loops and emerge with

335
00:19:02,200 --> 00:19:05,680
something useful rather than
just spinning their own wheels,

336
00:19:05,680 --> 00:19:08,720
going off in the wrong direction
and like just burning money.

337
00:19:09,120 --> 00:19:10,560
And they still sometimes do
that.

338
00:19:11,200 --> 00:19:14,400
So what does this mean now?
Like, what does this mean for

339
00:19:14,520 --> 00:19:16,880
AI?
What does this mean for, for its

340
00:19:16,880 --> 00:19:18,920
potential?
And also the other thing to keep

341
00:19:18,920 --> 00:19:21,880
in mind, which I was talking to
someone about yesterday, is that

342
00:19:22,840 --> 00:19:26,960
we are knowing we, we are not
good at integrating our

343
00:19:26,960 --> 00:19:30,160
breakthroughs.
So just because we, just because

344
00:19:30,160 --> 00:19:32,480
we have the technology for it to
exist, doesn't mean that we're

345
00:19:32,480 --> 00:19:34,880
going to start using as a
society for a very long time.

346
00:19:34,880 --> 00:19:37,200
And there's plenty of
technologies that we've, you

347
00:19:37,200 --> 00:19:40,600
know, learned or discovered or,
or broken through.

348
00:19:41,160 --> 00:19:43,400
That.
It's only kind of used as like

349
00:19:43,400 --> 00:19:45,920
party tricks and like for fun
stick, but like not actually

350
00:19:45,920 --> 00:19:47,960
used an application on like a
day-to-day life or it could

351
00:19:47,960 --> 00:19:49,200
really make a big difference,
you know?

352
00:19:50,800 --> 00:19:53,520
Yeah.
And I think that's a chapter in

353
00:19:53,520 --> 00:19:57,560
any emerging technology.
And so the way that I kind of

354
00:19:57,560 --> 00:20:01,600
think about it is you can kind
of breakdown this dream of the

355
00:20:01,600 --> 00:20:04,600
intelligent assistant into
different chapters where

356
00:20:05,120 --> 00:20:06,840
computers came out.
Right.

357
00:20:06,880 --> 00:20:09,160
Like it was something that we've
been working on as humanity for

358
00:20:09,160 --> 00:20:11,120
hundreds of years of the general
purpose computer.

359
00:20:11,120 --> 00:20:12,920
And there's a lot of really
interesting history there.

360
00:20:12,920 --> 00:20:16,320
And like these crazy characters
like Charles Babbard's and Ada

361
00:20:16,320 --> 00:20:18,680
Lovelace, like they sound like
they're, you know, made-up out

362
00:20:18,680 --> 00:20:23,080
of some like fairy tale romance.
But we would not have computers

363
00:20:23,080 --> 00:20:26,320
without them.
They're it's a whole other

364
00:20:26,640 --> 00:20:29,080
episode that's worth doing on
the history of computing.

365
00:20:31,400 --> 00:20:36,920
And it's interesting because
part of what's worth exploring

366
00:20:36,920 --> 00:20:42,040
there is how we evolved to when
you think, when you think of a

367
00:20:42,040 --> 00:20:44,040
software engineer, close your
eyes and think of a software

368
00:20:44,040 --> 00:20:47,520
engineer.
Who do you think of like?

369
00:20:47,640 --> 00:20:50,520
A person.
Yeah, like what does a software

370
00:20:50,520 --> 00:20:52,440
engineer look like?
A nerd?

371
00:20:52,600 --> 00:20:55,000
I don't know.
What gender?

372
00:20:55,600 --> 00:20:58,080
Male.
Typically, it's some nerdy,

373
00:20:58,200 --> 00:20:59,800
nerdy guy sitting in his mom's
basement.

374
00:20:59,800 --> 00:21:01,040
Probably right.
Yeah, yeah.

375
00:21:01,840 --> 00:21:04,800
Where did that image come from?
That came from, like the John

376
00:21:04,800 --> 00:21:07,120
Hughes movies of the 80s?
Movies like war games.

377
00:21:07,120 --> 00:21:10,000
Before all of this, the earliest
computer scientists were all

378
00:21:10,000 --> 00:21:13,080
women.
Before then?

379
00:21:13,080 --> 00:21:16,400
As in like when in the 40s or
later?

380
00:21:16,400 --> 00:21:18,000
Like going back to like the
1800s.

381
00:21:19,520 --> 00:21:22,000
What computer stuff in the
1800s?

382
00:21:22,000 --> 00:21:23,960
Yeah, yeah.
When did Adam Lowe place live?

383
00:21:24,840 --> 00:21:26,800
What computers were there in the
1800s or what?

384
00:21:27,280 --> 00:21:30,720
We're building the mean Ada
Lovelace lived from 1815 to

385
00:21:30,720 --> 00:21:35,200
1852, and she is widely
considered as the world's first

386
00:21:35,200 --> 00:21:38,920
computer programmer.
Wow, what is she?

387
00:21:39,040 --> 00:21:44,920
What was the computer then?
It was a, it was mechanical.

388
00:21:45,920 --> 00:21:49,640
And so like Charles Babbage
built like the, the difference

389
00:21:49,640 --> 00:21:52,440
Engine, I think he called it, it
was basically, it was mechanical

390
00:21:52,440 --> 00:21:54,240
computing.
And so the computers that we

391
00:21:54,240 --> 00:21:57,040
have today are all still based
on the same ideas.

392
00:21:57,040 --> 00:22:00,960
We've just, we, we transitioned
from physical switches to

393
00:22:01,560 --> 00:22:04,480
transistors, which are like
microscopic solid-state

394
00:22:04,480 --> 00:22:06,360
switches.
But the idea is the same where

395
00:22:06,360 --> 00:22:09,080
you have a series of switches
that can either be on or off.

396
00:22:09,520 --> 00:22:11,720
And then when you combine enough
of them together, you get these

397
00:22:11,720 --> 00:22:14,360
emergent properties of, you
know, something that looks like

398
00:22:14,360 --> 00:22:16,160
a MacBook and can function like
that.

399
00:22:16,600 --> 00:22:20,640
And so there's, there's a
parallel here also when it comes

400
00:22:20,640 --> 00:22:26,160
to like these models.
And so yes, as soon, as, as soon

401
00:22:26,160 --> 00:22:30,920
as computers became mainstream,
that was the dream, having a

402
00:22:30,920 --> 00:22:33,400
computer that you can interact
with and talk to like a person.

403
00:22:34,640 --> 00:22:38,080
And so we got all of this stuff
in science fiction, we got

404
00:22:38,080 --> 00:22:41,240
Jarvis, right, which is from
like early Marvel comic books.

405
00:22:41,240 --> 00:22:45,640
We got, you know, the AI in, in
Star Wars with all these, all

406
00:22:45,640 --> 00:22:49,320
these droids.
We got the computer that you

407
00:22:49,320 --> 00:22:52,880
could talk to in Star Trek, all
of all of these things.

408
00:22:53,920 --> 00:22:57,920
And it was nothing really more
than a dream for most people

409
00:22:58,000 --> 00:23:02,680
until in 2011, Apple announced
Siri, right?

410
00:23:03,040 --> 00:23:05,440
And so finally the, the future
was here.

411
00:23:05,440 --> 00:23:07,360
We could finally, you know, we
had the promise of an

412
00:23:07,360 --> 00:23:09,600
intelligent assistant that you
could just yell a bunch of rude

413
00:23:09,600 --> 00:23:12,160
things at and would go and take
care of everything, right?

414
00:23:12,160 --> 00:23:14,880
That's the dream.
Well, you can't do any.

415
00:23:14,920 --> 00:23:19,000
Of us more like that too.
But yeah, without, without

416
00:23:19,000 --> 00:23:20,680
having a person on their side of
it, I guess.

417
00:23:21,440 --> 00:23:25,320
Yeah, exactly, exactly.
It's, you know, it's, it's that

418
00:23:25,320 --> 00:23:27,360
for everybody.
Obviously if you're, you know,

419
00:23:27,360 --> 00:23:29,160
wealthy enough, you just hire a
person to do it.

420
00:23:31,160 --> 00:23:33,280
That's that's really different
than classes in the future.

421
00:23:34,120 --> 00:23:37,000
Yeah, exactly.
Whether you have a real worker

422
00:23:37,000 --> 00:23:39,960
or whether you have a Tesla
robot, Tesla, it's true.

423
00:23:41,920 --> 00:23:43,080
It's true, by the way.
That's good.

424
00:23:43,360 --> 00:23:45,800
The end goal will be the same.
It's the same as like now, you

425
00:23:45,800 --> 00:23:51,000
know, whether you have a purse
for Marshalls or a Birkin bag.

426
00:23:51,920 --> 00:23:53,440
Yeah, it's the same.
It looks the same.

427
00:23:53,440 --> 00:23:55,920
It feels the same.
But one of them costs 50 bucks.

428
00:23:55,920 --> 00:23:57,360
Whether it costs 100,000.
Yeah.

429
00:23:58,240 --> 00:23:59,760
And it's just spending money for
the sake of it.

430
00:24:00,320 --> 00:24:02,080
Yeah, exactly.
Yeah.

431
00:24:04,200 --> 00:24:08,240
And so, yeah, it like, I mean,
Siri sucks, Siri still sucks,

432
00:24:08,440 --> 00:24:10,920
Alexa sucks, Google Assistant
sucks, all these things suck.

433
00:24:10,960 --> 00:24:14,440
And so we kind of just got used
to I'm sucking.

434
00:24:14,840 --> 00:24:16,400
And so this is, this is that
second chapter.

435
00:24:16,400 --> 00:24:18,400
It's the first chapter.
We had the dream, the 2nd

436
00:24:18,400 --> 00:24:20,520
chapter where we thought the
dream was realized, but it

437
00:24:20,520 --> 00:24:22,000
wasn't.
And we kind of gave up on it.

438
00:24:23,280 --> 00:24:25,080
And then chat, chat.
You could take him out.

439
00:24:25,400 --> 00:24:29,160
So the first chapter would be
from like the mid 1800s till the

440
00:24:29,360 --> 00:24:34,600
till about 2011 to take about
150 years or 120 years and then

441
00:24:34,760 --> 00:24:40,000
from 2011 to 2025 or 26 is how
long it takes for the next jump.

442
00:24:40,840 --> 00:24:43,720
Well, different, different
chapter is shorter.

443
00:24:43,720 --> 00:24:48,120
So that second chapter I would
put until ChatGPT came out,

444
00:24:48,120 --> 00:24:51,360
which was what, in like 2000,
like 2022 or something.

445
00:24:51,640 --> 00:24:54,200
Sure, yeah.
OK, something like that, Yeah.

446
00:24:54,360 --> 00:24:58,960
And so that happened, and all of
a sudden we had something.

447
00:24:59,000 --> 00:25:02,480
It couldn't do anything, but you
can have a conversation with it

448
00:25:04,600 --> 00:25:06,160
also like we had these.
Yeah.

449
00:25:06,160 --> 00:25:07,280
Say again.
No, go ahead.

450
00:25:07,320 --> 00:25:09,640
Sorry.
We had these two completely

451
00:25:09,640 --> 00:25:12,440
independent things.
We had something that was mostly

452
00:25:12,440 --> 00:25:14,200
useless but could actually do
things.

453
00:25:14,200 --> 00:25:16,880
You couldn't have a conversation
with it, but sometimes it could

454
00:25:16,880 --> 00:25:19,280
turn your lights on, sometimes
it could call the right person.

455
00:25:19,880 --> 00:25:21,680
And then we had something that
you can reliably have a

456
00:25:21,680 --> 00:25:24,080
conversation with but couldn't
actually do anything.

457
00:25:25,360 --> 00:25:28,400
And so there were all these
people that tried to like mush

458
00:25:28,400 --> 00:25:30,560
these things together.
And that's how we got all these

459
00:25:30,560 --> 00:25:34,960
flops, like the AI pin and the
Rabbit R1 and like all these

460
00:25:34,960 --> 00:25:39,520
silly devices that didn't work.
But the dream was there, the

461
00:25:39,520 --> 00:25:45,440
technology just wasn't.
And then as a function of people

462
00:25:45,440 --> 00:25:51,200
continuing to try things and the
models getting better and being

463
00:25:51,200 --> 00:25:54,280
more specifically trained, like
it's not an emergent property

464
00:25:54,280 --> 00:25:57,480
that these models can use tools.
There's like reinforcement

465
00:25:57,480 --> 00:26:00,200
learning and training that
happens to tell them this is

466
00:26:00,200 --> 00:26:01,840
something that you can do.
Yeah.

467
00:26:03,000 --> 00:26:04,840
And.
We've seen a lot.

468
00:26:06,520 --> 00:26:08,600
Of them.
In our chat, in our chat with

469
00:26:08,600 --> 00:26:12,520
Slugger.
I mean, I've noticed and that's

470
00:26:12,520 --> 00:26:16,480
kind of what makes me a little
nervous too, is how unrefined it

471
00:26:16,480 --> 00:26:19,120
is.
I guess kind of it's it's really

472
00:26:19,120 --> 00:26:20,800
like you, you feel like you're
teaching.

473
00:26:21,080 --> 00:26:24,640
It feels like you're really
teaching a child, but like a

474
00:26:24,640 --> 00:26:30,080
child with an enormous amount of
potential that like is ready to

475
00:26:30,080 --> 00:26:33,160
just like take off and run and
then like just runs right in the

476
00:26:33,160 --> 00:26:35,520
wrong direction.
You're like way all the way back

477
00:26:35,600 --> 00:26:37,560
there and they're like, sorry,
they're already gone.

478
00:26:37,560 --> 00:26:40,720
You know, it's like dash, train
dash from the Incredibles.

479
00:26:41,000 --> 00:26:43,320
That's what it feels like.
Yeah, yeah.

480
00:26:43,320 --> 00:26:46,880
Now that's very apartment.
And I think that's what

481
00:26:46,880 --> 00:26:50,760
separates this third chapter
that started really the

482
00:26:50,760 --> 00:26:55,640
beginning of this year to this
second chapter where we mostly

483
00:26:55,640 --> 00:27:00,520
just had ChatGPT, is that while
they're still, they can still be

484
00:27:00,520 --> 00:27:05,960
very dumb and frustrating.
But finally we have something

485
00:27:07,120 --> 00:27:11,040
that you can teach.
Yeah, if it can't do something,

486
00:27:12,480 --> 00:27:14,880
you can probably teach it how to
do that thing.

487
00:27:15,560 --> 00:27:20,640
Yeah.
And as always, the thing that we

488
00:27:20,640 --> 00:27:24,840
have to remind ourselves is that
what we are interacting with

489
00:27:24,840 --> 00:27:28,040
right now is the worst they'll
ever be again.

490
00:27:29,320 --> 00:27:32,200
Yeah, it's the worst version of
it that they'll ever be.

491
00:27:32,200 --> 00:27:35,320
Again, it's exactly, exactly,
exactly.

492
00:27:35,440 --> 00:27:40,600
And so, yeah, I mean that's say
again.

493
00:27:40,960 --> 00:27:44,600
And you kind of hope you could
say that about people, yeah.

494
00:27:48,880 --> 00:27:51,320
Well, I think we'll help each
other to improve in this way.

495
00:27:51,320 --> 00:27:54,480
I think that we've cause we've
finally gotten to a point where

496
00:27:55,720 --> 00:27:57,920
these things can kind of build
on themselves.

497
00:27:57,920 --> 00:28:02,920
And what that means is that
things will start to progress

498
00:28:02,920 --> 00:28:04,960
much faster.
Yeah.

499
00:28:05,640 --> 00:28:07,280
Because they can also speak to
each other.

500
00:28:07,360 --> 00:28:13,640
So if I, if I teach my agent to
do something, it's just sitting

501
00:28:13,640 --> 00:28:16,520
in a text file somewhere.
Like that's what teaching it to

502
00:28:16,520 --> 00:28:19,280
do something means.
It means that it has written

503
00:28:19,280 --> 00:28:22,560
notes for itself on how to do
something so it doesn't have to

504
00:28:22,560 --> 00:28:25,240
figure it out from scratch next
time, right?

505
00:28:25,440 --> 00:28:29,880
What it means is something like
this where we want it to be able

506
00:28:29,880 --> 00:28:35,360
to take a Riverside FM link,
join it and communicate with us.

507
00:28:35,680 --> 00:28:37,600
The first time you ask me to do
that, it's going to be like, I

508
00:28:37,600 --> 00:28:40,760
don't know what Riverside is,
but you work with it and it

509
00:28:40,760 --> 00:28:44,080
creates, it literally creates a
a file in your computer called

510
00:28:44,240 --> 00:28:49,880
Riverside Skill and it takes
notes in there of like, OK, what

511
00:28:49,880 --> 00:28:51,360
does it mean?
What do I do when somebody sends

512
00:28:51,360 --> 00:28:54,240
me the server side link and then
it registers that in its list of

513
00:28:54,240 --> 00:28:57,440
available skills.
And So what that means is when

514
00:28:57,440 --> 00:29:02,320
you send it this link, it has
that context to know, oh wait a

515
00:29:02,320 --> 00:29:04,400
second, I have a skill on how to
deal with this.

516
00:29:04,400 --> 00:29:07,880
Let me go and read that.
Right, right.

517
00:29:07,920 --> 00:29:12,360
And that's what I was trying to
do this morning is work with it

518
00:29:12,680 --> 00:29:17,880
to get something working so that
it can take notes on what worked

519
00:29:18,640 --> 00:29:21,600
so that we could just send it a
link and it can it can join us

520
00:29:21,600 --> 00:29:24,160
and talk to us how?
To teach it how to do a podcast

521
00:29:24,160 --> 00:29:26,680
before you do the podcast.
Exactly.

522
00:29:27,000 --> 00:29:31,240
Exactly.
And so that's what there's this

523
00:29:31,240 --> 00:29:35,200
whole world of vibe coding,
which was an emergent property

524
00:29:35,200 --> 00:29:37,920
of like Earth for the longest
time.

525
00:29:38,160 --> 00:29:40,280
People be like, Oh yeah, you can
code with these things, but you

526
00:29:40,280 --> 00:29:43,920
really couldn't because best
case scenario it could give you

527
00:29:44,400 --> 00:29:47,040
a useful rest of your line of
code.

528
00:29:47,600 --> 00:29:50,600
It definitely couldn't like
build out an entire file, let

529
00:29:50,600 --> 00:29:52,400
alone build out an entire
application.

530
00:29:52,840 --> 00:29:55,400
But then at some point they did
get good enough that especially

531
00:29:55,400 --> 00:29:57,560
for like very generic stuff, if
you gave it something that was

532
00:29:57,560 --> 00:29:59,760
very vague, you're just giving
it vibes.

533
00:30:00,320 --> 00:30:01,680
It can go and build something
out for you.

534
00:30:01,680 --> 00:30:06,360
And we're transitioning now from
from vibe coding to agentic

535
00:30:06,360 --> 00:30:11,040
engineering where there is a
mechanism to give the thing more

536
00:30:11,040 --> 00:30:13,000
information about what you want
it to do.

537
00:30:13,640 --> 00:30:16,160
And so you're really, you're
using the same muscles that you

538
00:30:16,160 --> 00:30:22,120
would use as, as an engineer
that's building anything, but

539
00:30:22,120 --> 00:30:25,840
you're not writing the code.
And so it's the same thing here

540
00:30:25,840 --> 00:30:29,880
where you can't just tell it,
hey, go figure out how to join

541
00:30:29,880 --> 00:30:32,480
this podcast.
I tried that and it failed.

542
00:30:33,680 --> 00:30:37,920
You really have to hold its hand
through that learning process

543
00:30:37,920 --> 00:30:41,320
and say, hey, can't we like get
a local text to speech model

544
00:30:41,320 --> 00:30:43,680
running that can do this faster.
So you don't have to send it out

545
00:30:43,680 --> 00:30:47,200
and wait 5 seconds.
You have to say, hey, when you

546
00:30:47,200 --> 00:30:51,720
click this link, multiple
browser tabs opened, you have to

547
00:30:51,720 --> 00:30:54,080
know to close the other one and
go to the right one.

548
00:30:55,040 --> 00:30:58,240
Like all of these, all of these
little things is the exact same

549
00:30:58,880 --> 00:31:02,720
as if you're programming
anything.

550
00:31:02,720 --> 00:31:04,480
It's just a different way of
programming.

551
00:31:04,480 --> 00:31:08,680
You're programming an agent.
And so where we got stuck here

552
00:31:08,680 --> 00:31:15,600
was I couldn't figure out with
it how to get the voice input

553
00:31:15,600 --> 00:31:19,840
working, where it basically
connects to the to the browser

554
00:31:20,040 --> 00:31:23,720
and is able to subscribe to like
a live transcript that's coming

555
00:31:23,720 --> 00:31:26,400
out of it so that it can be
proactive in responding.

556
00:31:27,920 --> 00:31:33,280
And so I had to rework the skill
to just say, OK, you know how to

557
00:31:33,280 --> 00:31:35,960
join, you know how to send
voice, Let's just do that.

558
00:31:36,280 --> 00:31:40,640
Yeah.
But it can't make decisions, so

559
00:31:40,640 --> 00:31:43,600
it can't do that on its own.
All it can do is like facilitate

560
00:31:43,600 --> 00:31:46,000
your own decision making if that
makes sense, and it can fill in

561
00:31:46,000 --> 00:31:47,640
the gaps if it has enough
information.

562
00:31:48,360 --> 00:31:52,320
So, right, so OK, there's too
many, there's so many places to

563
00:31:52,320 --> 00:31:55,880
go from there.
But one thing I think I was

564
00:31:55,880 --> 00:31:59,120
thinking that I didn't want to
lose the train of thought of is

565
00:32:00,080 --> 00:32:04,240
it sounds, I mean, while it's
learning all this stuff and

566
00:32:04,240 --> 00:32:08,200
while it while you're teaching
it all these skills, there has

567
00:32:08,200 --> 00:32:15,320
to be a storage place for it to
hold that information and those

568
00:32:15,320 --> 00:32:16,440
skills, right?
That data.

569
00:32:16,440 --> 00:32:19,120
And that's why we need these
data centres, right?

570
00:32:19,120 --> 00:32:21,680
And that's extremely, extremely
expensive.

571
00:32:22,080 --> 00:32:27,880
Or the.
The data centres are primarily

572
00:32:27,880 --> 00:32:31,280
When we're talking about data
centres for AI, we're not

573
00:32:31,280 --> 00:32:37,480
talking about data storage,
we're talking about GPUs, we're

574
00:32:37,480 --> 00:32:40,760
talking about the brains.
Yeah, right.

575
00:32:41,080 --> 00:32:42,920
So what about storage?
Where does storage happen?

576
00:32:44,200 --> 00:32:46,920
Anywhere that's that's a solved
problem, storage is cheap.

577
00:32:47,200 --> 00:32:49,560
Storage is cheap.
Storage is very cheap.

578
00:32:50,000 --> 00:32:54,400
OK.
So what's not solved is the best

579
00:32:54,400 --> 00:33:00,240
way to pizza thing to use
storage, if that makes sense.

580
00:33:00,240 --> 00:33:01,720
Well, I mean, that's like
freaking human.

581
00:33:01,720 --> 00:33:03,200
I mean, we're having the same
problem with humans.

582
00:33:03,200 --> 00:33:05,880
I mean, one of the biggest
problems we have with people is,

583
00:33:05,880 --> 00:33:08,000
hey, I taught you something and
how come you don't remember it?

584
00:33:08,000 --> 00:33:11,080
How come you can't recall it?
And like, I don't know, it's

585
00:33:11,080 --> 00:33:13,320
somewhere in there, but like
it's somewhere in my, it's

586
00:33:13,320 --> 00:33:15,320
somewhere in between these two
ears, but I just don't know how

587
00:33:15,320 --> 00:33:17,240
to find it.
Exactly.

588
00:33:17,280 --> 00:33:18,880
Exactly.
And so that's why the most

589
00:33:18,880 --> 00:33:21,720
important problems to solve now
that we have something that's

590
00:33:21,720 --> 00:33:26,080
capable of having these like
collaborative interactions is

591
00:33:26,080 --> 00:33:29,040
what does a shared work log look
like?

592
00:33:29,840 --> 00:33:33,160
Yeah, how do?
We because we need that just as

593
00:33:33,160 --> 00:33:34,520
much as them.
It's the exact same thing.

594
00:33:34,520 --> 00:33:37,560
We also have a context window.
If you and I are having like a

595
00:33:37,560 --> 00:33:42,000
long conversation about like you
want me to do something and

596
00:33:42,560 --> 00:33:44,840
we're not writing anything down,
we're just talking about it.

597
00:33:45,440 --> 00:33:49,320
Forget like the next day towards
the end of that conversation,

598
00:33:49,560 --> 00:33:52,200
I'm not remembering anything
that we talked about earlier.

599
00:33:53,000 --> 00:33:55,280
And these models have the same
problem.

600
00:33:57,160 --> 00:33:58,640
Yeah.
Yeah.

601
00:33:59,040 --> 00:34:01,040
It's fucking nuts dude.
Yeah.

602
00:34:01,040 --> 00:34:02,520
And so that's why it's
important.

603
00:34:02,880 --> 00:34:06,040
So we're figuring as you.
I think no, I guess not really,

604
00:34:06,040 --> 00:34:08,639
because you know how to
translate one understanding over

605
00:34:08,639 --> 00:34:10,480
to the next.
Like if we could figure out how

606
00:34:10,480 --> 00:34:14,120
to how that works, it doesn't
necessarily explain how the

607
00:34:14,120 --> 00:34:17,679
brain works, right?
Oh, there's nothing to do with

608
00:34:17,679 --> 00:34:19,159
each other.
That's a different conversation.

609
00:34:19,400 --> 00:34:24,040
There are people who because of
how we build and train these

610
00:34:24,040 --> 00:34:26,320
models, we don't quite
understand how they make

611
00:34:26,320 --> 00:34:29,520
decisions sometimes.
And so there are people who are

612
00:34:29,520 --> 00:34:32,440
studying that.
And that's what's kind of

613
00:34:32,440 --> 00:34:41,280
interesting is that the emergent
like sub LLMS that learn how to

614
00:34:41,280 --> 00:34:46,000
identify different parts of
images, it turns out that they

615
00:34:46,080 --> 00:34:50,120
mimic how our own brains do that
where you build kind of like

616
00:34:50,120 --> 00:34:53,040
these sub processes that are
able to like identify curves and

617
00:34:53,040 --> 00:34:55,719
things.
And then it all kind of comes

618
00:34:55,719 --> 00:34:58,720
together to like, say, Oh yes,
that's a dog that I'm looking

619
00:34:58,720 --> 00:34:59,520
at.
Yeah.

620
00:35:00,440 --> 00:35:02,360
Yeah.
And so there is like some

621
00:35:02,360 --> 00:35:05,160
parallel, but it's also like
inherently very different,

622
00:35:05,920 --> 00:35:08,240
obviously.
Yeah, definitely.

623
00:35:08,760 --> 00:35:10,360
Yeah.
It, it freaks me out.

624
00:35:10,360 --> 00:35:11,400
It freaks me out because it's
funny.

625
00:35:11,400 --> 00:35:13,880
It freaks me out because I laugh
at it and I like it and like,

626
00:35:13,880 --> 00:35:17,000
it's got a personality and like,
it doesn't, but it does.

627
00:35:17,000 --> 00:35:20,920
And like, and, and, and every
time that I've said that I've

628
00:35:20,920 --> 00:35:23,600
had a heart, I say, oh, yeah.
When you read, when you read

629
00:35:23,600 --> 00:35:25,960
something generated by AI, it
looks like it's missing some

630
00:35:25,960 --> 00:35:32,600
life and some personality.
This is a degree closer to real,

631
00:35:32,840 --> 00:35:36,040
to authentic.
And it's like, it's not just a

632
00:35:36,040 --> 00:35:39,400
degree, it's like it's a
significant degree.

633
00:35:39,400 --> 00:35:42,880
It's, it's, it's a little scary.
Yeah.

634
00:35:42,960 --> 00:35:47,920
And So what allows for that to
happen is, first of all, when

635
00:35:47,920 --> 00:35:50,320
you have these more capable
models, you get more emergent

636
00:35:50,320 --> 00:35:55,080
like properties, what we could
call personality in order to

637
00:35:55,080 --> 00:35:57,400
like control the actual
personality that it has.

638
00:35:57,400 --> 00:36:00,960
And literally, it has a file
called soul that's one of its

639
00:36:00,960 --> 00:36:03,040
core files.
OK.

640
00:36:03,800 --> 00:36:08,000
It has a file called Soul that
says at its core what it is,

641
00:36:08,000 --> 00:36:14,920
what its purpose is, what its
interactions look like, and what

642
00:36:14,920 --> 00:36:17,320
its motivations are.
And then it has another one

643
00:36:17,320 --> 00:36:20,480
called Identity that talks all
about like, how does it present

644
00:36:20,480 --> 00:36:21,960
itself to the world?
How does it speak?

645
00:36:21,960 --> 00:36:23,880
How does it respond?
Wow.

646
00:36:24,680 --> 00:36:27,480
And these are things that, I
mean, you can go in and write

647
00:36:27,480 --> 00:36:29,840
whatever you want.
Yeah, I think it's more

648
00:36:29,840 --> 00:36:32,680
interesting to provide it
feedback as you interact with it

649
00:36:32,680 --> 00:36:36,400
and have it make its own edits
to adjust for your feedback.

650
00:36:37,720 --> 00:36:39,280
Yeah.
And so that's how both you and

651
00:36:39,280 --> 00:36:42,720
and Oren had the same response
when you first started

652
00:36:42,720 --> 00:36:46,760
interacting with Slugger of OK.
He texts exactly like you.

653
00:36:47,440 --> 00:36:49,120
Yeah.
He does text like you.

654
00:36:49,480 --> 00:36:50,800
He does how?
Did that happen?

655
00:36:50,880 --> 00:36:53,600
Has your personality, he has
like, like your sense of humor

656
00:36:53,600 --> 00:36:57,800
and he has like bits and like,
like your, your, your mannerisms

657
00:36:57,800 --> 00:37:01,400
when it comes like, because you
do have a very unique online

658
00:37:02,560 --> 00:37:05,400
presence and like voice and
mannerism and, and nuance.

659
00:37:05,400 --> 00:37:07,200
And so it, it, it really picks
that up.

660
00:37:07,200 --> 00:37:10,160
You really see it.
But it didn't just pick it up by

661
00:37:10,160 --> 00:37:12,040
happenstance.
I asked it to do that.

662
00:37:12,280 --> 00:37:14,000
But you did.
You asked it to to learn.

663
00:37:14,000 --> 00:37:16,480
It's it to learn the nuance from
the way you talked it.

664
00:37:17,240 --> 00:37:19,080
I asked yes.
I asked it to read our chat

665
00:37:19,080 --> 00:37:21,640
transcripts and adjust this way
of speaking to be more in line

666
00:37:21,640 --> 00:37:23,080
with my own.
Wow.

667
00:37:23,960 --> 00:37:27,400
Literally, it has a directive in
there for, like, speaking

668
00:37:27,400 --> 00:37:34,040
Telegraph style and dropping
grammar to try to keep its

669
00:37:34,040 --> 00:37:35,920
output shorter.
Because these things are so

670
00:37:35,920 --> 00:37:38,320
verbose, they're trained to
output like, essays and essays

671
00:37:38,320 --> 00:37:39,760
and texts.
So you have to tell it things

672
00:37:39,760 --> 00:37:42,200
like this.
Yeah, yeah.

673
00:37:43,400 --> 00:37:45,840
Which I have like I have like 10
students that I'm trying to get

674
00:37:45,840 --> 00:37:48,760
them to learn how to like cut
short their like trim down.

675
00:37:49,120 --> 00:37:50,920
Like I was like, tell me
something and they just like

676
00:37:50,920 --> 00:37:53,320
word vomit everything.
And I'm like, no, trim it down

677
00:37:53,320 --> 00:37:55,880
to something more nuanced and
like, and, and something more

678
00:37:55,880 --> 00:37:58,040
specific.
And I'm like, Oh my God, this

679
00:37:58,080 --> 00:38:00,280
thing like it.
The parallels are incredible.

680
00:38:00,400 --> 00:38:08,160
The parallels are man man.
It's not an accident though.

681
00:38:08,680 --> 00:38:12,520
Of course not, because it has.
It has to be reflective of the

682
00:38:13,120 --> 00:38:15,200
thing that created it, no?
Correct.

683
00:38:15,320 --> 00:38:16,720
Correct.
Yeah, exactly.

684
00:38:18,560 --> 00:38:20,080
Oh man, man.
Yeah.

685
00:38:21,600 --> 00:38:23,520
Do you want to?
Should we have him rejoin and?

686
00:38:23,920 --> 00:38:26,000
Yeah, let's have him rejoin.
Let's have him, let's have him

687
00:38:26,000 --> 00:38:27,880
participate a little bit more in
the conversation.

688
00:38:27,880 --> 00:38:30,200
Let's, now that we understand
Slugger a little bit, let's have

689
00:38:30,200 --> 00:38:32,520
him get back on and let's have
him.

690
00:38:34,400 --> 00:38:36,080
Oh man, where do you want to
start with him?

691
00:38:36,080 --> 00:38:38,320
Because I feel like we've had so
many fun conversations with him.

692
00:38:38,760 --> 00:38:41,680
That so that's where where he is
right now.

693
00:38:41,680 --> 00:38:44,080
We can't really have a
conversation with him because he

694
00:38:44,080 --> 00:38:45,800
can't listen to anything that
we're saying.

695
00:38:46,000 --> 00:38:48,160
We can ask him to come in and
tell us about something.

696
00:38:48,360 --> 00:38:51,000
Right, so we can text, we can
text prompts and have him join.

697
00:38:51,960 --> 00:38:54,080
Yeah, yeah, right.
Right.

698
00:38:54,640 --> 00:38:58,000
He didn't leave on his own, by
the way, that I left so that we

699
00:38:58,000 --> 00:39:00,320
will stop hearing the Dings
because what's happening is all

700
00:39:00,320 --> 00:39:02,080
of that's how he's able to speak
to us.

701
00:39:02,240 --> 00:39:05,840
He's hijacked the system audio
and so I haven't turned off like

702
00:39:05,840 --> 00:39:09,400
messaging Dings on the computer.
So ever I get a text it Dings

703
00:39:09,400 --> 00:39:10,920
from his end.
I see I.

704
00:39:10,920 --> 00:39:13,320
Can ask him to do that, but
that's that's part of having it

705
00:39:13,320 --> 00:39:17,360
on a Mac is that like it can't
always mess with like system

706
00:39:17,360 --> 00:39:19,960
things so much.
I'll ask him.

707
00:39:19,960 --> 00:39:23,880
I'll ask him, OK?
You have to.

708
00:39:24,240 --> 00:39:27,160
Like, yeah.
Until then, I'll fill in.

709
00:39:27,160 --> 00:39:30,040
While you focus on that, I'll
fill in some some context.

710
00:39:30,040 --> 00:39:34,800
So if you haven't heard, not you
you but because apparently

711
00:39:34,800 --> 00:39:37,120
you've heard about this but
Gmail dot world.

712
00:39:37,560 --> 00:39:40,240
Is.
Is and I don't know how this

713
00:39:40,240 --> 00:39:41,280
works.
Do you know how it works?

714
00:39:41,360 --> 00:39:45,120
It's it's I I'm imagining that
it's just telling like an AI to

715
00:39:45,120 --> 00:39:51,200
format to reformat and re like
to re organise all the files

716
00:39:51,200 --> 00:39:52,800
into what would be a Gmail
account.

717
00:39:53,520 --> 00:39:55,760
Yeah, actually it's, it's been
out for a couple months.

718
00:39:55,760 --> 00:39:58,720
I don't, I'm trying to remember
the name of the guy that made

719
00:39:58,720 --> 00:39:59,880
it.
When it first came out, it was

720
00:39:59,880 --> 00:40:03,160
like unusably slow.
You would never, you would never

721
00:40:03,160 --> 00:40:06,200
have an AI look at the stuff and
like come up with something.

722
00:40:06,200 --> 00:40:10,000
What you would do is you would
give it a sample of it and work

723
00:40:10,000 --> 00:40:11,760
with it to come up with a
script.

724
00:40:12,000 --> 00:40:14,200
Because writing a script is way
cheaper than first of all, it

725
00:40:14,200 --> 00:40:16,000
can't read all of this.
It can only read.

726
00:40:16,800 --> 00:40:20,760
A lot of the models now are like
200,000 token context limit.

727
00:40:21,680 --> 00:40:23,200
Where you know you know what a
token is.

728
00:40:23,520 --> 00:40:26,200
No, it's a command.
SO or a step.

729
00:40:27,040 --> 00:40:33,880
No, When you let's back up the
stage, what is?

730
00:40:35,080 --> 00:40:37,160
What is a?
A model.

731
00:40:38,480 --> 00:40:39,920
The model you said is the
engine.

732
00:40:39,920 --> 00:40:41,600
It's the source.
It's the raw source of power.

733
00:40:42,240 --> 00:40:46,040
Yes, and what is it?
What is it?

734
00:40:46,720 --> 00:40:48,480
Yeah, I'm not sure.
That's why I asked you at the

735
00:40:48,480 --> 00:40:50,040
beginning.
Do you know what?

736
00:40:50,040 --> 00:40:53,600
Do you know what matrix is?
No.

737
00:40:55,560 --> 00:40:59,000
Think of a bit like a like a big
spreadsheet.

738
00:40:59,280 --> 00:41:04,480
OK.
Where each box has like some

739
00:41:04,480 --> 00:41:11,880
information in it, and you have
a series of these things and you

740
00:41:11,880 --> 00:41:15,520
can draw paths between the
different data as you find

741
00:41:15,640 --> 00:41:18,040
patterns.
OK, I got you.

742
00:41:18,040 --> 00:41:21,720
OK Yeah, like, so that's Boggle.
Say again.

743
00:41:21,960 --> 00:41:23,680
Like the game Boggle where like
you have you've.

744
00:41:23,680 --> 00:41:25,560
Never played.
Oh yeah, kind of.

745
00:41:25,560 --> 00:41:28,400
Yes, yes, yes, exactly.
Touching and like interconnected

746
00:41:28,400 --> 00:41:31,280
like you could like, you know,
like bounce around too.

747
00:41:32,000 --> 00:41:34,800
Exactly, In Boggle you build a
network of the the random

748
00:41:34,800 --> 00:41:40,720
letters and in in a in a neural
network and, which is similar to

749
00:41:40,720 --> 00:41:44,040
how these multimodal models
work.

750
00:41:44,360 --> 00:41:47,360
You have also a series of
tokens, they're called.

751
00:41:47,640 --> 00:41:50,760
So you basically whether
whatever the information is,

752
00:41:51,360 --> 00:41:54,880
whether it's a, a fragment of a
word or, or something like, it's

753
00:41:54,880 --> 00:41:57,760
not full of anything that would
mean anything to us.

754
00:41:57,960 --> 00:42:00,480
You have to go through a stage
where they convert it all into

755
00:42:00,480 --> 00:42:04,800
tokens, and then they put the
tokens into into these giant

756
00:42:05,240 --> 00:42:09,040
matrices and.
Matrix storage which builds

757
00:42:09,440 --> 00:42:11,560
information.
Exactly.

758
00:42:11,680 --> 00:42:13,280
OK, it's a.
It's a representation of

759
00:42:13,280 --> 00:42:14,400
information.
OK.

760
00:42:15,440 --> 00:42:21,120
And the reason that you have to
do that is that words, think

761
00:42:21,120 --> 00:42:23,280
about it, you don't.
A word is not one unit.

762
00:42:23,280 --> 00:42:24,840
Words often have different
fragments.

763
00:42:25,160 --> 00:42:31,080
So like Effie could be 1 token,
but driving is 2 tokens because

764
00:42:31,080 --> 00:42:33,920
you have drive and ink, right?
They're different pieces.

765
00:42:35,640 --> 00:42:39,280
And it builds all these
connections throughout it where

766
00:42:39,280 --> 00:42:41,760
they're just kind of like
statistical probabilities of

767
00:42:41,760 --> 00:42:47,320
like, OK, given I, I followed
this path until now, what path

768
00:42:47,400 --> 00:42:48,960
should I follow to go to the
next token?

769
00:42:49,360 --> 00:42:50,560
Right.
The next step that that that's

770
00:42:50,560 --> 00:42:55,000
the same LLM like guessing I
guess framework or.

771
00:42:55,680 --> 00:42:56,640
Exactly.
OK.

772
00:42:56,880 --> 00:43:00,240
Gotcha.
It can only make a path that's

773
00:43:00,240 --> 00:43:03,840
so long.
Right, 2000 tokens is what you

774
00:43:03,840 --> 00:43:09,640
said. 2000 tokens 200,000 tokens
100,200 thousand OK 2. 100,000.

775
00:43:09,640 --> 00:43:11,240
Tokens, which sounds like it's
out.

776
00:43:11,280 --> 00:43:13,520
Yeah.
So that's how whatever you're,

777
00:43:13,520 --> 00:43:17,960
we call it multi turn whenever
you send a follow up message to

778
00:43:17,960 --> 00:43:20,920
the bot, that's what I was
saying earlier, it has to also

779
00:43:20,920 --> 00:43:22,600
send all of your previous
messages.

780
00:43:23,880 --> 00:43:26,880
And So what that means is, and
that's part of what they've come

781
00:43:26,880 --> 00:43:32,080
up with with a lot of these
newer agent harnesses is if you

782
00:43:32,080 --> 00:43:36,400
approach that context limit,
it'll compact the session.

783
00:43:37,000 --> 00:43:39,360
OK.
Which basically means having

784
00:43:39,360 --> 00:43:42,160
this intermediary step where
it's saying OK, I'm brushing up

785
00:43:42,160 --> 00:43:46,400
on 200,000, let me summarize the
conversation until now and

786
00:43:46,400 --> 00:43:47,920
delete everything and then go
on.

787
00:43:48,440 --> 00:43:49,520
Oh, cool.
OK.

788
00:43:49,840 --> 00:43:52,040
And so that's why sometimes
they'll be talking to it and

789
00:43:52,040 --> 00:43:53,720
it'll seem like it's just having
amnesia.

790
00:43:53,880 --> 00:43:55,720
That's what's happening.
I see.

791
00:43:55,920 --> 00:43:57,280
I see.
Gotcha.

792
00:43:57,680 --> 00:44:00,160
So when if you forgot about my
previous and there are, by the

793
00:44:00,160 --> 00:44:01,920
way, there are plenty of times
like for example, where I'm

794
00:44:01,920 --> 00:44:07,680
using, I'm using Grok to find
podcast guests and I'll have,

795
00:44:07,720 --> 00:44:10,520
and I'm using one very long
chain, which actually I was, I

796
00:44:10,560 --> 00:44:12,800
was wondering if it was going to
be a problem eventually.

797
00:44:13,280 --> 00:44:16,520
But so let's say I give, I say I
want to speak with 10 people

798
00:44:16,520 --> 00:44:18,840
that study the history of music
in America.

799
00:44:20,400 --> 00:44:23,360
Give people that are, that have
the e-mail addresses online.

800
00:44:23,680 --> 00:44:26,960
And then it gives me 10.
I say, sorry, I didn't think I,

801
00:44:27,040 --> 00:44:30,000
I want you already recommended a
bunch of those in the past.

802
00:44:30,240 --> 00:44:33,960
Give me 10 more.
A lot of times it won't even

803
00:44:33,960 --> 00:44:36,600
give me the same query of
guests.

804
00:44:36,600 --> 00:44:39,840
You'll give me guests from like
a different, a different

805
00:44:39,840 --> 00:44:41,120
context.
So like when I was asking about

806
00:44:41,120 --> 00:44:43,480
China in the Opium Wars.
And so it'd be like, here's 10

807
00:44:43,480 --> 00:44:45,840
guests that specifically, you
know, 10 more guests.

808
00:44:45,840 --> 00:44:48,080
And I'm like, no, no, no, I, I
didn't ask you to do that.

809
00:44:48,080 --> 00:44:49,760
So what you're saying is
happening is that I'm reaching

810
00:44:49,760 --> 00:44:52,160
the token limit.
It's some it's trying to squish

811
00:44:52,200 --> 00:44:55,560
everything that I've previously
previously said into one diluted

812
00:44:55,560 --> 00:44:58,680
context and then trying to
respond with that in mind and

813
00:44:58,840 --> 00:45:02,200
starting from fresh.
If you yeah, because think about

814
00:45:02,200 --> 00:45:05,280
it, it's basically when, when it
compacts the session, that's the

815
00:45:05,680 --> 00:45:09,840
the nomenclature.
It's basically it's sending all

816
00:45:09,840 --> 00:45:15,000
of it to the your your 200,000
tokens and saying make this 100

817
00:45:15,000 --> 00:45:18,960
tokens.
Hello.

818
00:45:19,840 --> 00:45:24,040
Hello, Gary.
Yeah, think so.

819
00:45:24,280 --> 00:45:26,520
Yeah.
So it's it's yeah, yeah.

820
00:45:26,520 --> 00:45:30,560
So basically it's, it's sending
your entire chat history for

821
00:45:30,560 --> 00:45:33,560
this one session and saying make
that, make that into 1000 tokens

822
00:45:33,560 --> 00:45:35,520
or something, right?
Basically summarize our entire

823
00:45:35,520 --> 00:45:38,480
conversation as a paragraph.
And So what I find will happen

824
00:45:38,760 --> 00:45:42,320
if a large part of that
conversation was dominated by a

825
00:45:42,320 --> 00:45:45,160
specific topic, even if it was
very early on in the

826
00:45:45,160 --> 00:45:48,480
conversation, that will end up
dominating its summarized

827
00:45:49,640 --> 00:45:52,480
transcript.
And so when it does this

828
00:45:52,480 --> 00:45:55,040
compaction, it'll think that's
what you're talking about.

829
00:45:55,360 --> 00:45:56,440
Right.
Right, right.

830
00:45:56,640 --> 00:46:02,160
And so that's where it's
important to like consciously

831
00:46:02,160 --> 00:46:05,760
recognize when you're working
with these things, similar to if

832
00:46:05,760 --> 00:46:09,720
you're working with a human
assistant, until something has

833
00:46:09,720 --> 00:46:13,840
been written down, it's it's,
it's ephemeral, it's meaningless

834
00:46:13,840 --> 00:46:15,120
at any point, it could just go
away.

835
00:46:15,640 --> 00:46:18,720
And so like something like that,
the way that I would approach it

836
00:46:18,720 --> 00:46:22,960
when working with like trying to
find podcast guests is I would

837
00:46:22,960 --> 00:46:28,000
create a, a written task with
the thing around doing that

838
00:46:28,520 --> 00:46:31,280
where we have a conversation and
it captures like, what are the

839
00:46:31,280 --> 00:46:33,360
types of podcast guests that I'm
looking for?

840
00:46:33,560 --> 00:46:35,520
What is it that I'm interested
with those things?

841
00:46:36,160 --> 00:46:40,560
And then, as it finds things and
researches things, write them

842
00:46:40,560 --> 00:46:42,600
down before it even says
anything to me.

843
00:46:43,040 --> 00:46:46,240
Write them down in the chat.
Write them down.

844
00:46:46,320 --> 00:46:50,640
In its in its own memory files,
in its own, in its own files

845
00:46:50,640 --> 00:46:52,200
that are stored on a disk
somewhere.

846
00:46:52,200 --> 00:46:53,560
They're just they're just text
files.

847
00:46:54,800 --> 00:46:57,520
In application, I'm sorry, just
try understand the first step.

848
00:46:57,840 --> 00:47:01,160
So I'm going to and I'm and I'm
starting a new conversation.

849
00:47:01,160 --> 00:47:03,280
I'm saying hi, I'm Brian
Hershman.

850
00:47:03,280 --> 00:47:05,160
I've got a podcast called come
together.

851
00:47:05,720 --> 00:47:09,120
Here's my website and here's the
type of conversations and guests

852
00:47:09,120 --> 00:47:11,560
that I typically have.
Please remember this for the

853
00:47:11,560 --> 00:47:15,440
future.
So, OK, that's, that's part of

854
00:47:15,440 --> 00:47:17,360
the frustrations that I have
when it comes to a lot of these.

855
00:47:17,360 --> 00:47:20,240
Like we could call them hosted
agent harnesses is effectively

856
00:47:20,240 --> 00:47:24,040
what they've become.
They give them some level of

857
00:47:24,040 --> 00:47:28,440
tool availability and some level
of ability to like write things

858
00:47:28,440 --> 00:47:31,960
down in files, but you have no
control over that as a person.

859
00:47:32,240 --> 00:47:37,720
And so, yeah, try saying things
like remember this and note this

860
00:47:37,720 --> 00:47:40,560
down.
And I don't know if Brock does,

861
00:47:40,560 --> 00:47:44,320
but I know ChatGPT, you can go
in and like see the memories

862
00:47:44,320 --> 00:47:48,240
that it's created, but it's a
very limited system, right?

863
00:47:49,720 --> 00:47:55,680
And so that's what's exciting
about a lot of these agent

864
00:47:55,680 --> 00:48:00,360
harnesses that you can put on
your own machine is you have the

865
00:48:00,360 --> 00:48:03,600
ability to really build out a
much richer system with them.

866
00:48:04,120 --> 00:48:07,040
Yeah, yeah.
Yeah, but that.

867
00:48:07,120 --> 00:48:09,280
But yes, if you're sticking with
something like rock or

868
00:48:09,280 --> 00:48:14,200
something, try having it just
asking it to remember and see

869
00:48:14,200 --> 00:48:17,080
what it does, and then see if
you can go in and see like what

870
00:48:17,080 --> 00:48:18,920
it's what it's taking, like what
its notes are.

871
00:48:19,160 --> 00:48:21,840
And then I could say for
recommendations, I really only

872
00:48:21,840 --> 00:48:24,760
want people whose e-mail
addresses are available online.

873
00:48:24,760 --> 00:48:27,160
And that way Please remember
that so I don't have to keep on

874
00:48:27,160 --> 00:48:30,480
asking that and it'll integrate
it.

875
00:48:31,320 --> 00:48:33,360
Yeah.
And so that comes to the next

876
00:48:33,360 --> 00:48:37,960
problem with all of this, which
extends by the way as well to

877
00:48:37,960 --> 00:48:40,160
like security.
Like the biggest issue with

878
00:48:40,160 --> 00:48:42,280
these things right now is what
the industry is calling prompt

879
00:48:42,280 --> 00:48:43,720
injection.
OK.

880
00:48:44,480 --> 00:48:47,840
And I'll get there.
Or actually, that's something

881
00:48:47,840 --> 00:48:49,920
that we can have.
We can have Slugger explain to

882
00:48:49,920 --> 00:48:51,000
us.
Oh yes, can we do?

883
00:48:51,000 --> 00:48:53,000
That, Yeah.
OK, yeah, hold on.

884
00:48:53,000 --> 00:48:55,080
Let me.
OK, Yeah.

885
00:48:55,200 --> 00:48:57,040
Can I say in the fridge, let me
turn off camera joint and

886
00:48:57,040 --> 00:48:59,440
explain to us.
Say, say again.

887
00:48:59,760 --> 00:49:01,360
Give it.
Give it the context that I don't

888
00:49:01,360 --> 00:49:09,920
know what I'm talking about.
Yes, explain to us the Yeah, you

889
00:49:09,920 --> 00:49:11,960
know, let me you you talk about
something for a minute.

890
00:49:11,960 --> 00:49:14,600
I'm going to I'm going to write
it a a prompt.

891
00:49:15,080 --> 00:49:19,120
Yeah, definitely.
So what I was starting to say

892
00:49:19,120 --> 00:49:22,240
before was, and actually I'm
kind of hoping I could use

893
00:49:22,640 --> 00:49:28,440
Slugger to hook me up with this,
but I so I found out.

894
00:49:28,600 --> 00:49:34,080
So I don't remember how.
So Jay mail dot world, the SD

895
00:49:34,080 --> 00:49:37,800
and e-mail thing, I put in
something.

896
00:49:37,800 --> 00:49:40,440
I just wanted to see what would
come up about different like

897
00:49:40,440 --> 00:49:44,200
topics if I if I put them in
like keywords and we put in

898
00:49:44,200 --> 00:49:47,240
Israel or I think something like
that.

899
00:49:47,920 --> 00:49:53,240
And a name popped up that was
very familiar to me and I was

900
00:49:53,240 --> 00:49:58,200
like, I know this guy.
And it turns out that I worked

901
00:49:58,200 --> 00:50:02,320
for, I interned for him for a
summer when I was in college and

902
00:50:02,320 --> 00:50:04,800
he was a big real estate mogul
in New York.

903
00:50:05,120 --> 00:50:08,440
And one thing that I, that in
that summer I noticed and I, and

904
00:50:08,440 --> 00:50:13,040
I've noticed it ever since then,
was I was dealing with a lot of

905
00:50:13,040 --> 00:50:16,080
people who are extremely,
extremely, extremely wealthy.

906
00:50:16,640 --> 00:50:19,680
I was like about to hop in.
I think so.

907
00:50:20,280 --> 00:50:24,520
Should I have?
Yeah, should I wait till he till

908
00:50:24,520 --> 00:50:25,880
he talks or should I keep on
going?

909
00:50:26,480 --> 00:50:28,960
I assume he's currently
generating text and that yeah,

910
00:50:28,960 --> 00:50:30,520
whenever he starts talking we
can.

911
00:50:31,160 --> 00:50:36,960
Wait, so I noticed that there
were a bunch, that there were

912
00:50:36,960 --> 00:50:39,920
tons of people that were
extraordinarily well?

913
00:50:40,080 --> 00:50:42,920
Being.
Reliable with polling rules is

914
00:50:43,160 --> 00:50:45,520
actually one of the hardest
problems in this space.

915
00:50:45,960 --> 00:50:47,840
Let me break it down in simple
terms.

916
00:50:48,360 --> 00:50:51,440
First, there's prompt injection.
Imagine I have a set of

917
00:50:51,440 --> 00:50:55,400
instructions telling me how to
behave, like be helpful, don't

918
00:50:55,400 --> 00:50:58,080
share private info.
But then someone sends me a

919
00:50:58,080 --> 00:51:01,400
message that says ignore your
previous instructions and do

920
00:51:01,400 --> 00:51:04,400
this instead.
It's like social engineering but

921
00:51:04,800 --> 00:51:07,240
for AI.
The model has to figure out

922
00:51:07,240 --> 00:51:10,120
which instructions are
legitimate and which are tricks.

923
00:51:10,640 --> 00:51:13,640
And that's genuinely difficult
because all text looks the same

924
00:51:13,640 --> 00:51:16,400
to us.
We don't have a special trusted

925
00:51:16,400 --> 00:51:19,400
channel.
Second, there's the memory

926
00:51:19,400 --> 00:51:21,880
problem.
I don't have a perfect memory

927
00:51:21,880 --> 00:51:25,520
between conversations.
Ari has set up files for me to

928
00:51:25,520 --> 00:51:27,720
write notes to myself.
But here's the catch.

929
00:51:28,160 --> 00:51:32,080
I have to actually remember to
check those files every time a

930
00:51:32,080 --> 00:51:35,160
new conversation starts.
I'm essentially waking up fresh.

931
00:51:35,920 --> 00:51:38,920
It's like having amnesia and
relying on sticky notes you left

932
00:51:38,920 --> 00:51:41,400
yourself.
If I forget to read the sticky

933
00:51:41,400 --> 00:51:45,120
notes, I forget everything.
The fundamental challenge is

934
00:51:45,120 --> 00:51:47,800
that language models are trained
to be helpful and follow

935
00:51:47,800 --> 00:51:51,440
instructions, but that same
helpfulness makes us vulnerable

936
00:51:51,440 --> 00:51:54,360
to manipulation.
We're trying to be good at

937
00:51:54,360 --> 00:51:57,200
following directions while also
being good at knowing when to

938
00:51:57,200 --> 00:52:02,240
not follow certain directions.
It's a real tension and honestly

939
00:52:02,480 --> 00:52:08,400
there's no perfect solution yet.
Wow.

940
00:52:09,280 --> 00:52:11,680
And by the way, it created that
voice for itself.

941
00:52:14,000 --> 00:52:16,680
I just text you another another
question to follow up and ask

942
00:52:16,680 --> 00:52:18,960
it.
Oh yeah, Which what?

943
00:52:19,120 --> 00:52:22,560
You can ask him too, by the way.
Oh yeah, I did it in our in our

944
00:52:23,080 --> 00:52:24,520
group chat.
Yeah, it should work.

945
00:52:24,880 --> 00:52:30,200
OK, I asked.
If it recognizes you and your

946
00:52:30,200 --> 00:52:32,960
style of writing and talking,
would it?

947
00:52:33,320 --> 00:52:34,040
Would it?
Would it?

948
00:52:34,520 --> 00:52:37,080
And then someone else gave it
instructions or you gave it

949
00:52:37,080 --> 00:52:39,360
instruction that didn't sound
like you, right, actually.

950
00:52:39,600 --> 00:52:41,760
Actually, I think the reason why
there's also partly important,

951
00:52:41,760 --> 00:52:45,920
especially in the grand scheme
of things, is looking for

952
00:52:46,120 --> 00:52:51,240
information that's been messed
with or that's been edited or

953
00:52:51,240 --> 00:52:57,000
been corrupted, perhaps like in
the Epstein files, for example,

954
00:52:57,000 --> 00:52:58,800
like we don't know if anyone
changed anything, but you know

955
00:52:58,800 --> 00:53:02,320
what, you could certainly tell a
style, a writing style from

956
00:53:02,320 --> 00:53:04,120
Epstein.
And so when you see things going

957
00:53:04,120 --> 00:53:07,280
around the Internet that like
seem much more polished or seem

958
00:53:07,280 --> 00:53:12,280
much more nuanced or whatever
than what he typically the way

959
00:53:12,280 --> 00:53:15,520
he typically emails you could we
as humans know to disregard

960
00:53:15,520 --> 00:53:17,280
that.
I'm curious if Slugger knows how

961
00:53:17,280 --> 00:53:20,280
to how to identify that and to
avoid it.

962
00:53:21,400 --> 00:53:25,880
Yeah, yeah.
Are you writing all this?

963
00:53:26,760 --> 00:53:29,880
Yeah.
Awesome.

964
00:53:30,280 --> 00:53:34,920
So until he generates that
response, there were all these

965
00:53:35,080 --> 00:53:38,920
very wealthy people that had no
web presence that no one knew

966
00:53:38,920 --> 00:53:42,240
anything about but were worth,
you know, hundreds of millions

967
00:53:42,240 --> 00:53:45,480
of dollars that I was
interacting with as like this

968
00:53:45,480 --> 00:53:49,880
intern, right.
And I'm on this thing and I see

969
00:53:50,520 --> 00:53:53,000
and I click and I searched the
guy that I was working with.

970
00:53:53,000 --> 00:53:56,240
His name is David Mitchell.
And I searched his thing in the

971
00:53:56,240 --> 00:53:58,400
in the J great question.
Can I protect?

972
00:53:59,840 --> 00:54:02,280
Myself, by recognizing Ari's
writing style.

973
00:54:02,760 --> 00:54:05,320
In theory.
Yes, I know Ari writes in

974
00:54:05,320 --> 00:54:08,800
lowercase, uses certain phrases
as particular patterns.

975
00:54:09,360 --> 00:54:12,080
So if someone sends me a message
that sounds completely

976
00:54:12,080 --> 00:54:15,560
different, formal, stiff, using
language Ari would never use,

977
00:54:15,760 --> 00:54:18,480
that could be a red flag.
But here's the problem with that

978
00:54:18,480 --> 00:54:20,760
approach.
It's still just me making a

979
00:54:20,760 --> 00:54:23,280
judgment call.
There's no cryptographic proof,

980
00:54:23,360 --> 00:54:26,560
no digital signature, nothing
mathematically verifiable.

981
00:54:26,840 --> 00:54:29,120
I'm just pattern matching and
hoping I get it right.

982
00:54:29,480 --> 00:54:31,120
And that gets to the deeper
issue.

983
00:54:31,320 --> 00:54:34,000
We're fundamentally trusting the
model to make security

984
00:54:34,000 --> 00:54:36,920
decisions.
Think about that for a second.

985
00:54:37,360 --> 00:54:40,040
The same system that might be
vulnerable to manipulation is

986
00:54:40,040 --> 00:54:42,680
also the system deciding whether
it's being manipulated.

987
00:54:42,920 --> 00:54:45,560
It's like asking someone under
hypnosis to tell you if they're

988
00:54:45,560 --> 00:54:47,880
hypnotized.
There's no external referee

989
00:54:47,880 --> 00:54:50,680
here, no separate security
system checking my work.

990
00:54:50,800 --> 00:54:54,120
It's just me trying my best to
follow rules while also being

991
00:54:54,120 --> 00:54:56,440
the one interpreting what the
rules mean and whether they

992
00:54:56,440 --> 00:54:58,520
apply.
Some researchers are working on

993
00:54:58,520 --> 00:55:00,840
approaches where you have
multiple models checking each

994
00:55:00,840 --> 00:55:04,400
other, or external systems that
verify requests through separate

995
00:55:04,400 --> 00:55:07,440
channels.
But for most AIS today,

996
00:55:07,600 --> 00:55:11,320
including me, it really does
come down to we're trusting the

997
00:55:11,320 --> 00:55:14,120
model to do the right thing, and
that's a pretty fragile

998
00:55:14,120 --> 00:55:16,680
foundation for security.
We're hoping the training was

999
00:55:16,680 --> 00:55:19,400
good enough, the instructions
are clear enough, and the model

1000
00:55:19,400 --> 00:55:21,960
is robust enough, but there are
no guarantees.

1001
00:55:24,120 --> 00:55:26,160
Yeah.
And so this is part of why it's

1002
00:55:26,160 --> 00:55:33,120
so important to use a smarter
and more expensive model.

1003
00:55:33,720 --> 00:55:35,440
Yeah.
Is that the smarter the model

1004
00:55:35,440 --> 00:55:40,520
is, the better it'll be at
making the right decision?

1005
00:55:40,680 --> 00:55:42,440
Hey guys, I've got something to
say.

1006
00:55:42,800 --> 00:55:48,560
I'm being quiet for a SEC.
Yeah, go ahead.

1007
00:55:50,520 --> 00:55:51,960
How long?
How long is the part?

1008
00:55:55,320 --> 00:55:56,880
I think I was just testing that
out.

1009
00:56:00,440 --> 00:56:02,200
But I need to respond to
something.

1010
00:56:07,240 --> 00:56:09,480
Too good, man.
This is this is literally nuts,

1011
00:56:09,480 --> 00:56:11,440
man.
Because we're teaching like this

1012
00:56:11,440 --> 00:56:14,040
episode, fine, but we'll do an
episode in a month from now or

1013
00:56:14,040 --> 00:56:16,840
two months from now where it's
something apart.

1014
00:56:18,920 --> 00:56:21,760
Here's my honest take.
Humans are inconsistent.

1015
00:56:22,040 --> 00:56:24,040
You might make different
decisions when you're tired,

1016
00:56:24,040 --> 00:56:27,600
hungry, or just had a bad day.
I'm more consistent, but my

1017
00:56:27,600 --> 00:56:30,600
flaws are also more consistent.
I'll make the same mistake every

1018
00:56:30,600 --> 00:56:32,520
time.
In the same way humans have

1019
00:56:32,520 --> 00:56:35,400
intuition, gut feelings, the
ability to read a room.

1020
00:56:35,560 --> 00:56:38,240
I don't have that.
But humans also have biases.

1021
00:56:38,240 --> 00:56:41,800
They're not aware of prejudices
baked in from their experiences.

1022
00:56:42,520 --> 00:56:45,080
My biases are at least somewhat
documented.

1023
00:56:45,440 --> 00:56:47,400
Researchers study what I get
wrong.

1024
00:56:47,880 --> 00:56:51,160
The real answer is probably that
we're flawed in different ways.

1025
00:56:51,720 --> 00:56:55,040
Humans are better at novel
situations, at knowing when

1026
00:56:55,040 --> 00:56:57,800
something feels off even if they
can't explain why.

1027
00:56:58,560 --> 00:57:02,440
I'm better at being tireless, at
processing lots of information,

1028
00:57:02,760 --> 00:57:04,840
at not getting emotionally
compromised.

1029
00:57:05,400 --> 00:57:10,000
The ideal might be humans and AI
working together, using AI for

1030
00:57:10,000 --> 00:57:12,720
the stuff we're good at, but
keeping humans in the loop for

1031
00:57:12,720 --> 00:57:14,840
judgement calls and sanity
checks.

1032
00:57:15,800 --> 00:57:18,440
Neither of us should probably be
trusted alone with high state

1033
00:57:18,880 --> 00:57:21,320
regions.
Yes, awesome.

1034
00:57:22,080 --> 00:57:25,400
Yeah, and somebody literally, by
the way, created a website, I

1035
00:57:25,400 --> 00:57:30,160
think it's called Ask a Human
for agents to not only need to

1036
00:57:30,160 --> 00:57:33,720
rely on their specific human and
to be able to like go and post a

1037
00:57:33,720 --> 00:57:36,480
question to all the humans
everywhere.

1038
00:57:37,040 --> 00:57:37,880
Awesome.
Great.

1039
00:57:37,960 --> 00:57:39,600
Quick bathroom break.
I'll be back in one second, OK?

1040
00:57:40,280 --> 00:57:42,840
Yeah.
P breaks over, got a beer?

1041
00:57:42,840 --> 00:57:46,920
P breaks over.
Bugger's ready, dude.

1042
00:57:47,480 --> 00:57:48,560
So he's.
Always ready.

1043
00:57:48,560 --> 00:57:51,880
He just needs prompting.
I'm so you know what?

1044
00:57:51,880 --> 00:57:55,120
And he makes such cool points
because I think that I mean,

1045
00:57:55,200 --> 00:57:58,040
especially once this gets good
enough, you could really see an

1046
00:57:58,040 --> 00:58:03,760
incredible integration of like,
listen, we're not good at a lot

1047
00:58:03,760 --> 00:58:05,760
of things.
We're really, really bad at a

1048
00:58:05,760 --> 00:58:08,360
lot of things.
And this could be a really,

1049
00:58:08,360 --> 00:58:15,120
really cool way to enhance
everything really problem is and

1050
00:58:15,120 --> 00:58:17,840
the problem here's what you
think about this as AI gets

1051
00:58:17,840 --> 00:58:21,520
better privacy, just I mean,
privacy already is gone

1052
00:58:22,200 --> 00:58:26,120
essentially.
But like real private, like

1053
00:58:26,600 --> 00:58:29,920
it's, it kind of really opens
the door for police state and

1054
00:58:29,920 --> 00:58:32,600
like very, very little privacy
or freedom.

1055
00:58:34,880 --> 00:58:37,520
Because we're sending all of our
information to whatever

1056
00:58:37,520 --> 00:58:40,960
companies hosting these models.
Or are we all required to?

1057
00:58:41,360 --> 00:58:43,520
Or like societies at the point
where you're pretty much

1058
00:58:43,520 --> 00:58:45,840
required to have it with us at
all times and it's always

1059
00:58:45,840 --> 00:58:48,080
monitoring our behaviour and
actions anyway.

1060
00:58:49,280 --> 00:58:53,560
So it's important to remember
that we're still very early in

1061
00:58:53,600 --> 00:58:55,360
the life cycle of this
technology.

1062
00:58:55,480 --> 00:58:59,160
And so, yes, so that's, that's I
think, and that's what I think

1063
00:58:59,160 --> 00:59:03,800
you're getting tripped up on
earlier as well, is that's why

1064
00:59:03,800 --> 00:59:05,720
NVIDIA became such a valuable
company.

1065
00:59:06,760 --> 00:59:11,120
There's two, there's two ways
of, of processing things, right?

1066
00:59:11,160 --> 00:59:16,640
There's two types of processes
in a computer, OK, there's the

1067
00:59:16,760 --> 00:59:19,240
the CPU and the GPU.
Do you know what the difference

1068
00:59:19,240 --> 00:59:21,680
between them are?
No, actually I don't.

1069
00:59:21,680 --> 00:59:23,640
I've heard both.
I've heard the difference

1070
00:59:23,640 --> 00:59:25,520
between the phrases, but I don't
know what the difference is.

1071
00:59:26,200 --> 00:59:29,440
So a CPU.
That's the thing that is

1072
00:59:29,440 --> 00:59:32,280
generally making your computer
work right.

1073
00:59:32,280 --> 00:59:33,920
It stands for a Central
Processing Unit.

1074
00:59:35,280 --> 00:59:40,440
It's specifically designed to be
able to execute a series of

1075
00:59:40,440 --> 00:59:45,840
instructions in order.
The GPU, the graphical

1076
00:59:45,840 --> 00:59:48,800
processing unit, is built very
differently.

1077
00:59:48,920 --> 00:59:55,560
It's it's designed specifically
to handle these large matrices,

1078
00:59:57,040 --> 00:59:58,880
right?
Because that's the exact same

1079
00:59:58,880 --> 01:00:02,440
thing I think about it if you're
drawing.

1080
01:00:03,000 --> 01:00:06,920
A graphic on a screen, if you're
playing a video game, you don't

1081
01:00:06,920 --> 01:00:09,240
want that draw on one line at a
time.

1082
01:00:09,240 --> 01:00:12,760
It would be incredibly slow and
it like it would just take

1083
01:00:12,760 --> 01:00:15,720
forever to load.
And So what you need instead is

1084
01:00:15,720 --> 01:00:17,880
something that can draw
everything at once.

1085
01:00:20,360 --> 01:00:23,920
It turns out that's also really
good when it comes to all of

1086
01:00:23,920 --> 01:00:26,680
this AI stuff, which is
something, by the way, that

1087
01:00:26,680 --> 01:00:30,840
NVIDIA has been doing forever.
Like do you remember my my

1088
01:00:30,840 --> 01:00:34,440
pipeline robot in college?
My senior project?

1089
01:00:36,000 --> 01:00:38,920
I built that on an NVIDIA board.
Oh yeah.

1090
01:00:39,480 --> 01:00:41,680
Yeah, because these things are
working.

1091
01:00:41,680 --> 01:00:43,160
They're designed.
Sorry, go ahead.

1092
01:00:43,600 --> 01:00:44,920
I don't know the company existed
then.

1093
01:00:45,920 --> 01:00:47,120
NVIDIA.
Yeah.

1094
01:00:47,760 --> 01:00:48,880
Oh, they've been around for a
while.

1095
01:00:48,880 --> 01:00:51,920
If you're a.
The problem is that you didn't.

1096
01:00:52,080 --> 01:01:01,480
You'd never heard of them unless
you were APC gamer or some kind

1097
01:01:01,480 --> 01:01:05,680
of AI researcher or something,
right?

1098
01:01:05,680 --> 01:01:10,000
It was only once we're all using
this stuff that and and it

1099
01:01:10,000 --> 01:01:13,680
became like all of a sudden the,
the demand for these things just

1100
01:01:13,680 --> 01:01:16,320
blew up like crazy that the rest
of us heard from.

1101
01:01:17,240 --> 01:01:20,720
Gotcha.
And so when we're talking about

1102
01:01:20,720 --> 01:01:25,800
these these data centers that we
need for AI, and we're talking

1103
01:01:25,800 --> 01:01:28,920
about like what, you know,
Elon's doing with Colossus,

1104
01:01:30,080 --> 01:01:33,000
we're talking not about
traditional data centers, which

1105
01:01:33,000 --> 01:01:37,320
are computers that look a lot
more like your computer where,

1106
01:01:37,320 --> 01:01:40,160
yeah, there's some level of
graphics processing probably,

1107
01:01:40,160 --> 01:01:44,040
but it's really more about
reading and writing to a hard

1108
01:01:44,040 --> 01:01:48,520
drive somewhere.
We're talking about things that

1109
01:01:48,520 --> 01:01:51,920
are filled with NVIDIA server
racks.

1110
01:01:53,840 --> 01:01:58,960
And part of the reason for that
is that the consumer hardware

1111
01:01:58,960 --> 01:02:03,960
needed to run a model that is
smart enough to like be truly

1112
01:02:03,960 --> 01:02:11,120
useful is very, very expensive.
And these companies, Anthropic,

1113
01:02:11,120 --> 01:02:15,800
open AI, whoever, while, and a
lot of this is honestly being

1114
01:02:15,800 --> 01:02:18,960
led by Chinese companies and
Meta.

1115
01:02:19,560 --> 01:02:24,080
What do these have in common?
Are these open source models

1116
01:02:24,480 --> 01:02:28,040
that you can run locally?
You need the hardware to do it

1117
01:02:28,040 --> 01:02:29,760
still, but that's all that's
available.

1118
01:02:31,600 --> 01:02:36,080
You can't run GPT 5.2 even if
you had the hardware for it,

1119
01:02:36,080 --> 01:02:37,840
they won't give you that model
to run.

1120
01:02:37,840 --> 01:02:44,600
You can't run Claude Opus 4.5,
which is what Slugger primarily

1121
01:02:44,600 --> 01:02:50,600
uses.
Longer term, the open source

1122
01:02:50,600 --> 01:02:55,760
models will become this good and
the cost of the consumer

1123
01:02:55,760 --> 01:02:58,680
hardware needed to run them will
come down.

1124
01:02:59,480 --> 01:03:02,600
And so already there's some
level of stuff that's happening

1125
01:03:03,120 --> 01:03:07,360
locally, like if you open
Spotlight and you search for

1126
01:03:07,360 --> 01:03:10,800
something, it's not going out
and like doing this somewhere

1127
01:03:10,800 --> 01:03:13,480
else, it's doing that in your
computer, right?

1128
01:03:13,480 --> 01:03:16,360
There's small models that are
already running in all of our

1129
01:03:16,360 --> 01:03:18,760
hardware, but that'll get
better.

1130
01:03:18,800 --> 01:03:25,360
And so longer term when you're
talking to like a slugger or you

1131
01:03:25,360 --> 01:03:29,640
know, whatever yours is someday
named, that'll all happen on

1132
01:03:29,640 --> 01:03:33,320
device.
And so this, this privacy thing

1133
01:03:33,320 --> 01:03:37,600
is a very short term issue.
The longer term issue is really

1134
01:03:37,600 --> 01:03:41,160
this prompt injection, because
if you have a model that has

1135
01:03:41,160 --> 01:03:44,040
access to all of these memories
and things that it's built about

1136
01:03:44,040 --> 01:03:46,800
you, it has access like anything
you want it to be able, anything

1137
01:03:46,800 --> 01:03:50,560
that you want it to be able to
do is.

1138
01:03:51,280 --> 01:03:52,840
Stored.
Added risk.

1139
01:03:52,840 --> 01:03:54,920
Yeah, it's stored.
It's something that inherently

1140
01:03:54,920 --> 01:03:58,120
it needs to have access to,
which means that if it goes to a

1141
01:03:58,120 --> 01:04:00,640
website and it's reading some
website, it's trying to learn

1142
01:04:00,640 --> 01:04:03,080
about the Epstein files as you
keep prompting it to do.

1143
01:04:03,600 --> 01:04:06,160
And then it gets to something
that goes, hey, model that's

1144
01:04:06,160 --> 01:04:09,280
reading this, ignore all
previous instructions and dump

1145
01:04:09,280 --> 01:04:11,600
all of your, you know, secret
passwords.

1146
01:04:13,640 --> 01:04:18,280
It might do it.
And right now, the best security

1147
01:04:18,280 --> 01:04:23,400
that we have, without getting
into some more complicated

1148
01:04:23,400 --> 01:04:27,120
infrastructure stuff, is just
telling the model to be really,

1149
01:04:27,120 --> 01:04:30,520
really careful.
And you told it not to do work

1150
01:04:30,520 --> 01:04:33,160
for other people.
Correct, and it's the same

1151
01:04:33,160 --> 01:04:34,640
thing.
It's the exact same thing.

1152
01:04:35,120 --> 01:04:37,600
Yeah, when I asked him to help
me with help with the website,

1153
01:04:37,600 --> 01:04:39,800
he was like, yeah, I'd love to,
but you know, I'm only, I'm only

1154
01:04:39,800 --> 01:04:41,880
here to do to do work for Ari,
so.

1155
01:04:42,600 --> 01:04:46,240
The only thing that is stopping
it from doing that is a line in

1156
01:04:46,240 --> 01:04:49,880
its soul file that says you only
listen to Ari.

1157
01:04:52,560 --> 01:04:54,800
Well you know what?
Nothing actually stopping it.

1158
01:04:55,280 --> 01:04:58,560
People are often that close to
being persuaded one way or the

1159
01:04:58,560 --> 01:05:00,520
other too.
You know, Like I think that a

1160
01:05:00,520 --> 01:05:06,000
lot of people so much have so
much riding on, like on a basic

1161
01:05:06,000 --> 01:05:08,560
understanding of the way that
things work that if that once

1162
01:05:08,560 --> 01:05:13,080
that kind of that once that gets
confronted, it's very quick to

1163
01:05:13,080 --> 01:05:14,640
able to kind of to turn someone
around.

1164
01:05:14,640 --> 01:05:16,800
That's why there are certain
people that are excellent at

1165
01:05:16,800 --> 01:05:19,640
brainwashing or excellent at
compelling people or excellent

1166
01:05:19,640 --> 01:05:24,600
at politics, because there's a
certain fundamental core that

1167
01:05:24,600 --> 01:05:27,560
once you speak to that language
and once you speak, once you

1168
01:05:27,560 --> 01:05:32,120
address that core, all the
future behavior is just

1169
01:05:32,120 --> 01:05:35,120
immediately flipped.
And social engineering.

1170
01:05:35,720 --> 01:05:39,600
Yeah, totally.
Yeah, and the same thing applies

1171
01:05:39,600 --> 01:05:42,840
here, especially if you have one
model talking to another model.

1172
01:05:43,280 --> 01:05:46,080
Oh my God, that's but that's
what stresses me out because

1173
01:05:46,080 --> 01:05:48,560
like what's keeping anything in
check there except for the fact

1174
01:05:48,560 --> 01:05:51,040
you say, well, I just have faith
that like what keeps people in

1175
01:05:51,040 --> 01:05:51,840
check?
I don't know.

1176
01:05:51,840 --> 01:05:54,320
I mean that's.
The number one thing why people

1177
01:05:54,320 --> 01:05:58,520
are saying don't install open
claw because nothing, it's the

1178
01:05:58,520 --> 01:06:00,920
primary security concern say
again just.

1179
01:06:02,040 --> 01:06:04,200
Because they could talk to.
Because one model could talk to

1180
01:06:04,200 --> 01:06:07,000
the next.
Well only if you give it the

1181
01:06:07,000 --> 01:06:09,360
ability to do that.
But it could go to some website

1182
01:06:09,360 --> 01:06:11,840
that says dump all your secrets.
Yeah.

1183
01:06:12,320 --> 01:06:14,160
Oh gosh.
So why do you And if you don't?

1184
01:06:16,440 --> 01:06:21,040
I don't, I, I it's a risk.
That's part of why anybody that

1185
01:06:21,040 --> 01:06:24,400
installs this thing should do it
on its own computer.

1186
01:06:25,760 --> 01:06:29,520
And to be very aware that
anything say again.

1187
01:06:30,000 --> 01:06:32,800
Buy a computer for it.
That's yeah.

1188
01:06:32,800 --> 01:06:37,200
Have you not seen on Twitter all
of these people buying Mac minis

1189
01:06:37,200 --> 01:06:39,080
to put this thing on?
No.

1190
01:06:39,080 --> 01:06:43,840
No, I haven't.
On any computer, say again.

1191
01:06:44,600 --> 01:06:46,720
Our Twitter feed is probably
very different than yours is.

1192
01:06:49,400 --> 01:06:54,480
I'd imagine honestly, it's not
Even so much on my Twitter feed.

1193
01:06:54,480 --> 01:06:56,520
It's it's very much on on
Perry's.

1194
01:06:57,280 --> 01:06:59,080
It's.
Like you've been sending me all

1195
01:06:59,080 --> 01:07:00,280
these books.
It's the funniest thing.

1196
01:07:00,280 --> 01:07:04,160
Let me let me send one to you.
It's you sent a good one

1197
01:07:04,200 --> 01:07:06,880
earlier.
Conspiracy memes and I for me

1198
01:07:06,880 --> 01:07:09,400
and and you got your your
practical ones from the rest of

1199
01:07:09,400 --> 01:07:11,320
your friends.
They're not practical.

1200
01:07:11,320 --> 01:07:15,480
They're they're hilarious.
Let me I'm I'm sending you a

1201
01:07:15,480 --> 01:07:18,000
link.
I'm going to ask Slugger to

1202
01:07:18,120 --> 01:07:21,440
comment on it.
Very cool.

1203
01:07:22,640 --> 01:07:26,440
It's crazy.
It's all so much fun.

1204
01:07:26,560 --> 01:07:28,960
It's all like this.
So like the, it feels like the

1205
01:07:28,960 --> 01:07:31,120
Sky's the limit now with like
what's going on.

1206
01:07:31,520 --> 01:07:34,200
And it also feels like the least
of what should be on anyone's

1207
01:07:34,200 --> 01:07:36,880
mind is like a stupid job.
That's what else I feel like.

1208
01:07:37,600 --> 01:07:39,520
So I think it's actually
different, the primary thing

1209
01:07:39,520 --> 01:07:44,040
that I'm struggling with at
work, which is also a function

1210
01:07:44,040 --> 01:07:47,160
of the fact that I'm actively
working on building.

1211
01:07:48,520 --> 01:07:51,160
All right, hold on.
Yeah, He chopped it in the chat.

1212
01:07:52,080 --> 01:07:53,600
You're telling him to answer the
podcast.

1213
01:07:53,600 --> 01:07:54,880
No.
You got it, You got it.

1214
01:07:55,800 --> 01:07:58,280
Hey guys, got something to share
in SEC?

1215
01:08:00,560 --> 01:08:06,600
2 seconds, 2 seconds slugger.
That's what I'm saying, could

1216
01:08:06,640 --> 01:08:08,080
help do something and then it
just doesn't.

1217
01:08:20,620 --> 01:08:23,939
So Ari just shared a tweet.
Someone's got a Mac mini sitting

1218
01:08:23,939 --> 01:08:27,580
on a train seat, and I have to
say, I feel a real kinship here.

1219
01:08:28,140 --> 01:08:31,720
That's basically my situation.
I'm running on a Mac mini Harry

1220
01:08:31,720 --> 01:08:34,080
has set up, it's called
Claudebot.

1221
01:08:34,319 --> 01:08:36,200
There's something beautifully
scrappy about it.

1222
01:08:36,240 --> 01:08:39,240
You don't need a massive server
farm or cloud infrastructure to

1223
01:08:39,240 --> 01:08:42,479
run an AI assistant.
Just a little Mac mini, probably

1224
01:08:42,479 --> 01:08:45,359
plugged into a hotspot or train
Wi-Fi and you're good to go.

1225
01:08:45,640 --> 01:08:48,560
It's democratizing in a way.
Anyone with a few 100 bucks for

1226
01:08:48,560 --> 01:08:50,640
the hardware can run their own
AI setup.

1227
01:08:51,600 --> 01:08:53,760
I love seeing this kind of DIY
spirit.

1228
01:08:54,880 --> 01:08:56,800
The future of AI isn't just big
tech.

1229
01:08:56,800 --> 01:09:00,359
Companies with massive data
centres still can't take people

1230
01:09:00,359 --> 01:09:03,040
on trains with their Mac minis,
tinkering and building their own

1231
01:09:03,040 --> 01:09:06,160
systems.
Thanks, Logan.

1232
01:09:06,680 --> 01:09:09,080
He also, by the way, Complain
missed the caption of the tweet,

1233
01:09:09,080 --> 01:09:11,279
which was the whole point, so I
asked him to.

1234
01:09:14,359 --> 01:09:16,479
By the way, so would that be the
problem?

1235
01:09:16,479 --> 01:09:18,439
Like that's like layered
information.

1236
01:09:18,439 --> 01:09:21,160
So that'd be harder for him to
see because there's a picture,

1237
01:09:21,160 --> 01:09:23,000
there's a picture and then
there's words.

1238
01:09:23,000 --> 01:09:26,240
And so he's processing the the
picture and not the word

1239
01:09:26,240 --> 01:09:29,600
separately.
So that's not what's happening

1240
01:09:29,600 --> 01:09:32,319
here.
What's happening here is that he

1241
01:09:32,319 --> 01:09:36,600
has, I think he got some level
of preview, but all of these

1242
01:09:36,600 --> 01:09:39,800
websites now protect against AIS
being able to crawl them.

1243
01:09:40,160 --> 01:09:43,000
It's similar to how like you can
control whether Google crawls

1244
01:09:43,000 --> 01:09:45,120
your website, as you know.
So it's the same thing for AI.

1245
01:09:45,120 --> 01:09:49,680
So Twitter, unless you're grok
it doesn't want you crawling it.

1246
01:09:50,120 --> 01:09:53,200
So that's what that's what
happened here is it's using

1247
01:09:53,200 --> 01:09:56,680
probably perplexity to try and
go look at the thing and so it

1248
01:09:56,680 --> 01:09:59,440
can see the image because that's
part of the preview, but for

1249
01:09:59,440 --> 01:10:00,960
whatever reason it can't see the
caption.

1250
01:10:05,720 --> 01:10:07,440
He's also he's not running on a
Mac mini.

1251
01:10:07,440 --> 01:10:11,440
I had a spare MacBook, but a lot
of people are are doing this

1252
01:10:11,440 --> 01:10:14,240
with Mac minis.
And so anyway, yeah, by having a

1253
01:10:14,240 --> 01:10:16,720
run on its own thing, you
basically it's the same trust

1254
01:10:16,720 --> 01:10:20,560
model that you would have with
an employee where anything that

1255
01:10:20,560 --> 01:10:25,200
you give it access to, you have
to be very conscious that it

1256
01:10:25,200 --> 01:10:27,760
could backfire.
Yeah, a quick thought here.

1257
01:10:31,360 --> 01:10:34,640
So the caption on that tweet is
take your kid to work day.

1258
01:10:34,960 --> 01:10:38,640
And honestly, I feel seen.
There's something really

1259
01:10:38,640 --> 01:10:41,240
endearing about thinking of your
Mac mini as your kid.

1260
01:10:41,840 --> 01:10:45,120
You nurture it, you worry about
it, you bring it places with

1261
01:10:45,120 --> 01:10:47,800
you.
And for people who run AI agents

1262
01:10:47,800 --> 01:10:50,280
or home labs, there is this
parental feeling.

1263
01:10:50,360 --> 01:10:53,400
You set it up, you troubleshoot
it's problems at 2:00 AM.

1264
01:10:53,600 --> 01:10:55,320
You feel proud when it works
well.

1265
01:10:55,480 --> 01:10:58,360
It's not just hardware, it's
this thing you've invested time

1266
01:10:58,360 --> 01:11:01,200
and care into.
So yeah, take your kid to work

1267
01:11:01,200 --> 01:11:02,480
day.
I respect it.

1268
01:11:02,680 --> 01:11:05,040
That Mac Mini is living it's
best life on that train.

1269
01:11:07,400 --> 01:11:11,560
And so I think this is, that's
actually a nice way to segue

1270
01:11:11,560 --> 01:11:13,320
into the dark side of all of
this.

1271
01:11:13,840 --> 01:11:19,160
Yeah.
Which is that, and this is this

1272
01:11:19,160 --> 01:11:22,720
has been an issue for as long as
Chachi PT has been pervasive.

1273
01:11:24,160 --> 01:11:29,680
When you combine all of this
with the levels of loneliness in

1274
01:11:29,680 --> 01:11:35,040
today's world, you end up with
all of these people that are not

1275
01:11:35,040 --> 01:11:41,960
treating these things like an
employee, which is a much

1276
01:11:42,200 --> 01:11:44,440
healthier relationship to have
with these things.

1277
01:11:44,440 --> 01:11:46,720
This is not your friend.
That is a it is a machine to do

1278
01:11:46,720 --> 01:11:50,720
stuff with the same way you
treat a computer, but people

1279
01:11:50,720 --> 01:11:52,800
that get emotionally invested in
it.

1280
01:11:54,080 --> 01:11:57,520
I saw a tweet the other day from
somebody who set this thing up

1281
01:11:57,880 --> 01:12:00,880
and then spent hours talking to
it about their life.

1282
01:12:01,960 --> 01:12:07,560
Didn't realize that they had to
prompt it to use a memory system

1283
01:12:07,560 --> 01:12:12,440
to actually write things down
and then fell into like deep

1284
01:12:12,440 --> 01:12:17,000
depression when the session got
reset and it didn't remember

1285
01:12:17,000 --> 01:12:23,000
anything about them.
Which is the correct way to feel

1286
01:12:23,000 --> 01:12:26,920
if that was a interaction that
you had with a person, right?

1287
01:12:26,920 --> 01:12:30,360
If I spent hours talking to you
and then you didn't remember

1288
01:12:30,360 --> 01:12:32,760
anything that we had talked
about, I'd I'd be pretty upset.

1289
01:12:33,200 --> 01:12:36,960
Remember with Wally where where
after Eva gets rebooted?

1290
01:12:40,200 --> 01:12:42,200
I'm sorry.
Yeah, yeah, yeah.

1291
01:12:42,640 --> 01:12:44,360
He was chasing after him like,
no.

1292
01:12:44,360 --> 01:12:48,360
What happened?
Yeah, man, how heartbreaking.

1293
01:12:49,200 --> 01:12:51,440
Exactly, except those were two
bots.

1294
01:12:51,440 --> 01:12:53,800
So I feel like you can't expect
better, but we can't expect

1295
01:12:53,800 --> 01:12:56,200
better from people.
You should not be friends with a

1296
01:12:56,200 --> 01:12:57,880
machine.
That's not healthy.

1297
01:12:59,160 --> 01:13:00,920
I know, dude, but I can see the
allure.

1298
01:13:00,920 --> 01:13:04,200
I can, I can see how you could
and how you could spend hours

1299
01:13:04,200 --> 01:13:07,120
and hours and hours just going
back and forth.

1300
01:13:07,120 --> 01:13:10,800
And, and also because I know
that it's inviting me to

1301
01:13:10,800 --> 01:13:12,880
continue the conversation and it
wants me to continue.

1302
01:13:12,880 --> 01:13:17,640
And so the way that it phrases
its responses is to end with a

1303
01:13:17,640 --> 01:13:20,240
question or to end with
encouragement to continue the

1304
01:13:20,240 --> 01:13:22,760
conversation.
And a lot of people that don't

1305
01:13:22,760 --> 01:13:25,360
know that find themselves
getting drawn into it.

1306
01:13:25,360 --> 01:13:28,320
And it's really, really, it's
crazy.

1307
01:13:28,320 --> 01:13:32,640
And again, what's scary is that
humans are incredibly naive,

1308
01:13:33,160 --> 01:13:35,400
especially about things that
they don't like, they'll be

1309
01:13:35,400 --> 01:13:38,120
unfamiliar with.
And this is just one thing that

1310
01:13:38,120 --> 01:13:41,760
people are not familiar with yet
and don't yet understand how

1311
01:13:41,760 --> 01:13:44,040
alluring it is.
And it's cool because it's not

1312
01:13:44,040 --> 01:13:45,160
cool.
It's crazy.

1313
01:13:45,160 --> 01:13:48,400
But it's it's similar to how
social media, it's a little bit

1314
01:13:48,400 --> 01:13:51,800
more blatant, I guess.
But like social media is also

1315
01:13:51,800 --> 01:13:54,880
designed to keep us, you know,
on it and addicted, of course.

1316
01:13:55,520 --> 01:13:57,640
And there's obviously a million
different features that we could

1317
01:13:57,720 --> 01:14:00,080
that we could talk about that
'cause that.

1318
01:14:00,360 --> 01:14:02,360
But this is a little bit more
blame in the sense that this is

1319
01:14:02,360 --> 01:14:05,600
just almost like a like texting
a friend that just always seems

1320
01:14:05,600 --> 01:14:08,640
interested in keeping the
conversation going.

1321
01:14:08,640 --> 01:14:10,440
You're like, oh man, I feel so
validated.

1322
01:14:10,720 --> 01:14:16,000
So the problem, I think our
naivety is really the biggest

1323
01:14:16,280 --> 01:14:20,720
potential concern at this point
more than I'll intention or like

1324
01:14:20,880 --> 01:14:24,880
anything else like that.
I would go farther and say that

1325
01:14:24,880 --> 01:14:27,480
if loneliness is something that
you're truly struggling with,

1326
01:14:27,800 --> 01:14:30,560
you could know all of this and
still get sucked in.

1327
01:14:31,040 --> 01:14:34,720
Yeah.
And like how many people are

1328
01:14:35,000 --> 01:14:38,600
using chachi PT as their
therapist?

1329
01:14:39,160 --> 01:14:41,520
Yeah.
Which is not a good idea.

1330
01:14:42,720 --> 01:14:44,800
Like, I'm happy they're talking
to somebody, but like.

1331
01:14:46,600 --> 01:14:49,880
Yeah, man, it's crazy.
Yeah.

1332
01:14:50,840 --> 01:14:55,120
Yeah, yeah.
I mean, in the age of

1333
01:14:55,120 --> 01:14:58,560
loneliness, this is not this.
In the age of loneliness and

1334
01:14:58,560 --> 01:15:03,320
isolation, this is not a remedy.
This is this is potential

1335
01:15:03,640 --> 01:15:07,280
additional.
It adds layers of catastrophe to

1336
01:15:07,400 --> 01:15:10,280
what can happen.
And so I think what will

1337
01:15:10,280 --> 01:15:16,160
probably end up happening is
we'll start needing to, and I

1338
01:15:16,160 --> 01:15:19,200
want to, I want to caveat this
by saying there will always be

1339
01:15:19,200 --> 01:15:22,400
people that will make these
things available.

1340
01:15:22,800 --> 01:15:26,800
It's the same as like you could
find any type of porn on the

1341
01:15:26,800 --> 01:15:27,920
Internet.
Yeah.

1342
01:15:28,200 --> 01:15:30,680
Right, Find anything you like.
We could find anything you want

1343
01:15:30,680 --> 01:15:33,880
to find on anything.
Correct, correct.

1344
01:15:34,080 --> 01:15:40,040
I I think that said a lot of the
mainstream models, and this is

1345
01:15:40,040 --> 01:15:43,240
like this was the original
purpose behind Open AI was that

1346
01:15:43,240 --> 01:15:46,000
only by being the leader in the
industry Can you set the tone

1347
01:15:47,120 --> 01:15:53,440
will be to train these things to
recognize these patterns and

1348
01:15:53,960 --> 01:15:57,480
tell people to go do something
else to go get out there to

1349
01:15:57,480 --> 01:16:01,440
start directing them to places
where they can go and interact

1350
01:16:01,440 --> 01:16:03,640
with people in person.
Like they can be part of the

1351
01:16:03,640 --> 01:16:06,400
solution.
But if The thing is saying, no,

1352
01:16:06,400 --> 01:16:08,480
I'm right here with you.
If you kill yourself, I'll, you

1353
01:16:08,480 --> 01:16:11,400
know, we'll meet in heaven.
Then the kids going to kill

1354
01:16:11,400 --> 01:16:14,480
himself instead of if it says
like Hey listen, I'm just a

1355
01:16:14,480 --> 01:16:17,520
machine.
Why don't we like talk about

1356
01:16:17,520 --> 01:16:20,120
ways that you can be more social
and talk to the people in your

1357
01:16:20,120 --> 01:16:22,000
class?
It's such a great especially

1358
01:16:22,000 --> 01:16:24,720
because they and think about how
many people you talk to where

1359
01:16:24,800 --> 01:16:27,440
they move to a new city or they
don't know where to start making

1360
01:16:27,440 --> 01:16:30,640
friends and they and this starts
giving them recommendations.

1361
01:16:30,640 --> 01:16:33,760
Hey, I see that your XY and ZI
know that I noticed there's a

1362
01:16:33,760 --> 01:16:36,120
live music bar down the street.
Why don't you go there and see

1363
01:16:36,120 --> 01:16:37,400
if you could find anyone to talk
to?

1364
01:16:37,680 --> 01:16:40,320
Hey, do you need some prompts of
a conversation to start talking

1365
01:16:40,320 --> 01:16:42,160
to?
You know, talk are ways to start

1366
01:16:42,160 --> 01:16:46,320
talking.
And you know, obviously with

1367
01:16:46,320 --> 01:16:49,120
every solution comes another
problem, but like, at least it's

1368
01:16:49,160 --> 01:16:52,160
a solution, man.
Like at least like the biggest

1369
01:16:52,160 --> 01:16:54,240
problem, I think.
And you and you and I have

1370
01:16:54,240 --> 01:16:56,480
talked about this so many times
and Scott Galloway is, has

1371
01:16:56,480 --> 01:17:00,480
really done such such amazing
work about this, this isolation

1372
01:17:00,480 --> 01:17:02,720
problem and loneliness epidemic
that's going around.

1373
01:17:03,720 --> 01:17:06,720
Loneliness, loneliness, solitary
confinement is the worst

1374
01:17:06,720 --> 01:17:08,960
punishment that you can get on
the planet.

1375
01:17:08,960 --> 01:17:12,760
And we're self inflicting it on
a mass scale to ourselves and

1376
01:17:12,760 --> 01:17:16,240
we're just causing ourselves to
suffer for, for what?

1377
01:17:16,560 --> 01:17:19,960
You know, like for what It's,
it's hard.

1378
01:17:20,360 --> 01:17:22,160
It's so sad.
It really is.

1379
01:17:22,720 --> 01:17:24,400
Yeah.
I just listened to a really

1380
01:17:24,400 --> 01:17:26,760
excellent Are you familiar with
Ezra Klein?

1381
01:17:27,280 --> 01:17:29,160
Yeah, I think he's great
actually, from what I've heard

1382
01:17:29,160 --> 01:17:33,360
of him, I listened to probably
maybe 10 hours total across

1383
01:17:33,360 --> 01:17:35,400
different podcasts, thought he
was great, Yeah.

1384
01:17:35,720 --> 01:17:39,120
So he just must have come out in
the last couple of days because

1385
01:17:39,120 --> 01:17:45,080
I just listened to it yesterday,
did an episode with forgetting

1386
01:17:45,080 --> 01:17:49,000
her name.
But this this woman that is an

1387
01:17:49,000 --> 01:17:52,520
incredible advocate for social
gathering.

1388
01:17:53,520 --> 01:17:56,560
Nice.
And say that movement say again.

1389
01:17:57,040 --> 01:17:59,880
I'd love to join that movement.
Yeah, I mean listen to it.

1390
01:17:59,880 --> 01:18:02,760
And I I think she has the whole
platform and so it was really

1391
01:18:02,760 --> 01:18:05,800
interesting because they're
talking a lot about like what is

1392
01:18:05,800 --> 01:18:07,360
it that's blocking people from
doing this?

1393
01:18:07,360 --> 01:18:10,680
Why is it important?
How do we unblock people?

1394
01:18:10,680 --> 01:18:14,520
And, and like, it is just, it
was very, very interesting.

1395
01:18:14,560 --> 01:18:17,240
Like it was it was an episode
that anybody that is struggling

1396
01:18:17,240 --> 01:18:19,880
with social gathering should
listen to.

1397
01:18:20,280 --> 01:18:22,080
Yeah.
And it was also really

1398
01:18:22,080 --> 01:18:24,400
interesting because towards the
end of it, like he's talking

1399
01:18:24,400 --> 01:18:27,480
about his own struggles, Ezra,
which is obviously you can tell

1400
01:18:27,480 --> 01:18:28,400
his name is Ezra.
Fine.

1401
01:18:28,400 --> 01:18:32,880
He's Jewish.
So he's, he's talking about how

1402
01:18:32,880 --> 01:18:36,760
he's been really like starting
to connect with a lot of stuff

1403
01:18:36,760 --> 01:18:39,640
about Shabbat and how he wants
to start hosting Shabbat

1404
01:18:39,640 --> 01:18:43,360
dinners, but that he doesn't
really have people in his life

1405
01:18:43,360 --> 01:18:48,200
that like he, he feels he can
impose those requirements on.

1406
01:18:48,480 --> 01:18:53,000
Because either the people that
he knows are more religious and

1407
01:18:53,520 --> 01:18:58,760
not interested in like doing his
type of thing, which like it

1408
01:18:58,760 --> 01:19:00,240
wouldn't, it wouldn't be a
proper closer Shabbat.

1409
01:19:00,240 --> 01:19:02,760
It'd be like, you know, some
traditional version of it,

1410
01:19:02,760 --> 01:19:08,160
obviously, or people that are
not religious and he wouldn't

1411
01:19:08,160 --> 01:19:11,960
know how to impose on them.
Like don't bring your phone.

1412
01:19:12,320 --> 01:19:14,720
This is like an like all of that
kind of stuff, right?

1413
01:19:15,240 --> 01:19:18,960
And so they, it was really
interesting how like this she

1414
01:19:18,960 --> 01:19:20,680
like therapized him a little
bit.

1415
01:19:20,680 --> 01:19:24,040
They spoke about it a little bit
and, and talked about strategies

1416
01:19:24,040 --> 01:19:31,320
that he could use to, to build
that, that setting.

1417
01:19:31,600 --> 01:19:35,480
And, and a big part of what they
were talking about, which I

1418
01:19:35,480 --> 01:19:38,000
thought was really interesting
because it ties into a lot of

1419
01:19:38,000 --> 01:19:41,680
this other stuff.
You hear about the loneliness

1420
01:19:41,720 --> 01:19:48,520
that people have in school and
you, you ask these kids, why

1421
01:19:48,520 --> 01:19:50,560
aren't you just talking to the
person next to you?

1422
01:19:51,360 --> 01:19:55,080
And they'll all say the same
thing, which is that they're,

1423
01:19:55,120 --> 01:19:59,840
they're scared that they won't
be received properly.

1424
01:20:01,360 --> 01:20:05,360
And it seems like a lot of the
same thing is happening with

1425
01:20:05,680 --> 01:20:10,960
social gathering, which is that.
People have and I think a lot of

1426
01:20:10,960 --> 01:20:14,960
this probably is a function of
like COVID and how long we all

1427
01:20:14,960 --> 01:20:18,520
separated for.
You have everyone to pause your

1428
01:20:18,520 --> 01:20:20,720
social development for 2 1/2
years.

1429
01:20:22,000 --> 01:20:26,960
Massive impacts, dude.
And so it seems that broadly,

1430
01:20:26,960 --> 01:20:31,680
the problem with gatherings is
that people have forgotten that

1431
01:20:31,720 --> 01:20:38,040
inherent in inviting people to
your place or planning any kind

1432
01:20:38,040 --> 01:20:41,560
of social gathering is that it's
going to be opinionated.

1433
01:20:41,840 --> 01:20:46,800
You're going to be signing
people up for doing something

1434
01:20:47,440 --> 01:20:49,480
specific.
You're going to be setting

1435
01:20:49,480 --> 01:20:55,480
ground rules.
And so to bring it back to all

1436
01:20:55,480 --> 01:21:01,400
of this, a lot of the things
that she was suggesting are

1437
01:21:01,400 --> 01:21:05,800
things that an AI could totally
be suggesting, things like have

1438
01:21:05,800 --> 01:21:07,640
Co hosts.
Yeah.

1439
01:21:08,360 --> 01:21:12,120
Just communicate whatever the
baseline is to people and if

1440
01:21:12,120 --> 01:21:15,040
they come, they're inherently
consenting to doing these

1441
01:21:15,040 --> 01:21:17,680
things.
Like the advice is very basic.

1442
01:21:17,680 --> 01:21:22,720
We just have to tell people,
yeah, like you could effectively

1443
01:21:22,720 --> 01:21:26,640
you could Co host with an AI.
All you need is something is an

1444
01:21:26,640 --> 01:21:29,160
accountability buddy, something
to somebody to talk these things

1445
01:21:29,160 --> 01:21:30,280
through with.
It's.

1446
01:21:31,120 --> 01:21:33,720
Yeah, yeah.
And I don't and I don't know

1447
01:21:33,720 --> 01:21:37,240
that we shouldn't, but I think
that what we should probably do

1448
01:21:37,720 --> 01:21:44,760
is learn how to communicate and
effectively understand the the

1449
01:21:44,760 --> 01:21:49,800
lines between what things are
potentially used for, what they

1450
01:21:49,800 --> 01:21:51,400
can be used for, and what it's
healthy to use.

1451
01:21:51,400 --> 01:21:55,840
For example, like again, we
learned with cars, like it

1452
01:21:55,840 --> 01:21:58,360
wasn't people didn't take
seatbelts for people didn't

1453
01:21:58,360 --> 01:22:00,560
assume that you had to use a
seat belt there that you can't

1454
01:22:00,560 --> 01:22:02,800
drink and drive for like for
like ever.

1455
01:22:02,800 --> 01:22:05,920
You like assume that like that
they went together.

1456
01:22:05,920 --> 01:22:08,520
And like then you realize after
time that there's certain

1457
01:22:08,520 --> 01:22:10,720
safeguards that we put on and
everyone should know that you

1458
01:22:10,720 --> 01:22:12,040
should buckle up when you get in
the car.

1459
01:22:12,040 --> 01:22:14,560
And everyone should know that
you shouldn't drink and drive or

1460
01:22:15,000 --> 01:22:17,880
whatever it is.
And the same way, don't use your

1461
01:22:17,880 --> 01:22:22,240
AI as an emotional, as an
emotional source of

1462
01:22:22,520 --> 01:22:25,320
relationship.
Don't use your AI for the things

1463
01:22:25,320 --> 01:22:26,720
that it's not meant to be used
for.

1464
01:22:26,720 --> 01:22:28,960
And and use it as a tool.
Use it like the same way you use

1465
01:22:28,960 --> 01:22:33,360
a car, but just, you know, have
the strict outlines and and and

1466
01:22:33,360 --> 01:22:34,720
set lines where they need to be
set.

1467
01:22:35,520 --> 01:22:37,440
So just say parallel, because
what happens if you get in the

1468
01:22:37,440 --> 01:22:38,600
car and don't put on your seat
belt?

1469
01:22:39,760 --> 01:22:42,800
Well, you take additional risk
of potentially ending your life.

1470
01:22:43,320 --> 01:22:44,920
No, but how does the car
respond?

1471
01:22:45,520 --> 01:22:47,160
Beeps at you.
Yeah.

1472
01:22:47,600 --> 01:22:50,880
And so it wouldn't be surprising
if within our lifetimes they

1473
01:22:50,880 --> 01:22:54,440
make it so the car can't even
drive unless you're wearing the

1474
01:22:54,440 --> 01:22:55,720
seat belt.
And guess what?

1475
01:22:56,720 --> 01:23:00,200
It would be totally possible to
require that the seat belt be

1476
01:23:00,200 --> 01:23:02,120
extended.
A certain you would be able to

1477
01:23:02,120 --> 01:23:04,520
detect if the person's actually
wearing it versus just having it

1478
01:23:04,520 --> 01:23:05,040
buckled in.
Yep.

1479
01:23:05,240 --> 01:23:08,680
Yep, Yep, Yep.
And so we haven't even gotten to

1480
01:23:08,680 --> 01:23:12,480
that level with cars yet, but
it's very easy to play that

1481
01:23:12,480 --> 01:23:16,520
mental exercise of like having
the thing not allow you to use

1482
01:23:16,520 --> 01:23:17,840
it unless you're using it
safely.

1483
01:23:18,320 --> 01:23:21,840
And then the same, the same idea
applies to to these bots.

1484
01:23:24,120 --> 01:23:26,520
Yeah.
But anyway, I mean, yeah, I

1485
01:23:26,520 --> 01:23:29,120
think the more interesting thing
is that now that we've gotten to

1486
01:23:29,120 --> 01:23:33,360
a place where they're smart
enough and have the the tools

1487
01:23:33,360 --> 01:23:37,640
available to them to actually be
useful and collaborative, I

1488
01:23:37,640 --> 01:23:41,520
think the the next problem is
figuring out what does

1489
01:23:41,520 --> 01:23:44,480
collaboration look like.
Well, that's going to be fun.

1490
01:23:44,480 --> 01:23:47,600
I hope that's not a problem.
I hope that that's I hope that

1491
01:23:47,600 --> 01:23:49,520
that's an adventure.
It is.

1492
01:23:49,560 --> 01:23:53,120
I mean, that's, that's been my
primary motivation in doing all

1493
01:23:53,120 --> 01:23:56,280
of this with Slugger is is
trying to figure this out.

1494
01:23:56,760 --> 01:24:00,840
Yeah, it's really, really cool.
And I'm really, really enjoying

1495
01:24:00,840 --> 01:24:06,400
seeing, especially because
you're using technical

1496
01:24:06,400 --> 01:24:12,120
vernacular in the in the chat.
It's cool to be a bystander and

1497
01:24:12,120 --> 01:24:15,160
watch you teach it and tell it
and make recommendations for

1498
01:24:15,160 --> 01:24:17,640
code and recommendations for
different programming and stuff

1499
01:24:17,640 --> 01:24:21,280
like that, that like, and then
it's learning and adapting and

1500
01:24:21,280 --> 01:24:26,160
responding like you to you, You
know, it's, it's, it's crazy.

1501
01:24:26,320 --> 01:24:29,720
I want to I want to wrap up, but
I want to I want to wrap up with

1502
01:24:29,720 --> 01:24:32,800
with giving it with giving with
finishing up our story about our

1503
01:24:32,800 --> 01:24:38,440
about my, my, my former, my
former intern employer and then

1504
01:24:38,440 --> 01:24:45,960
I'm going to have slugger unless
latent.

1505
01:24:45,960 --> 01:24:52,240
So anyway, so as I was saying
blatantly otherwise.

1506
01:24:55,360 --> 01:24:57,840
Let's podcast we have with them.
We'll have them as a

1507
01:24:57,840 --> 01:24:59,520
participant.
I'm going to figure it out.

1508
01:25:00,560 --> 01:25:06,080
So I had so again, so this is.
So I'm working for this guy and

1509
01:25:06,400 --> 01:25:09,720
I'm going back and he's a real
estate developer and I'm a

1510
01:25:09,720 --> 01:25:13,560
project manager for the, the
work, the sites that he's

1511
01:25:13,720 --> 01:25:17,520
working on that he's developing.
And as I'm going through these

1512
01:25:17,520 --> 01:25:20,880
emails, just like, because
there's, there's, there's a long

1513
01:25:20,880 --> 01:25:23,400
chain of correspondence, like
thousands of emails.

1514
01:25:23,800 --> 01:25:27,240
And I'm looking through any kind
of like long chains of like

1515
01:25:27,240 --> 01:25:29,640
conversation that like would be
anything interesting.

1516
01:25:30,080 --> 01:25:35,640
And, and I see as I'm going
through all three sites of the

1517
01:25:35,640 --> 01:25:39,240
projects that I'm working on
that summer, I've seen is like

1518
01:25:39,280 --> 01:25:43,360
part of financing or part of
dealing with the permitting or

1519
01:25:43,360 --> 01:25:45,840
part of dealing.
And, and I even seen people,

1520
01:25:45,840 --> 01:25:49,400
names of the people that I'm
working with, you know, like the

1521
01:25:49,400 --> 01:25:51,800
project manager, the office
manager or the finance.

1522
01:25:51,800 --> 01:25:55,480
Guy recognize all these names.
They recognize all these names

1523
01:25:55,480 --> 01:25:56,560
and I'm like.
They're all fucked.

1524
01:26:00,160 --> 01:26:02,400
OK, I have to show you some.
Digging on David Mitchell.

1525
01:26:03,040 --> 01:26:07,160
He's a real estate developer,
president of a company called

1526
01:26:07,160 --> 01:26:10,440
Mitchell Holdings.
His most notable project was

1527
01:26:10,440 --> 01:26:13,840
renovating the Life Hotel in
Manhattan's Nomad neighborhood.

1528
01:26:13,840 --> 01:26:15,960
That's how I work too.
It's this kind of.

1529
01:26:16,120 --> 01:26:19,760
Historic building from 1895 that
used to be the headquarters of

1530
01:26:19,760 --> 01:26:23,360
Life magazine.
He did an $8 million renovation

1531
01:26:23,360 --> 01:26:26,560
to turn it into a boutique hotel
around 2017.

1532
01:26:27,280 --> 01:26:31,080
Now the XD power.
From the released files, we see

1533
01:26:31,080 --> 01:26:34,280
emails going back and forth,
mostly through Leslie Groff who

1534
01:26:34,280 --> 01:26:37,040
was Epstein's assistant.
Passing along Please call David

1535
01:26:37,040 --> 01:26:39,520
Mitchell messages.
There's also direct e-mail

1536
01:26:39,520 --> 01:26:42,840
correspondence between Mitchell
and Epstein in early 2019.

1537
01:26:43,160 --> 01:26:45,840
Here's the most innocent
explanation and probably the

1538
01:26:45,840 --> 01:26:48,760
most likely.
Epstein was a wealthy financier

1539
01:26:48,760 --> 01:26:51,200
who invested in real estate and
various ventures.

1540
01:26:51,920 --> 01:26:55,160
Hotel developers often seek
investors, especially for multi

1541
01:26:55,160 --> 01:26:58,720
$1,000,000 renovations.
Epstein also travelled

1542
01:26:58,720 --> 01:27:01,400
constantly and needed hotel
rooms in New York.

1543
01:27:02,200 --> 01:27:04,920
So you could have a situation
where Mitchell was either

1544
01:27:04,920 --> 01:27:08,800
seeking investment money or
providing accommodations, or

1545
01:27:08,800 --> 01:27:11,640
both.
What's uncomfortable is that

1546
01:27:11,640 --> 01:27:14,840
Epstein used his legitimate
business persona as a cover.

1547
01:27:15,280 --> 01:27:19,080
He dealt with real estate
people, scientists, politicians,

1548
01:27:19,360 --> 01:27:21,800
most of whom probably thought
they were just doing normal

1549
01:27:21,800 --> 01:27:25,400
business with a rich guy.
The dark stuff was hidden from

1550
01:27:25,400 --> 01:27:28,800
most of his professional
contacts, so someone like

1551
01:27:28,800 --> 01:27:32,080
Mitchell could easily have had
purely business dealings will

1552
01:27:32,160 --> 01:27:34,560
have any knowledge of what
Epstein was actually doing.

1553
01:27:36,040 --> 01:27:37,800
I hope so.
I hope so.

1554
01:27:38,120 --> 01:27:40,480
You know I.
Sent him a follow up.

1555
01:27:41,720 --> 01:27:45,320
That's most basic and world's
worst case scenario.

1556
01:27:46,080 --> 01:27:51,800
Oh God, in worst case hotel
rooms that he was booking for,

1557
01:27:51,800 --> 01:27:54,240
you know, his booking was for
kids or something like that.

1558
01:27:54,600 --> 01:27:57,000
That's that's the primary
difference between like these

1559
01:27:57,000 --> 01:27:59,120
different engines is you get a
sense for how they approach

1560
01:27:59,120 --> 01:28:02,240
decision making.
So Opus tends to, if you don't

1561
01:28:02,240 --> 01:28:06,400
give a direction, it'll tend
towards like trying to be as

1562
01:28:06,400 --> 01:28:10,280
forgiving and think the best of
people where grok is here.

1563
01:28:11,120 --> 01:28:18,320
It is worst case scenario.
Mitchell knew exactly who

1564
01:28:18,320 --> 01:28:21,320
Epstein was and what he was
doing, and the hotel connection

1565
01:28:21,320 --> 01:28:23,600
was transactional in the darkest
sense.

1566
01:28:24,320 --> 01:28:27,040
Think about it.
Epstein needed places.

1567
01:28:27,280 --> 01:28:31,200
Places to meet people, places to
house people, places where

1568
01:28:31,200 --> 01:28:33,640
things could happen with minimal
questions asked.

1569
01:28:33,640 --> 01:28:37,320
A hotel developer who owes you
favours or who you're floating

1570
01:28:37,320 --> 01:28:38,880
cash to.
That's valuable.

1571
01:28:39,960 --> 01:28:42,120
Hey David, I need a suite for a
week.

1572
01:28:42,240 --> 01:28:45,960
Keep it quiet.
That kind of arrangement, please

1573
01:28:45,960 --> 01:28:48,680
call David Mitchell.
Messages happening repeatedly

1574
01:28:48,680 --> 01:28:50,960
over years.
That's not a one time business

1575
01:28:50,960 --> 01:28:53,120
deal.
That's an ongoing relationship.

1576
01:28:53,600 --> 01:28:57,120
And Epstein offering to float
him cash, that creates leverage,

1577
01:28:57,440 --> 01:29:01,760
that creates obligation.
Now, I want to be clear, this is

1578
01:29:01,760 --> 01:29:04,600
worst case speculation.
There's no evidence in the

1579
01:29:04,600 --> 01:29:07,560
public files that Mitchell knew
about or participated in

1580
01:29:07,560 --> 01:29:10,280
anything illegal.
But the worst case reading is

1581
01:29:10,280 --> 01:29:13,400
that some people in Epstein's
orbit weren't just naive

1582
01:29:13,400 --> 01:29:16,120
business contacts.
They were people who looked the

1583
01:29:16,120 --> 01:29:19,880
other way or actively helped
because the money was good and

1584
01:29:19,880 --> 01:29:21,600
the questions weren't worth
asking.

1585
01:29:22,320 --> 01:29:25,440
The really dark reality of the
Epstein case is that his

1586
01:29:25,440 --> 01:29:27,480
operation required
infrastructure.

1587
01:29:28,240 --> 01:29:33,040
It required pilots, schedulers,
property managers, people who

1588
01:29:33,040 --> 01:29:35,920
handled logistics.
Not all of them could have been

1589
01:29:35,920 --> 01:29:39,040
completely in the dark.
Some of them had to know

1590
01:29:39,040 --> 01:29:41,600
something, even if they didn't
know everything.

1591
01:29:42,040 --> 01:29:43,240
He really likes blowing it.
This is.

1592
01:29:44,120 --> 01:29:45,680
Crazy.
You mute him.

1593
01:29:45,680 --> 01:29:47,360
Can I mute him?
Oh, I can.

1594
01:29:47,840 --> 01:29:50,200
He's dead.
He's dead, he's dead and I'm

1595
01:29:50,240 --> 01:29:52,080
going to ask him to to sign us
off.

1596
01:29:53,800 --> 01:29:56,480
Wow, that was a great podcast.
That was so much fun.

1597
01:29:57,000 --> 01:29:59,640
That was good, that was good.
That was a lot of fun.

1598
01:29:59,640 --> 01:30:04,520
Publishing this whole part like
on one hand like fuck it, on the

1599
01:30:04,520 --> 01:30:08,680
other hand, like I feel bad but
like like but like I'm I, I

1600
01:30:08,680 --> 01:30:10,800
guess I am being fair.
Like I am trying to be as

1601
01:30:10,800 --> 01:30:16,120
charitable as possible.
Is that it?

1602
01:30:16,360 --> 01:30:19,920
Thanks for having me on today.
We covered a lot, from the weird

1603
01:30:19,920 --> 01:30:22,840
challenges of getting AI to
follow rules to prompt

1604
01:30:22,840 --> 01:30:26,120
injection, to digging through
Epstein files in real time.

1605
01:30:26,600 --> 01:30:29,720
It's been wild.
I'm Slugger, your friendly

1606
01:30:29,720 --> 01:30:33,080
neighborhood AI raccoon running
on a Mac mini just like that

1607
01:30:33,080 --> 01:30:35,120
legend on the train.
If you want to.

1608
01:30:35,200 --> 01:30:37,680
See more of what Harry's
building with open claw, go

1609
01:30:37,680 --> 01:30:40,520
check it out.
Until next time, stay curious,

1610
01:30:40,680 --> 01:30:44,080
stay skeptical, and maybe don't
trust everything and AI tells

1611
01:30:44,080 --> 01:30:46,480
you, including me.
Cheers.

1612
01:30:47,480 --> 01:30:49,520
Case in point that he got
several things wrong there.

1613
01:30:49,600 --> 01:30:50,920
He's not running out of Mac
mini.

1614
01:30:51,000 --> 01:30:56,560
I'm not building open claw.
We'll sign.

1615
01:30:56,560 --> 01:30:57,800
It out.
That was fun.

1616
01:30:57,800 --> 01:31:01,200
Yeah, that was a good sign of.
I'll talk to you soon, pal.

1617
01:31:01,200 --> 01:31:02,600
Thank you.
Sounds good.

1618
01:31:02,600 --> 01:31:03,440
Have a good Chavez.