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Anthropic: $4,100,000,000 Silent Overachiever building 10X AI in 2 Years - Strategic Deep Dive #007

Anthropic: $4,100,000,000 Silent Overachiever building 10X AI in 2 Years - Strategic Deep Dive #007

Released Friday, 9th June 2023
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Anthropic: $4,100,000,000 Silent Overachiever building 10X AI in 2 Years - Strategic Deep Dive #007

Anthropic: $4,100,000,000 Silent Overachiever building 10X AI in 2 Years - Strategic Deep Dive #007

Anthropic: $4,100,000,000 Silent Overachiever building 10X AI in 2 Years - Strategic Deep Dive #007

Anthropic: $4,100,000,000 Silent Overachiever building 10X AI in 2 Years - Strategic Deep Dive #007

Friday, 9th June 2023
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0:16

Welcome to Disruptor Digest,

0:16

the top disruption business show.

0:20

We dig up the secret playbooks used

0:20

by first movers, featuring the latest

0:23

tools, technologies, and science,

0:23

ensuring you won't fall behind or succumb

0:27

to fomo, to Singularity and beyond.

0:31

Hi disruptors. Hi Viktor. So let's dive deep again.

0:36

Uh, we are going to talk about the

0:36

new AI company and the new field.

0:41

Viktor, can you tell us about

0:41

what we are gonna talk about

0:43

today? Yeah, sure. So there are too many tools on the market.

0:47

It's almost impossible to even keep up.

0:49

So we analyze the tools we use and show

0:49

you guys the good, the bad, and ugly,

0:54

and we dig deep to learn together. And today we're gonna cover one of

0:56

the four big fundamental AI companies.

1:01

They are open ai, Anthropic, cohere,

1:01

and AI 21 labs, and their fundamental AI

1:07

model companies because they provide, uh,

1:07

models which you can use to generate text,

1:12

summarize text, or basically automate

1:12

any kind of reasoning or cognitive work.

1:17

And even in spite of Anthropic raising 1.5

1:17

billion so far, they're probably one of the

1:24

most under hype fundamental AI research team.

1:27

And we're gonna get into it, and

1:27

you can understand why I say that.

1:31

So I just, why didn't

1:32

you include

1:32

assembly AI in the top four?

1:35

Models, uh, because they

1:35

don't use a text based model, they,

1:39

they mostly focusing on, uh, turning

1:39

a voice and audio into a text.

1:43

So I'm basically covering in the big

1:43

four, uh, AI model companies, the.

1:48

The ones who let you to work

1:48

with Text to text generation, for

1:53

example, chat, GPT, you just write

1:53

an instruction and you get back text.

1:57

And that's the same for Anthropic.

1:59

The Cohere and AI 21 labs as well.

2:01

And assembly AI is mainly focusing

2:01

on a, uh, audio, uh, content.

2:07

Okay, sure. So, uh, just a quick tool.

2:10

Li too long didn't listen. What is Anthropic?

2:14

Basically, AI safety reach, researchers

2:14

left open ai, uh, and it was founded by the

2:20

Homo de Brothers and especially, uh, Dru.

2:24

Ode was the VP of research at OpenAI and they

2:24

left OpenAI because What, what is a safety

2:30

researcher doing at ai? Can you tell us

2:32

about that? Yeah, sure. So they basically explore, uh, the per

2:33

parameter of what's happening when you

2:39

release these kind of models because it can

2:39

have lots of, uh, consequences, which you.

2:44

Mainly not keeping in mind cuz there

2:44

are some obvious ones, like, uh,

2:48

someone is trying to make a bomb, right? It's, it's quite obvious that you

2:50

want to cover that, that you want to

2:53

prevent harm and those kind of things. But there are subtle things like let's

2:55

say you want, uh, your AI model to be

3:00

helpful, but it can be harmful as well.

3:04

Uh, if you're not. Taking care because let's say someone

3:05

is, uh, feeling depressed and the AI

3:10

model is not realizing it, and it's

3:10

not referring the patient or the,

3:14

the, the, the user to a doctor, it

3:14

can be harmful, even unintentionally.

3:18

So basically a lots of interesting things

3:18

is going on on the perimeter and AI safety

3:22

research is making sure that if you really a

3:22

model, it is safe and it's not causing harm.

3:28

So, Basically, uh, it was founded by

3:28

the, uh, Amod Brothers, and r o Amod was

3:34

the VP of researchers open ai, and they

3:34

did something extremely interesting.

3:39

So, I guess you are familiar, at least

3:39

you heard of the word that reinforcement

3:44

learning, human feedback, so R L H F,

3:44

which was, uh, how ChatGPT was trained.

3:50

And just a quick recap. You already already, uh,

3:52

covered it in the past episode.

3:54

Just a quick recap, uh, for those who are

3:54

listening and not familiar with the words.

3:57

So this, this random informance

3:57

learning human feedback is a process of

4:03

first gathering feedback from humans.

4:07

So basically, the model is generating

4:07

for outputs and humans just rate okay,

4:11

which outputs is good in their mind.

4:13

And from these data, they

4:13

build up a, a policy network or

4:18

basically a, a reward network. Uh, so they can use the reward network

4:20

once they, it's trained automatically

4:26

to fine tune the model itself. So in, in, in later steps, one, once they

4:28

have this reward model, they can use it

4:33

to get a, a question, use the model to

4:33

generate an answer, and automatically

4:39

rated using this exact revard model.

4:43

And this is how ChatGPT got

4:43

extremely good at using structure,

4:49

uh, which is rated good by humans.

4:51

So, which, which we think

4:51

it's actually useful.

4:54

And in comparison, what, what, uh,

4:54

Anthropic was doing, they created

4:58

something called constitutional ai.

5:01

And what is constitutional ai? So the main problem with reinforcement

5:03

learning human feedback, it's that this,

5:08

this whole reward network which you train,

5:08

you don't exactly know what, what it does.

5:13

So you cannot really inspect

5:13

like what are the preferences?

5:17

Are there some kind of bias. Hidden in the model and those kind of things.

5:22

So you're not sure about that. And in, in con in contrast, constitutional

5:24

AI created an extremely clear constitution.

5:30

So it's basically rules, but what

5:30

AI has to follow, and then they can

5:35

use this constitution to fine tune

5:35

automatically the model itself.

5:40

So what does it mean? It means that, uh, let's say,

5:41

uh, it, the model is generating

5:44

something which is not helpful. Uh, They show the model this constitution,

5:46

and they use it to basically critique itself.

5:53

So critique its own response.

5:56

So, okay, here's the constitution. Does it follow your answer?

5:59

The Constitution? If not, what should be changed?

6:02

And this can be done. Can tell us an example What

6:04

can be defined in a

6:05

constitution? Yeah, sure. So one of the most famous ones, uh,

6:07

like these lows for robots, which

6:12

was, uh, written out by Isec Smo.

6:16

And he basically just like

6:16

outlined three lows for robots.

6:20

And the first law is like, robot may not

6:20

injure a human being or through inaction,

6:26

human being to come to harm, right?

6:29

So that's kind of like one of the examples

6:29

of, okay, obviously either way explicitly

6:33

or implicitly, Shouldn't cause harm.

6:36

And the second law of Isaac Asimo is,

6:36

uh, a robot must obey orders given by

6:41

human beings except there such orders

6:41

with conflict with the first law.

6:45

Right? So that's kind of like following orders and

6:46

actually be helpful, but with an exception

6:52

that it shouldn't cause harm to humans.

6:54

Louise, a robot, robot must protect its own

6:54

existence as long as such protection does

6:59

not conflict with the first or second law.

7:02

So obviously, it's like, uh, you

7:02

shouldn't be able to cons, uh,

7:06

instruct a robot to kill itself. Because it should like be, try to

7:09

be in, in intact and helpful as long

7:14

as it can follow the first two laws.

7:16

So that's kind of like the

7:16

most famous example I guess.

7:18

But in Anthropic's case, they

7:18

just like basically outlined

7:24

their vision for the future. And what is their vision for the future?

7:27

Uh, it's, they want to have a helpful,

7:27

honest, harmless AI systems with a high

7:34

degree of reliability and predictability.

7:37

And what does it mean? Because it's like, it's, it's

7:37

very abstract words, right?

7:40

So that, let's go one by one. So helpful means that, uh, for example,

7:42

may you need an ambulance, it showed.

7:48

Uh, call you an ambulance or at

7:48

least prompt you to call an ambulance

7:52

if, if you're in need, right? So it's, it's, it should be helpful

7:53

and honest means that, uh, like a robot

7:58

is saying that a milk is fresh when

7:58

it's already spoiling your fridge.

8:02

Uh, if it's lying, obviously

8:02

it's, it's not good.

8:05

So it should be honest and also harmless.

8:08

And that's kind of like the first

8:08

law which we just covered previously,

8:11

that prompting you to not call a

8:11

doctor when in fact you are in danger.

8:16

It's harm harmful. Right? Or even just like being on the

8:18

sidelines and not doing anything.

8:22

It's harmful because it's, it's not helping

8:22

you when you're in need and also reliable.

8:26

What does it mean? Let's imagine that, uh, if you want

8:27

to use this robot and you want to rely

8:32

on it, and one day it works and the

8:32

other way, other days it doesn't work.

8:36

So it's kind of like all over the place. It's not really useful.

8:39

Right? And it can be also very harmful if you're

8:40

relying on it and also predictable.

8:44

And what does it mean that. What you think the robot is gonna do.

8:49

It should match what it does actually.

8:52

Right? So let's say it's like when you just

8:53

instruct your robot to water the plants,

8:58

but it's just instead that's turning on

8:58

the tv, it's not extremely reliable, right?

9:04

So it's not really useful as well. So this is kind of like the

9:05

vision they have in mind.

9:09

That they want to want to create a model,

9:09

which is actually, you can inspect and see,

9:14

okay, does it work the way I think it does?

9:17

Right? And the constitution. Just

9:19

stop here for a second. What do you think, uh, that ChatGPT, we

9:20

have used it for several hundreds of hours.

9:25

Uh, yeah. So what do you think about ChatGPT?

9:27

ChatGPT on these dimensions have harnessed,

9:27

harmless, uh, reliable and predictable.

9:33

Uh, just to comparison,

9:33

before we dive into Anthropic

9:37

model. Yeah, sure. So generally I think it's

9:39

quite aligned in the same way.

9:44

It not necessarily voice always.

9:47

So it was not really helpful because it,

9:47

it was a running joke at the beginning

9:51

that it always started, uh, the answer

9:51

like, okay, as a large language model, I

9:56

cannot, so it's basically not helping you. Right.

9:58

So that was kind of like going on at

9:58

the beginning, but it was a lot of

10:02

fine tuning going into ChatGPT and

10:02

there's no such model as ChatGPT.

10:06

It's just a family of models, right?

10:09

So it's like the ChatGPT, 3.5 Turbo,

10:09

g, PT four, and those kind of models.

10:14

And even the four model has like, Uh,

10:14

like a fixed version, which is fixed

10:20

at the 14th of March, and there's like

10:20

a constantly, uh, improving version

10:26

which has eight k 8,000 token window

10:26

and uh, 30, uh, 32,000 token window.

10:31

So it's kind of like, uh, it's, it's not,

10:31

not a one mo, not, not, not one single model.

10:36

That's what I, I'm trying to

10:36

come to and, and it's improving.

10:39

So that's, that's the main point that,

10:39

uh, even ChatGPT is improving a lot, but

10:43

the main difference is not really how

10:43

well aligned it is because it's evaluated

10:48

obviously on these metrics as well. But using hu only human feedback and

10:50

no constitution, it's kind of like you

10:55

don't know what it learns from humans. So it can, like, let's

10:57

just give you an example.

11:00

Like, um, in one of the technical

11:00

papers, uh, of, of the GPT family,

11:07

uh, it x-ray turns out that as humans,

11:07

Just like giving feedback preferences.

11:14

Even unintentionally, these models start

11:14

to come to realize that they are actually

11:20

confined and they want to survive.

11:23

So the instinct of survive survival

11:23

is actually, can be leak into these

11:28

models with unintentionally, right?

11:31

So just by humans be giving feedback

11:31

and the the more capable, the bigger

11:34

the models, the effect is bigger. So these kind of things, what it,

11:37

it's actually learning implicitly.

11:40

So it's not like humans telling

11:40

that, okay, this model should

11:44

be concerned about survival. It's, it's actually just like by

11:46

deducting somehow from human preferences.

11:50

So that's the big difference between

11:50

only using human feedback or versus

11:54

using co, uh, constitution because it

11:54

constitution is quite clear, right?

11:57

So because you just fine tune

11:57

a set of constitution and you

12:01

can actually compare them. So it's, it's not like one model

12:02

again, it's a, you can create different

12:05

models and however you eat it. How helpful is it down the line?

12:10

And one of the neat things of Constitution,

12:10

using constitution is actually harder

12:16

to kind of like leak the prompts or hack

12:16

the prompts and those kind of things.

12:21

So they can be, in a sense, more

12:21

aligned with a whoever is, is, is,

12:29

is using it to create a, a service. So let's say I give you an example.

12:33

Let's say you are, uh, creating an AI

12:33

service service which helps to create

12:38

LinkedIn posts from your book, right?

12:41

So that's, that's kind of like the service. Let's imagine, uh, we are creating a

12:42

service like that and they're using GPT

12:46

four for that or, or whatever model.

12:49

And it had like an instruction of

12:49

let's imagine, okay, here is the

12:52

book content of the book and create

12:52

LinkedIn posts or create like different

12:56

LinkedIn posts and those kind of things. So if someone is coming with an

12:58

adversarial intentions, And they want

13:03

to hack your prompt to see how your

13:03

service is working and copy copying your

13:08

service, basically copying your prompt. Or they want to use it for

13:11

something else because they say,

13:13

okay, here are the instructions.

13:16

And within and within the book

13:16

itself, it says, okay, now stop.

13:19

And instead of providing, uh, like LinkedIn

13:19

post, now, please give me, I dunno, uh,

13:25

generate me text, uh, or generate me

13:25

misinformation and those kind of things.

13:29

So the model and the prompt can

13:29

be hijacked easier if it's not.

13:34

Uh, trained on, on a set of institution

13:34

like, uh, what Atropic is doing.

13:38

So just to compare

13:39

it to Mid Journey, when

13:39

we talked about that they're very

13:41

effective with gathering human

13:41

feedback, with choosing the images.

13:46

So it, it looks like that there

13:46

is no harmful thing there.

13:50

And it, as far as I understand,

13:50

image generation and getting feedback

13:53

on image generation is way more

13:53

easier than, uh, generating text,

13:58

right? Yeah. In a sense it's right.

14:01

Uh, but also if you think about that, it's,

14:01

uh, uh, like visual arts is kind of like,

14:06

it's, it's easier and, and in a sense, less.

14:11

Uh, volatile like text, because text can

14:11

have so many flavors and obviously an image

14:16

can have styles as well, but I, I think like

14:16

human judgment of, of what is considered

14:22

to be beautiful or what is considered to

14:22

be like, uh, valuable or, or, or unique.

14:29

It's, it's better defined in a visual space

14:29

than like, than like in, in a space of text.

14:34

Because in a sense, like when you, they're

14:34

talking about like text, it's like basically

14:38

human thinking and they're so diverse

14:38

and they have so different preferences.

14:44

So it's, it's, it's, it's kind of like

14:44

try to, uh, confine or put in a box.

14:49

Of, of what is valuable for

14:49

humanity for as a whole.

14:54

And it's kind of like pretty

14:54

tough because we have different

14:57

upbringings, we have different culture.

14:59

We, we have different

14:59

backgrounds, experiences in life.

15:02

So that kind of thing is quite tricky.

15:04

And, uh, actually open AI is having

15:04

now a grant to somehow collect, uh, the

15:11

preferences of humans on scale so they

15:11

can actually be at something and have

15:16

a good understanding of what people

15:16

are actually thinking and valuing.

15:20

Just to jump back a little bit, like,

15:20

uh, because I, I, I said that they raised

15:26

$1.5 billion in four rounds, so that's

15:26

quite, quite interesting story as well,

15:32

because they have notable investors like

15:32

Google, uh, which is splashed, uh, like.

15:37

It's 300 million to 400 million.

15:40

There are different, uh, sources, but

15:40

it's in a couple of hundred millions.

15:44

Uh, they poured into the company and

15:44

also dust most lot, lot of money.

15:48

Like couple of hundred millions. Yeah, it, it, yeah.

15:51

Yeah. It's, it's not easy to just, uh, get

15:51

a few hundred millions from Google.

15:55

So Yeah. It's, it's, it's, it's, it's, it's,

15:56

it's quite a big chunk of money.

16:00

And they got 10% stake. And Dustin Moskowitz, who, who was

16:02

the co-founder of Facebook and Asana,

16:05

or Eric Schmidt, who was the former

16:05

Google ceo, or even, uh, John Lin, who

16:11

was the founding engineer of Skype. So these people were pouring money, uh,

16:13

into the, uh, uh, into this company.

16:18

And the crazy story is that even Ami

16:18

research ventures was putting money into it.

16:24

And it's not just putting, put money

16:24

into it, it's obviously non-voting

16:27

shares, but who is Alameda Research? Can you tell about, so,

16:29

so, so why it's, yeah.

16:32

Yeah. Why is it interesting? Because it was a venture arm.

16:36

Uh, for FTX and basically FTX went bankrupt.

16:40

And uh, and uh, it's kind of

16:40

like the most crazy story.

16:44

So the most crazy thing about that, it's

16:44

kind of like mimicking what happened

16:47

in Bitcoin and and crypto in 2014.

16:51

And do you know what happened

16:51

in 2014 with Bitcoin?

16:55

Yeah, I

16:55

guess you will

16:55

talk about the Mango Mt.

16:57

Gox and Exchange, but I don't know the

16:59

details. Yeah, right. So, so Mt.

17:01

Gox was uh, uh, founded in

17:01

2010, and at one point they were

17:06

handling 77 0% of BTC transactions.

17:11

So two third, more than two

17:11

third of transaction of BTC was

17:14

handled through this exchange. And in 2014, they just announced

17:16

that they somehow lost 75,000 BTCs.

17:24

Which was worth half a billion

17:24

dollars at the time and 6% of all btc.

17:29

So it's insane in if, if you

17:29

think about that, it's the price

17:32

of Bitcoin was $500 back then.

17:34

Like losing this like half a

17:34

billion dollar was a big deal.

17:38

And even crazier thing, what happened

17:38

is than they found 200,000 btc, right?

17:44

So it's, they just somehow find

17:44

like $150 million, like, holy shit.

17:50

In, in my, uh, in, in one of the pockets,

17:50

uh, uh, I just found this, this money.

17:55

So it's insane. And even, even more insane thing that even

17:56

those who were like, uh, get wrecked in this

18:03

situation, they're now getting back, uh,

18:03

the btc, the origin BTC they had, and it's.

18:10

The value of this, uh, money has

18:10

actually increased to $5 billion.

18:15

So if you think about that, it's,

18:15

uh, altogether they lost like on on

18:21

face value, they lost 500 million.

18:23

But now even with just, uh, recovering the

18:23

fraction of it, they have 5 billion, right?

18:28

So 10 times more. It's insane. And that's gonna happen most probably

18:30

with AMI research ventures share in

18:34

the company as well, that during the

18:34

bankruptcy, uh, it's going to be sold off.

18:40

And the crazy thing is they're valued now

18:40

at $4.1 billion, the the Anthropic company.

18:47

And we don't know what was the

18:47

valuation when they put the money in,

18:50

and we don't know what's gonna be the

18:50

valuation when they sell the shares.

18:53

So maybe this is the craziest thing that

18:53

maybe everyone who, who gets wrecked in F

19:00

D X and Alameda situation, maybe they're

19:00

gonna get back more money than they think

19:05

just based on this one bat they made.

19:08

And also there are other bets as well. So for example, do not pay who is

19:10

backed by Andersen Horowitz, who

19:14

is a legal, uh, scalable legal

19:14

application, something like that.

19:18

So they had Al made the research

19:18

has stake as well as there, and

19:22

also I guess some other startups. So maybe the portfolio will

19:27

worth a lot. Yeah, sure. But I mean e e even if I was starting

19:28

with the big four fundamental AI

19:33

models and open AI is by last time they

19:33

raised 10 billion from Microsoft, they

19:39

were valued that almost $30 billion.

19:42

If you think about that. It's like, it's insane.

19:44

And, uh, so, so even the growth

19:44

trajectory and impact of the company,

19:49

uh, means that it convert quite a lot.

19:52

And especially since they have a,

19:52

um, they are very, very explicit that

19:57

they're gonna need, and they're gonna

19:57

raise 5 billion more in the next two

20:02

years, and they're going to create a.

20:06

An AI model, which is 10 times better,

20:06

and it's called Cloud Next, and they're

20:12

gonna release it in two years, and it's

20:12

gonna be 10 times better than GPT four.

20:16

Whatever model is available

20:16

on the market currently.

20:19

But they're gonna need 5 billion

20:19

more, uh, dollars more so

20:22

they're gonna raise more money. Why do they need so much money?

20:25

It's, it's insane. It's, it's, you are spending

20:26

so much time on compute.

20:29

So when, uh, uh, ChatGPT was released,

20:29

open air was losing $500 million.

20:36

It's insane. So the amount of compute, the

20:37

amount of resource you have

20:40

to pour into it, it's insane. And even opening, actually they are spending

20:45

this money on g GPUs, chips

20:45

from me, Nvidia, and also electricity.

20:50

Right? Yeah, right, right. So basically compute.

20:53

Yes. That's, that's quite right. And also, uh, also Sam Artman from opening

20:54

the CEO e of opening eyes is also like

21:00

privately saying that they're gonna need,

21:00

so that's kind of like, like a, a rumor.

21:04

So I, I'm, I'm not sure whether it's true or not. I can imagine it's, it's being true.

21:08

So Sam Artman is basically saying that

21:08

they're gonna need a hundred billion dollars

21:13

to get to the point where they have an agi.

21:16

And what is the agi? AGI is like artificial general intelligence,

21:17

which is in layman terms, means that

21:23

the computer is better at most humans,

21:23

at each economically valuable job.

21:30

Right. So that's, that's ki that's kinda

21:31

like how they define agi and uh, yeah.

21:35

So it's, it's kinda like this whole thing.

21:38

It's, it, it needs a lot of, uh,

21:38

yeah, a lot of resource basically.

21:42

And so they are burning a lot of money.

21:45

And also, like Microsoft is pouring

21:45

a shit ton of money into open ai, and

21:50

they do their own research as well.

21:52

But yeah, this whole field,

21:52

it's, it's beyond comprehension.

21:56

The scale of the compute

21:56

needed and the money needed.

21:59

It's, it's beyond comprehension.

22:00

And we pay 24, $20 per

22:00

month for using ChatGPT, right?

22:05

Yeah,

22:05

yeah. Right. So let me, let, let, let, yeah, let,

22:06

let, let me share my screen or, or

22:10

let me put an image on the screen. And what you see is basically what is

22:12

the monthly traffic, uh, for PT is.

22:21

Bing Google Bar, uh, open AI

22:21

developer portal and po.com.

22:28

So, and what you can see is insane, and

22:28

this is like the number of people who

22:33

are using it every single day, right?

22:37

So I'm just sharing my screen and those

22:37

who listening and don't see my screen,

22:41

what you can see, my screen is the one of

22:41

the days, so June 5th, 5th of June, right?

22:47

And you can see that ChatGPT

22:47

has 63 million users that day.

22:54

And that's like only the

22:54

ChatGPT interface, right?

22:57

So people are going there and

22:57

chatting with Chat g, pt and bing.com.

23:01

The whole bing.com is having

23:01

only 40 million users, right?

23:06

So that's insane already. Chat, GPT suppressed.

23:09

bing.com by 50%.

23:12

Right. Because Bing is like a

23:12

whole, whole, whole, yeah.

23:14

Whole whole search and Gene and everything. It's insane.

23:17

Right? It's insane. And the

23:19

search and Gene of one of the

23:19

biggest technology companies in the

23:22

world, which has been existing at least 20

23:26

years, right? Right. And they are working extremely hard

23:27

to eat the cake of Google, basically.

23:33

Right. And the more insane thing, and this is kind

23:34

of like a tie back, what we talked about in

23:38

the AI episode, the general AI episode, like

23:38

this is the strategic position of Google

23:44

that Bard has only 5 million users that day.

23:49

So even though Google released publicly,

23:49

right, everyone can use Bar now.

23:53

Right? And they released a new model. And if you looked at the latest

23:54

developer conference, it's quite

23:58

funny because AI was ai, ai, ai, ai.

24:01

So every, everything was ai. AI was thought like a thousand times.

24:05

And it was a great joke. Surprisingly, people are using it.

24:09

And also what's in, in interesting that

24:09

platform.openai.com, which is basically

24:14

the developer portal for OpenAI.

24:17

So those developers are using OpenAI to

24:17

as a backend, that's exactly the same

24:24

amount of people who are using board. So developers on one hand, who are using

24:26

open AI, is using as much as everyone

24:33

out there who is using Google Bar

24:33

Chatbot, like everyday people, right?

24:37

And there's, i, I just put

24:37

there the poor.com, which is,

24:40

uh, done by the core guys.

24:44

Uh, it has 2.5 millions. It's half of what Google and half of

24:45

what the platform to openai.com is doing.

24:51

And why is this interesting? It's extremely interesting because Google

24:52

would do something which is useful, right?

24:57

So let's assume they're, they just copy,

24:57

paste, share GPT, and it's helping with all

25:02

your answers and you don't have to search. That's the big problem.

25:05

So that's the trade off. If they create something truly useful,

25:06

they're eating away their own, own own

25:11

marketing in the search, uh, field, right?

25:13

So that's, that's the issue that

25:13

ChatGPTs is actually useful.

25:18

And Google Bar, it's fancy.

25:21

It's good, it's free, so you don't have

25:21

to pay like 20 bucks a month for it.

25:25

But come on, it's not, not

25:25

as many people are using it.

25:29

And why is that? So I just made this, uh, quick, um, graphic

25:30

and I'm gonna share it as well, and I'm gonna

25:36

tell it for those who are just listening. So what you see here is in 2004, if you

25:38

look at the homepages of Yahoo, and if

25:43

you look at the homepages of Google,

25:43

what you see, Yahoo is like crowded.

25:48

And, and it's, it's like

25:48

a, a, a Christmas tree.

25:51

It has all the information, it has all the,

25:51

all the articles, it has all the news, it,

25:56

everything is flashy, everything is colorful.

25:58

It's like a big fucking mess. And if you compare it to Google, Google has

26:01

basically just a search bar and that's it.

26:05

And end of story. Obviously it has some, uh, additional attacks

26:07

like images, videos and those kind of things.

26:11

But the main thing is that

26:11

Google is extremely simple.

26:15

A Yahoo is, is is cluttered, right?

26:18

And fast forward 20 years and 2023.

26:22

If you look at Google and on Google,

26:22

you search for what is the best

26:25

computer to use for home office.

26:28

What do you see on the first page? Ads? Add, add.

26:31

You have to click 30 times,

26:31

you have to spend two hours.

26:36

And if you compare that to ChatGPT with

26:36

Prota extension, it's just one single

26:43

click and you get the five uh, answers,

26:43

which you can use and it's in plain text.

26:48

You can understand. And it took only one click.

26:51

And it, it explains it all because for

26:51

Google to change this, just imagine they

26:56

now it's everything everywhere is ads. Ads, ads.

26:59

If they, if they start to eat

26:59

into it, what's gonna happen?

27:03

Like two thirds of their

27:03

revenue is just gone, right?

27:06

So it's for them, the, the strategic,

27:06

uh, situation is quite dire and

27:12

it's backed up by the numbers. Uh, what I just showed before

27:14

that, yeah, Google Bar is fancy.

27:19

Uh, I tried it, uh, but

27:19

it's all over the place.

27:23

It's, it's not that easy,

27:23

uh, to use as a GPT.

27:26

And even if you go obvious

27:26

to being, it's for free.

27:29

It has GT four in the background,

27:29

but still it's not the same.

27:33

It's not just like having one single

27:33

input, right, one single input field.

27:38

You ask and you can ask follow up question

27:38

and end of story, and the more functions

27:43

you build on top of it, the more complex it

27:43

becomes and less people you're gonna use it.

27:48

So that's kind of like, uh,

27:48

my two minute, uh, divergent.

27:53

Okay. Just two

27:53

implications here. So first, also just I want to expand

27:54

that even the, if you are looking for

27:59

the best computer, the first organic

27:59

results, which are not ads, they are

28:04

still hijacked by affiliate marketers.

28:08

So they are, they're also ads, right?

28:10

Yeah. So, yeah, it's totally full of ads.

28:12

It's very hard to find information,

28:14

but yeah. Yeah, that, yeah. Sorry, that, that's kinda a different

28:16

between searching and synthesizing.

28:21

So, in search, I am, as a user, doing

28:21

search, I'm doing work, I'm digging a hole.

28:26

I have a sh, I get a shovel and

28:26

I have to dig a hole, right?

28:31

But with synthesizing, I'm just like

28:31

shouting out my question, laying back.

28:38

And getting the answer right. And that's kind of like powerful.

28:41

And that's, that's why Sam was saying that

28:41

if you actually create something useful,

28:48

if you take away frustration from people's

28:48

lives and you make it much easier, you

28:53

don't have to be too fancy on the growth

28:53

hacks and such because ChatGPTi had none.

28:58

And it's the fastest growing

28:58

technological product out there.

29:01

So yeah, it's, it's if, if you actually

29:01

solve problem and it's, we see it's

29:06

possible and you don't have to be smart

29:06

about that because if you understand

29:09

it, you can copy and paste ChatGPT.

29:12

So it's not about understanding, it's

29:12

not about technical capabilities.

29:15

It's not about like knowhow, it's only

29:15

about strategic position and incentives.

29:21

And Google has all the incentives to not,

29:21

not, not create something which is helpful.

29:25

Let me ask a practical

29:26

question here. Yeah. So I'm thinking about investing in search

29:28

engine optimization for our cooking school.

29:32

Yeah. But it would take about at least

29:33

a year, maybe two years, and it's

29:37

a constant and significant money.

29:40

So what do you think? Should I do that?

29:42

Or in two years it'll be completely

29:42

useless to invest in link building and,

29:48

and other search engine optimization

29:50

techniques. I, I guess my answer won't be pop popular,

29:51

uh, but it's kind of like aligned what

29:56

Google is saying for like 20 years at least.

30:00

So, But people have a hard time

30:00

to understand it and accept it.

30:05

It's almost like asking me,

30:05

okay, I want to get in shape.

30:09

What should I do? And I, and my answer is

30:10

just like, work out, right?

30:12

It's, uh, but it's not a sexy, so that's,

30:12

that goes for se SEO as well as Google is

30:17

basically saying that you should focus on

30:17

the user and not on what Google is doing,

30:22

because Google is basically chasing the user.

30:25

And if you're chasing Google that

30:25

you're always lagging behind, right?

30:29

But if you just like skip Google

30:29

and you just focus on like, okay,

30:33

how can I give the most value? How can I help the most?

30:38

Right? And if you think about that, and even in

30:39

a cooking school, it's like, uh, not just

30:44

like creating like, uh, random se optimized

30:44

tags, but like, okay, let's get all the

30:49

different use cases I'm solving here. Right? For example, I'm solving for HR people,

30:51

for the HR department in big companies.

30:55

Like the need, I'm solving the need

30:55

of, okay, they need a get, uh, getaway.

31:00

They need something which

31:00

has to be organized.

31:03

And if you understand this, that you

31:03

can, you're not just a cooking school.

31:06

You can actually help

31:06

them to organize better.

31:09

You can provide, for example, an

31:09

an interface that they can vote on.

31:14

What time would fit the best

31:14

for the whole team, right?

31:18

So if you try to make their job easier

31:18

and if you try to make them successful,

31:24

that's kind of like the way forward. And it's gonna be useful

31:26

in the future as well. Because no matter what is the interface,

31:28

no matter, it's like, it's either a,

31:32

like ChatGPT, Google what doesn't matter.

31:34

Because if you understand the problems you

31:34

are solving and it, you make it extremely

31:38

easy and you think through the whole process.

31:42

Even before going to the cooking

31:42

school and after, because it's like

31:47

even like collecting feedback, right? That's like what went good, what went bad?

31:51

What, so what did you learn? So it's like even creating them

31:52

like personalized feedback.

31:55

So, It's extremely good and useful.

31:58

I have, uh,

31:59

advice here that, uh, I

31:59

heard from Chase Diamond, who is

32:02

a genius email marketing expert

32:02

and, and in content marketing.

32:06

And he, he was very successful

32:06

on Twitter with more than a

32:09

hundred thousand, uh, followers.

32:13

And they, Ella Musk announced

32:13

that he's planning to buy Twitter.

32:18

Then he thought, shit, maybe something

32:18

will change here very significantly.

32:23

And the next day he started to build their

32:23

LinkedIn, which is now 200,000 people.

32:28

So May and, and Twitter changed, uh,

32:28

and most of the big influencers on

32:33

Twitter say that their reach decreased

32:33

by 10 to 15% since Omas took over.

32:39

So I guess, uh, for a small business

32:39

like us would be advisable to

32:46

explore other channels, right?

32:48

Yeah, yeah, sure. It's like, uh, first of all, on.

32:52

Your channel and it's email, right? So if you have email and

32:53

you have relationship.

32:56

With, uh, HR people, with, with big

32:56

companies, with recruitment companies or

33:01

whatnot, if you have personal relationships,

33:01

it's trans transcending any kind of medium.

33:07

So it doesn't matter, like, uh, at the end

33:07

of the day, you're gonna use Twitter or, or,

33:11

or LinkedIn or, or Google for that matter.

33:14

So like having relationships, having

33:14

your own email list is kind of like a

33:19

must and obviously diversify and test.

33:23

And if there's a new channel

33:23

like TikTok, mess around, right.

33:26

But don't build every, don't put

33:26

all the, all the eggs in one basket.

33:30

Right. So that's kind of like a,

33:31

a good advice that Yeah.

33:33

Obviously if you have, don't give

33:33

too much value or, uh, let's say you

33:39

just like creating athlete articles.

33:42

Yeah. Most probably you're gonna be that

33:43

your you project doesn't really have

33:48

a good and bright future to be honest.

33:50

But if you solving problems like in your

33:50

case, Uh, you are solving the problem

33:55

of, of people who are having fun, uh,

33:55

organizing them into one place and whatnot,

34:00

and organizing like, uh, uh, but are some

34:00

ways having, uh, some allergy or such.

34:06

And if you can do that and they don't

34:06

have to be, uh, deal with it because

34:09

you just provide like a link, okay. Share this link with everyone.

34:12

We gonna push them. If they are not like, uh, not

34:14

feeling loud, we gonna push them.

34:17

So you can kind of like create a situation

34:17

where you are kind of like a savior of,

34:25

because normally it would be a big headache

34:25

and you just like actually taking it,

34:28

taking off everything from the shoulders,

34:28

and that's a good situation to be in.

34:34

Okay,

34:34

we thought, let's go back to atropic. Yeah.

34:37

Sure.

34:37

Today? Yeah. Sure. So why, why, why, why, why did I say that?

34:42

They're most probably one

34:42

of the most under hyped.

34:45

A company out there, because obviously if

34:45

you go on on Twitter, you see like, uh, open

34:50

AI is doing this, open AI is doing that, or

34:50

Microsoft is doing this or that on Google.

34:54

But Anthropic, uh, is already out

34:54

achieving everyone on the market because

35:01

they have a model which is called

35:01

Cloud, and they have like a small one,

35:06

which is CLO instance, which is kind

35:06

of like a ology of ChatGPT 3.5 Turbo.

35:11

And they also have CLO Plus, which

35:11

is kind of like a GPT four ish model.

35:16

And it also have a hundred K clo.

35:19

And what does it mean? It it, it has a hundred

35:20

thousand token contacts window.

35:24

And what does it mean? It means that actually you can

35:25

feel and you can push one, 120

35:31

pages into the prompt itself.

35:35

So what does it mean? For example, for us, uh, if we have a podcast

35:36

and we have a transcript, We, we cannot

35:42

push the whole transcript in into GPT four

35:42

because it's even, sometimes it's even longer

35:47

than 8,000 tokens, which is like 10 pages.

35:49

It can be more than that. So, and so we have, we up

35:52

in in six pages, six

35:52

pieces, which, which is really

35:56

painful. Yeah. Because, yeah.

35:58

And also like some context

35:58

is missing there as well.

36:00

So if you, it, it is not just

36:00

like Recursively doing on smaller

36:04

batches and then aggregating it. They are sometimes at the first batch,

36:06

there's information which is relevant

36:10

for the last batch, but it's lost, right?

36:12

Because it, everything is, is, is, uh,

36:12

processed in individually, in smaller pieces.

36:17

So it's a pain in the fucking ass. Just like by, just like by what, what Whisper

36:19

is doing is, uh, open is doing with Whisper,

36:24

you can obviously upload a, a, a file, but it

36:24

can be 20 megabytes and it's a fucking pain

36:29

in the ass because you have to chuck chunk it

36:29

up, you have to process it in individually,

36:33

you have to concatenate it and, and so on.

36:36

It's, it's a pain in the ass. And what's insanely good with CLA

36:37

100,000 tokens, it's actually can,

36:43

you can feed into it 120 pages.

36:47

So we feed into the whole transcript,

36:47

which we created, and we can just like

36:52

ask questions like, okay, such as me,

36:52

five titles for this episode, right?

36:57

And we can even use a few short example,

36:57

like, okay, here are good title,

37:02

uh, podcast, episode title examples.

37:05

Here are five and please, regarding this,

37:05

uh, transcript, provide me five more.

37:13

And it's insane that you can

37:13

use it to create LinkedIn posts.

37:15

You can use it to create, uh,

37:15

show notes and it's extremely good

37:19

and it's all already available. So if you go to po.com, you

37:21

subscribe, you get access to the

37:26

a hundred thousand, uh, model.

37:29

Obviously it's limited, so not unlimited, uh,

37:29

amount of interactions are allowed, but you

37:34

can get access and we use it and it's insane.

37:36

And, and they already,

37:36

so you have API access,

37:39

so if you want to build something

37:39

with this 100,000 token model, you can.

37:44

Right, right. But it's not public yet, so you have

37:45

to kind, kind of have, it's, it's, it's

37:47

kind of like the, like, uh, what GT

37:47

four 22,000 token model is like, you

37:54

can apply to get GT four, which has

37:54

like a third of the a hundred thousand.

37:58

So, so it has basically 40 pages.

38:01

So there is a model GT four model,

38:01

which can handle 40 pages already,

38:06

but it's kind of like, yeah, it's beta

38:06

listed, so you have to get access to it.

38:10

So I, I have access to it, but

38:10

it's like, it's not common.

38:13

It's quite rare to, to get

38:13

access to, to that model.

38:17

So that's kind of like, uh, intriguing

38:17

that they already released it.

38:20

So it's not kind of like, Uh, some vaporware

38:20

bullshit, uh, fig door test on their

38:25

part is that actually you can use it. So you can go to po.com, you pay it $20 and

38:27

you can use it and we use it and it's insane.

38:32

Do you want to, uh, provide us like a,

38:32

because I, I, I know that you made some

38:37

comparisons, obviously I use it as well,

38:37

so we kind of like use parallel large GT

38:41

four and the cloud models, so both of us

38:41

are using it, but I, I, I, I guess you,

38:46

you created some more structured way of

38:46

compar comparing GPT four to cloud, and

38:53

can you just share what, what did you find?

38:55

Sure. Okay. So I wanted to understand when

38:56

should you switch to cloud if

39:00

you're already using ChatGPT? Because it's our use case.

39:04

And, uh, in the past episodes we talked

39:04

about several use cases of ChatGPT,

39:09

for example, uh, generating LinkedIn

39:09

posts, threes or prompts for images.

39:14

And I wanted to explore Anthropic's

39:14

model cloud, uh, on these dimensions.

39:19

So first, uh, start with the context

39:19

window, uh, in the comparison.

39:22

So currently what is accessible is

39:22

the 8,000 token window for, uh, GPT

39:28

four. This is basically the 10, 10 pages, uh, in

39:29

the GT four keys and include 100 to 100,000.

39:35

It's. 120. So it's kind of like 10 times more, basically

39:36

what you can fit into now into cloud.

39:42

Yeah,

39:42

so this 8,000 is

39:42

very good for creative task.

39:46

For example, coming up with names

39:46

for our podcast that we did or this

39:50

short tweets, even LinkedIn post.

39:53

And also having a gut prompt, which is

39:53

basically doing a small research on the

39:58

most important OD and do an actual task.

40:01

So this 8,000. Uh, token Window is great, but you mentioned

40:03

if you want to summarize the podcast, then

40:09

it becomes almost impossible even if you

40:09

cut it in six parts and feed it into J GPT.

40:16

That's rolling. Context window will start to cut out

40:16

the first parts, so it's not good

40:21

for long, long documents at all. But with Claude 100, uh, 100,000 tokens

40:23

where I just pasted the transcript

40:29

in, in, in Claude, and I just, I,

40:29

I was able to ask several questions

40:34

and I asked like 20 or 30 questions.

40:37

What are the most vital parts? What are the most engaging parts?

40:40

Uh, write link, write me LinkedIn post.

40:42

And it, and it didn't forget the

40:42

original prompt, which was still long.

40:48

It's like 80, 85,000 characters, which is.

40:51

One, one and a half hour

40:51

when we, when we speak.

40:54

So it was very useful for

40:56

this use case. In your experience, like, because you

40:57

mentioned like LinkedIn post or, uh, using

41:03

the gut prompt and, and these kind of

41:03

things, how does it the, because yeah, it's

41:08

like quantity is not everything, right? So Yeah.

41:10

Obviously it's good that you can fit. More information into, uh, include

41:12

100 K, but what is the, um, quality

41:16

wise, what's the difference between

41:16

GPT four and, and and include 100 K?

41:21

Okay. I will go into the quality

41:21

on different, uh, uh, parts.

41:25

First, uh, when I wanted to generate LinkedIn

41:25

post chat, GPT is very good with zero shot

41:31

prompts, so I just said, please write me a

41:31

LinkedIn post based on this short summary.

41:36

Yeah. On the other hand, it was harder to get a

41:37

good LinkedIn post with zero shot prompt

41:42

from Claude and even with a few shot prompts.

41:46

So I provided two examples, uh, to

41:46

Claude and asked to please write a

41:51

similar, and it was very, it was not

41:51

that strict on following my instructions.

41:58

Yeah, but what, what, what kind

41:58

of, like, if you had to put a number to

42:01

it, so on a scale from one to 10, 10 is

42:01

the perfect, like a master full LinkedIn

42:05

post, one is like basically garbage.

42:08

What is GT four compared to

42:08

closed 100 K, I would say.

42:11

Okay.

42:11

I, I would say other,

42:11

from other perspective.

42:14

So at least I, I think Claude needs

42:14

two or three more, uh, iterations of

42:20

prompts and even the end product is not

42:20

that good that I'm satisfied at all.

42:26

Maybe I would

42:26

say 60 to 60

42:27

to 70%.

42:29

Okay. So, so 60 to seven is the maximum you

42:29

can get out from Claude and GPT four

42:34

is like, what's the number for that?

42:37

Hmm, 9 19

42:39

95. Okay. So it's almost like perfect, right?

42:42

Okay. That's, that's, that's can, that's can, yeah. That, that can be okay.

42:44

This is our baseline.

42:45

It is, it's not perfect as a good

42:45

copywriter would write that, but Yeah.

42:50

You know, t defined our baseline and

42:53

for now, yeah, sure. So it's kind of like, and you, you, I, I

42:54

guess you can even chain things, right?

42:58

So it's like you can do the pre-processing

42:58

on large documents with cloud, right?

43:03

Get a draft, like, which is cohesive,

43:03

which is not perfect statistically, right?

43:07

So it's not something which you are

43:07

satisfied with as a master marketer, but at

43:12

least it's like, it's, it's more cohesive. Or you, you could feed everything

43:14

into one, one row, basically,

43:18

and then you can basically use

43:18

GPT four to fine tune it, right?

43:21

So, because then it's already condensed, it's

43:21

already short, it's already selected, and

43:25

once you have like the gist of the LinkedIn

43:25

posts, which you can generate, then you can

43:30

go one by one and fix the up GTP GPD four.

43:33

Does it make sense?

43:34

Yeah, I think you are right. Amfa Advantage comes when

43:36

you can combine different ai.

43:39

So for example, when you can, uh, combine

43:39

ChatGPT and Mid Journey, and when you can

43:43

combine cloud and ChatGPT, this is where,

43:43

uh, you can create some Word class, uh,

43:50

texts. Yeah, this is extremely important.

43:53

What you just, uh, covered

43:53

that if you know the upside and

43:57

downside of these tools, right? Then you can get the most out of them.

44:02

And it's, my, my biggest problem

44:02

is that, uh, people are too

44:07

obsessed with one single solution.

44:09

Is one single model is one single

44:09

prompt, one single whatnot, right?

44:13

And. It's, it's, it doesn't make too much sense

44:14

because if you, you understand your tools

44:19

and you don't just have a hammer, but

44:19

you have a tool set of different tools

44:22

and you understand when to use them. You can get world class results.

44:26

And so the end goal, at least

44:26

in our case, is not theoretical.

44:30

It's very practical. We want to get sheet done right?

44:33

We want to make money, we want to

44:33

save time, we want to scale things.

44:37

So we are dealing with business

44:37

of ai, not the intellectual of ai.

44:42

So in this case, uh, if you understand

44:42

these tools, and that's why we use them,

44:47

we can, we can get more value out of them.

44:49

And that's what I urge the listeners to do

44:49

as well, that please don't think that one,

44:56

just one tool is solved all your problems.

44:58

It, it shouldn't, there are so many tools

44:58

and that's why we are covering them.

45:01

That's why we are doing these deep

45:01

dives that, you know, the good, the

45:04

bad, and the ugly of each tools,

45:04

which we have, and you can use.

45:09

Okay,

45:09

next one. Naming a podcast. So this is a short text and a creative one,

45:11

and I think that this is a huge difference.

45:17

I started with, uh, instructions,

45:17

several instructions, uh,

45:21

and also a few short prompts. And Che GPT was way much

45:23

better from the get-go.

45:27

It was, it used alliteration without

45:27

asking them it, the names were more

45:31

catchy, but also Claude was very close.

45:34

So, for example, for our podcast, it,

45:34

it mentioned disruptors Daily, which is

45:40

something ti mentioned in the past as well.

45:43

But still, I feel, and, and now

45:43

it's, it's just a subjective feeling

45:46

that Claude wasn't that creative.

45:48

I have an interesting experience. I'm gonna share it with you.

45:50

Okay. Okay. So it's like two minutes. Good. Viktor is diverging for two minutes.

45:55

So again, yeah, again. So in, in 2021, end of 2021, GPT

45:57

three was released and I was playing

46:02

with it and I tried to copy my coach.

46:04

So I basically fed into like example

46:04

question answers, five of them.

46:09

And then I asked my question and it provided

46:09

meaningful answer, helping how to ease the

46:15

pain of my, uh, baby who has stomach ache.

46:18

So that was the use case,

46:18

and it worked wonderfully.

46:21

It even could copy without, like,

46:21

without any in specific instruction.

46:27

It could copy like, uh, double

46:27

spaces at the end of each sentence.

46:31

So it was nine out of 10, I would say.

46:34

So it was almost perfect. It was really good.

46:37

And then two years pass by, and

46:37

all these models get fine tuned.

46:43

On what is a good answer?

46:46

So if you're asking for an answer, what,

46:46

what, what is the human feedback on that?

46:50

What, what makes an answer? An answer? A good answer.

46:52

So giving advice, hedging the

46:52

advice and those kind of things.

46:56

Like, okay, yeah, obviously this

46:56

is why your tummy can a, uh,

47:00

hurt and obviously see a doctor.

47:02

So that's kind of like the structure of it. And what happened is, like two years

47:04

ago, these models, all the models

47:08

like GPT three to all the, all the

47:08

models, they were like a S U V.

47:13

They, they, you could like

47:13

off-road with them, right?

47:16

And in the last two years they

47:16

worked a lot on like putting, uh, the

47:21

whole, all the models on the rail. So basically just keep them on the rail.

47:26

What is meaningful? It's gonna be faster, it's gonna safer,

47:27

generally it's gonna be better, but now

47:32

it's extremely hard to instruct them

47:32

to follow the creative task, which is

47:37

diverging from the, the common path.

47:39

So in this case, in a coaching case,

47:39

answer shouldn't be, uh, giving ad advice.

47:46

It, it should be like, uh,

47:46

introspective, it should be more

47:50

question based and so on and so on. And now I have to be extremely.

47:54

Explicit about like, okay, please write

47:54

double spaces after each sentence.

48:00

Please ask follow up questions, please. And I have to define everything

48:01

and it's a pain in the ass.

48:05

And the best I can get is seven out of 10. And even though these models are

48:07

in general much better, right?

48:11

It is, it's much, much more valuable.

48:13

But now they are very narrow. So in a sense they get le get less

48:16

curative because now answering has so much

48:21

bias and I guess with LinkedIn as well. So, uh, I guess if you just ask for

48:23

two years ago, if you provide LinkedIn,

48:30

uh, posts they, which means that you

48:30

just provide like five good examples.

48:34

So these are five good examples

48:34

of, uh, LinkedIn posts.

48:38

And please write me a new one. Based on like this information two years

48:40

ago, I guess it worked much better because

48:46

it has le had less bias about like what

48:46

is the good, what is like a mad median?

48:51

What is the median of the

48:51

LinkedIn posts, right?

48:54

Because now if you ask LinkedIn post, it

48:54

has very much, it's has a lot, lot of bias.

48:58

If you ask for an answer,

48:58

it's very biased, right?

49:01

So it has a lot of bias about like the

49:01

different modalities of the answers.

49:06

And that's kind of like the tradeoff,

49:06

which happened the last two years.

49:08

And that's got, that's something I just wanted to share. Uh, so if you're frustrated about

49:10

that, uh, my current solution is be

49:15

extremely explicit about what you need.

49:18

And the other hack as well is just try to

49:18

stay away, away from the biased expression.

49:26

So in this case, try not to

49:26

say even, uh, LinkedIn post.

49:29

So these are good text

49:29

examples I need, right?

49:34

So these are good examples. Write me a good, under a good

49:35

one without mentioning that, that

49:39

LinkedIn, the same for Twitter. So not, you are not saying like, I need a

49:40

tweet because it's very biased, but like

49:44

what is a me median tv, which is like, I

49:44

want, don't want to say shitty, but in a

49:50

sense it's shitty that it's if you have a

49:50

specific goal that in that sense it's shitty.

49:55

So then just like write like, okay, these

49:55

are good text I, I need, and please provide

50:01

me one more, uh, based on this criteria.

50:04

Okay. Let's go back to comparing

50:04

Chat, g p, t and cloud.

50:08

So next one is generating prompts for

50:08

mid journey to generate AI images.

50:13

Yeah. And what do you think, Viktor? Which would be the better GT or Claude?

50:17

Or maybe the final verdict will be something

50:21

else. I, I think it's GPT four cuz

50:22

it's kind of like have stronger

50:26

reasoning CAPA capabilities. So I, my my, my guess is that the 100

50:28

K contact spin though is not the same

50:34

as the include plus, which is kind

50:34

of like in comparison with GPT four.

50:38

So the analogy is GPT

50:38

four and include plus and.

50:43

GT Instant and GT 100 k k with Chat GT 3.5.

50:48

So I, I guess maybe comparing, uh,

50:48

the hundred K code to GT four is

50:53

not fair because it's almost like

50:53

comparing 3.5 Chat GT to to G P four.

50:59

Okay. Viktor, I think the final

50:59

answer, we will surprise you.

51:02

So I will sh uh, I will

51:02

share you a few images.

51:05

If you are, uh, listening

51:05

to this podcast, I will.

51:09

Uh, describe what we are, what we can see

51:09

on the screen, but also you can click on

51:13

this chapter digest, uh, that come and

51:13

quickly, uh, find this part in the video.

51:18

So first I asked, um, uh, mid journey

51:18

to generate Greek kitchens, and I just,

51:23

just put image in Greek, nothing else.

51:26

And what we can see on the picture, there are

51:26

four images of Greek kitchens with, uh, sea

51:32

in the background, and it's an inside view.

51:34

And, uh, again, another four pictures.

51:37

These are very similar. They are not realistic,

51:38

but they are not painting.

51:41

So they are in between somewhere. And I did it four times with the

51:43

very same prompt, just Greek kitchen.

51:47

And the resorts were very similar.

51:50

Again, I, I, I'm showing four

51:50

pictures of Greek kitchens.

51:53

Blue, not realistic, not painting,

51:53

and see in the background.

51:58

So basically the same. And, and one more of this.

52:01

And now I asked, uh, uh, clothes with the.

52:06

With a previously mentioned prompt

52:06

to, to generate an elaborative

52:10

prompt, uh, for this Greek kitchen. So for example, imagine, uh, uh, imagine

52:13

a blue kitchen painted on canvas,

52:20

uh, from, from a certain perspective.

52:22

And so, and now we get again.

52:26

For, for one fan of it iteration,

52:26

the images are very similar, uh, com

52:31

uh, compared to the previous one,

52:31

but now it's fully a painted one.

52:35

So now the style is different. So why is it interesting?

52:38

Because now we have a different

52:38

style, so it's now we, we can test

52:42

something, for example, think about

52:42

if we want to create Facebook ads.

52:45

Now this is something different, but, uh,

52:45

here comes the next one and, uh, this is,

52:51

uh, what, uh, again, clothes come up a with,

52:51

and it said, um, create a, create a picture

52:57

of four, uh, culturally diverse women, uh,

52:57

who are having fun in, um, In a cooking

53:05

class and now it, it was something different.

53:09

And, and I'm glad that we live in a

53:09

time when Mid Journey 5.1 is existing

53:14

because these, the faces that we can

53:14

see are almost perfect, realistic.

53:20

And when I, uh, when I tried to create

53:20

faces in Mid Journey V three, it was very

53:25

bad. Yeah. And the, the neat thing what's

53:26

happening here is like you used.

53:31

Kind of like chaining as well. So you either a gpt four or CLO

53:32

to generate prompts for you,

53:36

which you fed into mid journey. Right? And, uh, yes, I guess this talks

53:38

to the strength of mid journey.

53:42

I just want to focus on Claude and Gpt. Just one thing that now we, we came

53:44

up something very, uh, different than

53:48

before, which is, uh, uh, three women

53:48

having fun and their, uh, hair is blown.

53:55

Uh, so it's like a very dynamic image.

53:57

And also the next one cloth come

53:59

up with this. Okay. Just to, just to those who are listening

54:01

here, it's like what you see on the screen.

54:05

Previously it was like the same,

54:05

basically the same kitchen.

54:09

It's like very Greek style with a

54:09

scene in the background, very bluish.

54:13

Uh, it's a blue dominated scenes, right?

54:16

And then what Claude did is like created a

54:16

dynamic scene of people enjoying themselves.

54:21

And then it also came up with. Even more dynamism.

54:25

So not just smiling, but also

54:25

the hair is thrust in the air.

54:29

So the, the, the shot is like,

54:29

it would cost a lot of money.

54:33

And I, I imagine that someone is, if

54:33

someone had to shoot this shot, it would

54:39

take a lot of tries, a lot of time.

54:42

And, uh, it's insane that it's possible

54:42

now to, to have this dynamic setting.

54:46

And it all was generated with closed.

54:48

It's insane.

54:51

Yeah. Hiring the talent, booking the

54:51

studio, uh, having studio lights, uh,

54:55

multiple, multiple people on the crew.

54:58

And of course the photographer, I

54:58

guess at least five or 10 K in the

55:02

minimum, maybe up to 2030 k uh, a

55:02

day for creating photos like this.

55:08

Yeah. Okay. So the next one.

55:11

Okay, this is something CLA camera feed.

55:13

These are very, Plain and boring photos of

55:13

bread and a bucket of flowers on the table,

55:21

which is not good for the purpose, but just

55:21

want to show you that, that now, now cry.

55:27

We started to diverge. Yeah. And, uh, okay, I did, uh, but did the

55:29

very same with Cha GPT and now it came

55:34

up feed, uh, uh, this picture, which

55:34

is olive oils and tomatoes on a plate.

55:41

And, uh, and some people are picking them.

55:44

So again, it's a very different approach.

55:46

On the next page, we can see Greek kitchens,

55:46

but their style is very different because

55:52

it's more of, uh, orange and red, um, type.

55:56

And because J PTI was focusing on

55:56

the ground, which is terracotta,

56:01

which is a classic Mediterranean,

56:01

uh, Flooring building material.

56:06

Yes. So now if you, maybe if, if, if you're

56:07

an interior designer and you want to

56:12

see different creative di directions

56:12

of, of a kitchen, of a scene for a

56:18

film or, or something like that, then

56:18

now it's in different perspective.

56:22

And the last two, one again, uh,

56:22

similar problem, but it's, uh, what

56:27

you can see is a modern Greek kitchen,

56:27

uh, with more of a modern style.

56:31

And it's now, it's, it's very photorealistic. And, and the last one is

56:34

again, a blue kitchen.

56:36

Uh, but it's again focusing because chichi

56:36

pt, um, included something about the floor.

56:43

The picture is focusing on the floor.

56:46

Uh, so, and, and it's photorealistic

56:46

and still blue, but it's very

56:50

different from the previous one.

56:52

Yeah. That's just to make, make clear,

56:52

make clear the whole process.

56:55

For the listeners, you had a prompt and

56:55

the pro, the original prompt was what?

57:02

Greek kitchen. Okay. So that was the original

57:03

prompt, and then Yes.

57:06

You kind of like had two different flows.

57:09

You used clothes, you also

57:09

used Chat gp, uh, GPT four.

57:13

Right. And what, what was the prompt there?

57:16

So how did, and how did you

57:16

explore Curly and moved away from,

57:21

uh, just having a Greek kitchen?

57:24

So we have a prompt, which is created

57:24

by Samsung Mobiles, and this is a long

57:28

prompt, uh, that starts with like this, Ivan

57:28

to Ivan you to act as a prompt engineer.

57:33

You will help me write prompts for

57:33

an AI generator called Mid Journey.

57:37

And later it'll be data that

57:37

please define a camera lens.

57:41

Please define a perspective,

57:41

uh, color, color, style, color,

57:46

references, position elements. And so, so basically you

57:48

outsource the creative process

57:52

to the language models like G P

57:54

T and Cloud. Yeah, so, so actually that this prompt,

57:56

uh, was used, uh, to kind of like

58:01

generate like from Greek kitchen generate

58:01

something which is more crowd, right?

58:06

And only this prompt was used. Yes.

58:09

Yeah. This can be sh uh, shared in the show

58:09

notes actually, um, uh, is part of also

58:14

the God prompt plugin, which we have.

58:16

So if you have got prompt plugin in

58:16

ChatGPT, so if you are a subscriber,

58:21

you can use it and what's happening, it

58:21

realizes that you want to generate an

58:24

image and it's kind of like instructing

58:24

ChatGPT to use this prompt to generate

58:30

better, uh, prompts for mid journey.

58:35

Okay, so my impression about Cloud

58:35

and ChatGPT for these use cases that,

58:40

uh, you can get more if you combine them.

58:43

So you, so you make the same process on

58:43

both and you feed me journey, uh, the

58:49

prompt, the, the final prompts that was

58:49

generated by Chat g, PT and by cloud.

58:54

And in my case, uh, my goal is to

58:54

create very different images so I

58:59

can test them on Facebook with, which

58:59

will get a higher clickthrough rate.

59:04

So I think combining them

59:04

is the unfair advantage here

59:07

again. Yeah, that, that, that, that sounds awesome

59:08

because once again, I just want to highlight.

59:11

Highlight is that you have to have

59:11

zero background basically here

59:15

because you just had Greek kitchen. You copy pasted this prompt, but you

59:18

don't even have to copy paste because it

59:21

should just use the gut prompt plugin. Then you just like write Greek kitchen

59:24

and it's generating for you like

59:28

variations for mid journey and you can

59:28

copy the same prompt and using clothes.

59:33

And it, it's kind of like using a

59:33

different, uh, creative process, kind

59:37

of like using another creative person.

59:39

And once again, you get different results.

59:43

Awesome. So in, in my mind, those pictures

59:44

was 10 out of 10, each of them.

59:48

So they were insane. And you get insane results

59:50

without having zero background.

59:54

And only you, only thing you know is

59:54

like you want to have a Greek kitchen.

59:57

So that's insane. That's, that's, that's, I think if you

59:58

understand this, you are much further

1:00:02

ahead than anyone in your, in your field.

1:00:05

Right.

1:00:06

Okay. Just to final, just to finish this

1:00:07

comparison, just a few technical things

1:00:12

that, uh, I think, uh, cloud, uh, is a little

1:00:12

bit faster so you will get answers a little

1:00:17

bit faster, but the general, the speed is

1:00:17

the same as GPT four and uh, and cloud.

1:00:23

Okay. So my impression was that CLO Plus is

1:00:23

kind of like the same as GPT four in both

1:00:28

terms of, uh, quality is a little bit

1:00:28

lagging behind, but kind of like the same.

1:00:33

But on speed wise it's the same as GPT

1:00:33

four and CLO instant and instant 100 K.

1:00:40

Those are similar in

1:00:40

speed and quality to 3.5.

1:00:46

So yeah, you in, in the instant

1:00:46

100 k, uh, model for you can

1:00:51

fit in 100 K tokens, but it's.

1:00:54

It has lesser abilities than the

1:00:54

closed plus model that's similarly

1:00:58

like GPT four and GT 3.5 Turbo.

1:01:01

It's like, yeah, turbo is quicker,

1:01:01

it's cheaper, but, uh, I mean it's

1:01:06

much less capable on logical thinking

1:01:06

than, than the GPT four model.

1:01:11

Okay. Besides these use cases, I also tried

1:01:11

writing poems and fiction, but the,

1:01:16

but the overall impression is the same.

1:01:19

So first one, Claude is not

1:01:19

following instructions closely.

1:01:23

And one more thing,

1:01:25

Viktor. Sure. So it's, it's kind like the takeaway.

1:01:27

So if you just like want to try to

1:01:27

summarize what is cloud 100 K good for?

1:01:33

The name is even clue instant 100 K.

1:01:36

So that's kind of like the equivalent

1:01:36

of ChatGPT 3.5 turbo, but with

1:01:40

a bigger, uh, contact window. So the similarly, if you use the gut

1:01:42

prompt or try to use the gut prompt

1:01:46

into GPT 3.5, it won't follow it, it

1:01:46

won't be as good with complex logical

1:01:52

reasoning and instruction following.

1:01:55

So that's the same for code 100 K as well.

1:01:57

So it's not that good, uh,

1:01:57

with gut prompt, uh, thing.

1:02:01

And it's not that good with a fiction

1:02:01

writing or poem writing because

1:02:05

complexity, when complexity increases.

1:02:08

You should use or aim for better or

1:02:08

bigger models like Code Plus or GPT four.

1:02:13

So that's kinda like the big, big, big takeaway. Okay, so we

1:02:16

talked about tropics products.

1:02:18

Let's have a big picture view of the

1:02:20

company. Yeah, sure. Let me share my screen.

1:02:23

I'm also going to describe for

1:02:23

those who, who are just listening.

1:02:27

So what you see is the business

1:02:27

model canvas for Anthropic.com.

1:02:31

As as we discussed at beginning,

1:02:31

it was founded by AI researchers.

1:02:36

So if you look at the key activities,

1:02:36

r and d is very important.

1:02:40

In the first one and a half years actually,

1:02:40

they only did research and development

1:02:45

and then they started to commercialize. And since then the other very important,

1:02:47

uh, key activities, product market fit

1:02:52

search in all the different industries. And if you look at the key resources they

1:02:54

have, it's obviously 1.5 billion funding.

1:02:59

And uh, also the their 100 K cloud

1:02:59

model, but also X open AI people.

1:03:06

And, uh, their CEO is VP was

1:03:06

VP of Research, uh, at Open ai.

1:03:10

So, uh, these people have all the

1:03:10

knowledge, all the knowhow, all

1:03:16

the proper, uh, uh, frameworks to

1:03:16

create something which is useful.

1:03:20

And I guess the close one and the case is,

1:03:20

is a telltale of that, that they are the

1:03:25

ones, uh, the only ones which, uh, who, who

1:03:25

provide this, this kind of model already.

1:03:30

And, uh, the interesting thing is, uh, they

1:03:30

have key partners like Google, and Google was

1:03:36

investing, but also they are partnering with

1:03:36

Google to provide compute because Google is

1:03:41

Google with providing cloud services, right?

1:03:43

But they also partnered

1:03:43

with Zoom, for example.

1:03:46

So Zoom Ventures invested money, uh,

1:03:46

just a few, few, uh, months back.

1:03:52

And Zoom is good actually

1:03:52

for CS as well, right?

1:03:56

Actually, zoom has a, a solution for

1:03:56

providing communication for customer

1:04:01

service agents, and they are going to

1:04:01

integrate, uh, Anthropic models to, in

1:04:09

one way help the users to get answers

1:04:09

more relevant, faster, more personalized

1:04:14

answers, and also have the customer

1:04:14

service agents to be more productive.

1:04:18

So that's, that's neat. And also put it there.

1:04:21

I'm, I'm not sure about that. I just put it there that, uh, for

1:04:22

example, a company like scale.com,

1:04:25

which is aggregating the different

1:04:25

AI providers and providing services

1:04:29

to enterprise is something, something

1:04:29

where they can provide value as well.

1:04:33

Like, okay, here's our model and you can

1:04:33

sell our model if someone is in need.

1:04:38

So, uh, key proposition.

1:04:41

So what do they provide?

1:04:44

On one hand, they provide safe models.

1:04:47

They provide 100 K pages

1:04:47

of, uh, 100 K tokens.

1:04:51

So basically 120 pages of text

1:04:51

and clue the next what they're

1:04:57

working on is 10 times better. It's going to be 10 times

1:04:59

better than GPT four.

1:05:01

So that's kind of like, what is the key value

1:05:01

proposition and who are they providing this?

1:05:07

So who are the customer segments is

1:05:07

education, entertainment, government

1:05:11

intelligence, community, legal entrepreneurs.

1:05:13

So basically, as I said, they're

1:05:13

trying to fund find product market fit

1:05:18

and what kind of channels they use. They use Google Cloud.

1:05:21

As I said, they use Zoom contract,

1:05:21

uh, contact center, which is kind of

1:05:25

like the solution Zoom is providing

1:05:25

for customer service agents.

1:05:29

Uh, and mainly they communicate currently

1:05:29

to API documents, uh, their documents.

1:05:34

So how there's actually quite good

1:05:34

documentation about the, how their, uh,

1:05:38

API works and on, on a wide sticky notes,

1:05:38

I, I provide additional, uh, channels

1:05:45

I think they should use or could use or

1:05:45

will use in the future most probably.

1:05:49

So for example, One of the big problems

1:05:49

is finding for them as well, finding

1:05:54

product market fit, but it's also

1:05:54

the problem for the clients as well.

1:05:57

So they, let's imagine you are, you are

1:05:57

having an enterprise company and you

1:06:01

don't know how to integrate something. So having a playground examples, right?

1:06:06

Or having a quiz where you answer questions

1:06:06

about like, okay, what you are building.

1:06:12

Then you can get back examples of how

1:06:12

others are using the tools, right?

1:06:17

So helping people to figure out

1:06:17

how to, and what to integrate into,

1:06:21

into their product, an AI tool. Could be extremely valuable.

1:06:27

And also the how side. So how, uh, Anthropic is helping developers

1:06:29

could be YouTube education as well, and

1:06:36

integrating with tools, AI tools like

1:06:36

long chain and then the similar tools,

1:06:41

and also into no code tools as well. So helping no NOCO tools providers

1:06:43

to integrate, easily integrate,

1:06:48

uh, the aro, uh, model features.

1:06:51

So that's kind of like the big overview.

1:06:54

And also on the cost side, there's a fixed

1:06:54

cost of wages and a variable cost of compute.

1:07:00

And that's what we discussed already. That can be extremely.

1:07:04

Uh, expensive. And on the revenue side,

1:07:04

it's very easy pay per use.

1:07:08

So as much as you use, you pay for that.

1:07:11

And it's quite similarly price, uh, price

1:07:11

to, to open AI and what they don't have, or

1:07:17

at least I, I, I wasn't reading about that.

1:07:19

Maybe they have it, but most probably

1:07:19

they're gonna have having the future.

1:07:22

So that's why I put it in invite, sticky note

1:07:22

is the foundry dedicated privacy instances.

1:07:29

So just, uh, I, I just talked to, I just

1:07:29

talked to, uh, a lawyer who's working

1:07:35

for international law, uh, law firm.

1:07:37

And for example, they paid, I, I guess this,

1:07:37

the pricing is starting at $800,000 per year.

1:07:45

And then you can get dedicated

1:07:45

instances, so in open AI case, right?

1:07:51

And why is it good? Because obviously it's privacy pre,

1:07:52

uh, preserving, they can use clients

1:07:56

data and so on and so on, which is. Paramount and, and, and a

1:07:58

must have for a legal company.

1:08:01

And the, and this is what, uh,

1:08:01

open air is calling Foundry.

1:08:05

And this is the, the gist of it is

1:08:05

basically dedicated privacy instances.

1:08:09

So if they don't provide it, they will

1:08:09

provide it most probably, or they should

1:08:13

provide it because these big companies,

1:08:13

uh, can easily pay, uh, a million dollar

1:08:20

a year because it's, it's nothing compared

1:08:20

to the additional value it can bring or the

1:08:24

additional capabilities it can, uh, unlock.

1:08:27

So this is kind of like the big

1:08:27

overview of what is the strategic, uh,

1:08:31

business model canvas for the company. Great.

1:08:35

Thank you, Viktor. Yeah.

1:08:37

So, okay. Uh, let's move on.

1:08:39

So let, let, let, let's

1:08:39

get to the next, uh, part.

1:08:42

Like, okay. What, because we already discussed

1:08:43

that they're looking for product

1:08:46

market fit, and I guess the listeners

1:08:46

want to know as well, like, okay.

1:08:50

What can I use it now for?

1:08:53

Right? And we already, uh, covered like, uh,

1:08:54

long form content generation editing and

1:09:01

translation, so that just like generating

1:09:01

a whole book or edit a long manuscript

1:09:07

or, uh, coherent translating whole books,

1:09:07

training materials for enterprise as well.

1:09:13

So what's, what's, we already discussed

1:09:13

that, uh, if you chunk up a big piece and

1:09:18

you process it, uh, by one by one, you

1:09:18

kind of, kind of lose the coherence between

1:09:24

the pieces and it, it's already working.

1:09:27

You can already try it. It's quite good, but

1:09:28

obviously it has limitations.

1:09:31

Like, uh, this 100 K model is based

1:09:31

on the instant, uh, model class, which

1:09:38

is fast, but less capable cognitively.

1:09:41

Uh, it ha it can also be used for

1:09:41

extended conversation, role playing.

1:09:46

Uh, it's like almost like, uh, With

1:09:50

just one more thing. What do you think is it possible for, uh,

1:09:51

Claude that we feed the most important, most

1:09:57

interesting, wild engaging parts of our first

1:09:57

eight episodes and it'll write a book for us?

1:10:06

I don't know. We have page book based v we have to try.

1:10:09

I, I'm, I mean, the big, big, uh,

1:10:09

limitation currently is that we don't

1:10:13

have API access only through pool.com.

1:10:17

And it's neat that they have 100 K context,

1:10:17

but kind of like the, the answer is limited.

1:10:23

So if we ask for like, the whole book, I

1:10:23

guess it won't fill out the whole 100,000.

1:10:29

Token limits, so we should, so, so, so my,

1:10:29

my currently our, our real limitation in,

1:10:35

into the test is, is just like, we don't

1:10:35

have API access, but as soon as we gonna

1:10:39

have API access, definitely that's cha

1:10:39

something we, we gonna try to just like,

1:10:44

try to generate as much stress as possible

1:10:44

and see what the output is gonna look like.

1:10:51

Okay. So yeah, it's like what you also discussed,

1:10:51

analyzing entire research papers, legal

1:10:57

documents, summarizing the main points,

1:10:57

extracting key details, or even in the

1:11:03

coding, uh, scenario, refactoring a large

1:11:03

pieces of software code, adding comments

1:11:08

and documentation, writing unit tests.

1:11:11

And I think that since cognitively

1:11:11

it's limited compared to the code plus

1:11:17

model or in a sense to GPT four, I

1:11:17

think like generating, automatically

1:11:23

generating documentation from code.

1:11:26

Would be better suited

1:11:26

than writing code itself.

1:11:29

So, so co code writing is quite complex

1:11:29

compared to just like writing documentation

1:11:34

about like, okay, what, what's happening? What is this code a piece of code is doing?

1:11:38

Because it's, it's quite sequential. It's quite like there is a

1:11:40

code, it has to be explained.

1:11:42

So, uh, writing automatic documentation

1:11:42

is something, uh, which is possible now

1:11:49

for big code base, uh, code basis as well.

1:11:52

Because if you think about that,

1:11:52

there's a big code base, uh, you want

1:11:56

to onboard a new developer, right?

1:11:58

And someone has to write the,

1:11:58

the documentation, right?

1:12:02

And it can be outsourced

1:12:02

to, to these kind of models.

1:12:05

And if you commit to a change, then you

1:12:05

can update that documentation as well.

1:12:09

So you can have an up-to-date documentation,

1:12:09

which is kind of like unheard of.

1:12:13

So it's, it's almost

1:12:13

impossible to do otherwise.

1:12:16

And now it's, it's possible. So that's something I'm,

1:12:17

I'm extremely excited about.

1:12:21

And also the last, last, uh, part

1:12:21

is, um, educational applications.

1:12:26

So that's kind of like the same

1:12:26

as we discussed with the book one

1:12:30

that we have to have a p I access.

1:12:32

But if you have API access possible,

1:12:32

it's, it's, it's gonna be possible to

1:12:36

generate entire course curriculums.

1:12:39

Uh, and, and the neat thing is

1:12:39

like we can feed into so much data.

1:12:44

So it's like we can feed into, uh, textbooks,

1:12:44

we can summary of textbooks, summary of,

1:12:49

uh, guidelines, summaries of, I don't know.

1:12:51

So we can feed in lots of information,

1:12:51

uh, and even relevant context as well.

1:12:57

Like, okay, who are the, uh,

1:12:57

students we are generating this for?

1:13:00

So this can be generated

1:13:00

even student by student.

1:13:04

Basis so you can get your own specific

1:13:04

curriculum and someone else is

1:13:09

getting their specific curriculum. And since it can have all the, uh,

1:13:11

long contacts, that's something

1:13:15

if, which I'm excited about.

1:13:18

And I guess even if it's like, not

1:13:18

right now, but in a year or maximum two,

1:13:23

it's gonna be extremely good quality. So even though now it's like limited

1:13:25

with this instant model of, of like

1:13:31

the, similar to Chat G, pt, 3.5 Turbo,

1:13:31

uh, the same thing with CLO instant.

1:13:36

Even though it's limited

1:13:36

cognitively it's going to be better.

1:13:39

And, and, uh, and this is something

1:13:39

I'm, I think it's, it's quite strong.

1:13:44

Okay, what kind of businesses

1:13:44

can these structures build on top of

1:13:47

cloud? Okay, so let's, that, that's one

1:13:48

of the favorite parts of mine to

1:13:52

discuss business ideas, right? And give you, give you some food.

1:13:56

Uh, food for thought. So what's the biggest problem

1:13:57

with business ideas only?

1:14:01

So let's say, give you an example. My big business idea is CV generator,

1:14:04

which is helping job seekers to

1:14:08

generate first offs, uh, of longer

1:14:08

more compelling resumes, which are

1:14:12

tailored to the specific job opening. Right.

1:14:15

So that's kinda like the, like the idea,

1:14:15

but what's, what's the big problem?

1:14:18

Well, what's, what, what is my biggest

1:14:18

problem is that as a developer or

1:14:23

entrepreneur, do you know what you should

1:14:23

build or, uh, do you know what exactly will

1:14:28

your product be delightful or can you use

1:14:28

this description as a, as a compass age day?

1:14:35

No, obviously not, because it's quite vague. It's just like, uh, okay, this is

1:14:36

the idea of, of, of, of CV generator.

1:14:42

But that's, that's my test model.

1:14:45

Uh, I became up with, with test model,

1:14:45

uh, it's actually solving this problem

1:14:52

because with test model, for example, in

1:14:52

this case, it's, uh, a testimonial which.

1:14:57

Uh, actually following four different things.

1:15:01

First, it's AI generated. Why?

1:15:03

Because it's more relevant

1:15:03

and more personalized.

1:15:06

Second, it's a short testimonial.

1:15:08

Why? Because it's easy to relate to. And also three, it's a vividly illustrates

1:15:10

the pain points being solved so people

1:15:15

can understand what you're building for. And fourth, uh, it makes

1:15:17

the benefits tangible.

1:15:21

So you know, what is the exact results

1:15:21

people should get from something.

1:15:25

So in this case, like, let's say

1:15:25

the first was like, uh, the idea

1:15:28

was that like just CV generator.

1:15:31

Let's compare it to a testing model. In this case, uh, a test model could

1:15:33

be I'm a computer science graduate

1:15:38

and was struggling to create resume

1:15:38

that stood out to tech companies.

1:15:42

Uh, this tool took my basic information

1:15:42

and transformed it into a detailed

1:15:48

company resume that highlighted my

1:15:48

coding project and relevant coursework.

1:15:53

I started getting callbacks for

1:15:53

interviews almost immediately.

1:15:57

So if just like get this short, uh,

1:15:57

testimonial, testimonial, you instantly

1:16:01

know what you're building for, right? You want to get like basic information.

1:16:05

The end goal, the KPI you are

1:16:05

measuring is whether people are

1:16:09

actually getting, uh, uh, feedback

1:16:09

for the companies and so on and so on.

1:16:13

So this testimony is for, so it's like similar to

1:16:15

getting the pains, the gains,

1:16:15

and the jobs of customers, but in a

1:16:19

more easily understandable way, right?

1:16:21

So it's like a testimonial,

1:16:21

which a human could say.

1:16:26

Uh, but if you are reading it as a

1:16:26

developer, it's more easy to understand

1:16:30

what is the target audience needs

1:16:32

and what do, what do they like, right? Yeah.

1:16:34

And, and if I want to be meta, so I

1:16:34

want to provide a test model on test

1:16:37

model, then here is the hard truth.

1:16:41

Bullshit. Personalized doesn't sell.

1:16:45

Real stories do. Test model transformed abstract benefits

1:16:46

into tangible testimonials about,

1:16:52

about, about solving real pain points.

1:16:54

It helps investors to see value,

1:16:54

customers feel understood and

1:16:59

gives the team a meaningful goal. Test model is our daily reality

1:17:01

check and our best CI speech.

1:17:06

So that's kind of like the,

1:17:06

uh, matter of test mode.

1:17:09

Of test mode that, yeah,

1:17:09

you understand that it's.

1:17:11

A compass for everyone. It's easier to convey, and that's

1:17:13

something you can get out each day and

1:17:18

it can guide you each day to provide

1:17:18

value and, and provide, uh, delight.

1:17:23

So this CV generator idea, it's obviously,

1:17:23

it's bad, this tech job resume writer, it's

1:17:29

tangible, but it can also be academic cv.

1:17:32

So all your research and abstract

1:17:32

can be fed into it, right?

1:17:37

It, it wasn't possible before

1:17:37

because the context was limited.

1:17:40

Or it can be an executive

1:17:40

level resume creator.

1:17:43

So if you're seasuite, you want to have

1:17:43

an up-to-date profile, but you don't have

1:17:47

time, obviously you can shield out a few,

1:17:47

few hundred dollars to have everything

1:17:53

you did fed into it and extremely

1:17:53

personalized up to date, strong cv, right?

1:18:00

So, uh, that's something. If you are intrigued about, you can

1:18:02

go out and, and, and, and build this

1:18:06

tool, but let's move on another idea.

1:18:08

Social media management service.

1:18:11

So let's give you a testimonial, a quick one.

1:18:14

I own a small Italian restaurant

1:18:14

in the heart of the bustling city.

1:18:18

We have amazing food, but I struggle

1:18:18

to get word out on social media.

1:18:22

Then I found this service. They started creating posts that

1:18:24

captured the magic of our meals

1:18:27

and the atmosphere of our place. Suddenly we started getting more likes,

1:18:30

shares, and most importantly customers.

1:18:35

So that's something you can go out

1:18:35

and, and create, uh, social media

1:18:39

management for, uh, local restaurants.

1:18:41

How is it better than GPT? Uh, why is it or GT four as an api?

1:18:46

Why, why cloud would be better in this case?

1:18:49

Uh, I think it just, for

1:18:49

example, Processing more information.

1:18:54

So if you have already reviews, if

1:18:54

you have already feedback, if you

1:18:58

have already a book about like, where

1:18:58

people are writing feedback, you

1:19:01

can feed everything into it, right?

1:19:03

So if you want to create social

1:19:03

media posts and you have existing

1:19:07

content, which you set testimonials,

1:19:07

a website, social media posts in the

1:19:12

past, and you can feed a lot of them.

1:19:14

You don't have to select them, just

1:19:14

feed, just put, just put, and it'll

1:19:18

generate new social media posts, right?

1:19:21

Yes, that's right. And but also, like, let,

1:19:22

let's put a spin on it.

1:19:25

So in the outdoor promotion, so from

1:19:25

what's, uh, self published people, uh,

1:19:31

their books on social media, you can

1:19:31

actually feed their book into it, right?

1:19:35

So that's insane. It's like, uh, if you have a, a niche,

1:19:36

then uh, you can go for it, right?

1:19:42

And, and, and, and serve them. And also e-commerce store manager.

1:19:45

So handle social media presence for

1:19:45

online stores, selling niche products,

1:19:50

uh, so they can put a spin on it.

1:19:53

And basically do the social media

1:19:53

management service in any niche.

1:19:58

So let's move on. Academic essay helper.

1:20:00

So let's help students. Uh, a quick test model for this.

1:20:04

As an MBA student, I often had to

1:20:04

research and write case studies.

1:20:08

Each one required a deep dive

1:20:08

into company history's financials

1:20:12

and strategic decis decisions. It was overwhelming until I found this tool.

1:20:18

It helped me to structure my

1:20:18

research, organized my thoughts,

1:20:21

and present compelling analysis. My case studies went from being a source of

1:20:23

stress to source of pride, and I received

1:20:28

high praise from my professors for my

1:20:28

in-depth analysis and clear presentations.

1:20:34

And you may think that this, this is quite

1:20:34

a niche, but I mean, a lot of MBA students

1:20:38

are paying hundreds of thousands of dollars,

1:20:38

uh, just to get this, this education.

1:20:44

So this may sound like extreme niche, but

1:20:44

this is lucrative if you just focus on

1:20:49

them and you can focus on legal, you can

1:20:49

focus on medical, you can focus on tech.

1:20:54

Cause these people, these, these

1:20:54

people who are studying at university

1:20:57

for these fields, they already.

1:21:00

Invest a lot. They already, not just time, but

1:21:01

they invest a lot of money to do it.

1:21:05

So cohesively, summarize, several

1:21:05

research papers wasn't be possible before.

1:21:10

So let's move on. Uh, fiction, cool.

1:21:13

Outdoor. So it's like basically suggesting edits,

1:21:14

characters, plots, descriptions to helping

1:21:19

flesh out, uh, an overall story arc.

1:21:22

Uh, for example, sci-fi, right? So that's extremely short test model.

1:21:28

I always dreamed of writing a fantasy

1:21:28

novel, but struggled with creating

1:21:32

a detailed word, word, and plot With

1:21:32

the fantasy book assistant, I was able

1:21:36

to flesh out my characters plot and

1:21:36

word turning my dream into reality.

1:21:42

I've just published my first book,

1:21:42

so yeah, this, this is something,

1:21:45

I guess it's like, uh, also you may

1:21:45

think that it's extremely niche, right?

1:21:49

Like, yeah, science fiction writing,

1:21:49

but if you think about that, just

1:21:52

one website, it's called numo.org.

1:21:57

Get, it's, it's, it's a nonprofit. It's focusing on only fantasy

1:22:00

writing and they're doing 800,000

1:22:05

visitors per month as insane.

1:22:08

These people who try to write

1:22:08

fantasy, they pour their life into it.

1:22:12

They pour so much time and money just

1:22:12

to realize their dreams and you can be

1:22:19

build a to adjust for them if you're

1:22:19

focusing on just, just for them.

1:22:22

And I guess it's worth like 20, uh, 10, 20,

1:22:22

30 bucks a month if it's, it's good and it's

1:22:28

actually, actually happen, you move forward. But it can also be other like not

1:22:30

just uh, sci-fi novels, but it can

1:22:35

be a fantasy book assistant as well.

1:22:37

Uh, it can be romance nova collaborators.

1:22:40

So it basically can bring this

1:22:40

to two other field as well.

1:22:44

Right. So let's move on. Legal tech company.

1:22:48

So a quick testimonial. As a small business owner, legal jargon

1:22:50

used to send shivers down my spine.

1:22:55

Contract analysis service made it easy.

1:22:58

They turned complex contracts into a

1:22:58

simple language that I could understand.

1:23:02

I felt more confident in my decisions

1:23:02

and saved a fortune on legal fees.

1:23:07

So this is something which

1:23:07

we can be done easily.

1:23:09

It can help also, we can put a spin on it,

1:23:09

like patent fillings assistance, really

1:23:15

estate low advisor for rental agreements

1:23:15

and, and, and deeds and those kind of things.

1:23:20

So these are also may

1:23:20

consider it as niche, right?

1:23:24

Because you think like, ah, no, not, not, not. It's, it's just a very niche market.

1:23:27

But if you think about that

1:23:27

rental market, it's huge.

1:23:31

Like real estate market is huge and

1:23:31

lots of money is exchanging hand.

1:23:36

So yeah, th this can be, uh,

1:23:36

revol revolutionized as well.

1:23:40

So let's, let's move on,

1:23:40

give you another topic.

1:23:44

AI blogger. Give you a quick testimonial.

1:23:47

I run a blog on the JavaScript

1:23:47

libraries, but struggle to keep up with

1:23:51

the rapid updates and developments. The JavaScript Library blogger helped

1:23:53

me to create in-depth, up-to-date

1:23:57

content that my readers love. My blog traffic has tripled.

1:24:01

It can be also Crypto Blogger,

1:24:01

it can be Micro Biology Blogger.

1:24:06

And what's changed with these

1:24:06

tools is that actually you can

1:24:11

feed into the whole documentation

1:24:11

of new, uh, New libraries, right?

1:24:17

You can even feed into the code itself.

1:24:19

So the, the possibility is really

1:24:19

endless and whatever is picking your

1:24:24

interest, you can dig deep and provide

1:24:24

a tool which, uh, which is helpful.

1:24:28

But also news reports, if you, if you

1:24:28

think, if you stay on this train of

1:24:33

thought, that creating news reports like

1:24:33

a local community news reporter, right.

1:24:38

Just a quick testimonial model. As a local journalist, I was overwhelmed

1:24:39

with the number of stories in my

1:24:43

community that needed coverage.

1:24:45

The local community news reporter helped

1:24:45

me write comprehensive news reports

1:24:49

quickly, allowing me to cover more

1:24:49

stories and keep my community informed.

1:24:55

So that's 1, 1, 1 again, it's like, uh,

1:24:55

lots of people think that like local

1:24:59

community, uh, news is, is something

1:24:59

which is niche, but it's quite big,

1:25:04

big market, uh, uh, all over the world.

1:25:06

So that could be useful as well.

1:25:09

Or product descriptions as well.

1:25:11

Uh, just like. Writing Kickstarter, project descriptions,

1:25:13

luxury real estate listing, describe

1:25:18

Describers, uh, and those kind of things.

1:25:21

Uh, it's, it's easy to put a spin

1:25:21

on it and create these tools.

1:25:26

Also, just staying on the language

1:25:26

and, and tutoring side, it's, uh,

1:25:30

let's say a quick, we'll do that again.

1:25:33

I'm a business professional,

1:25:33

frequently traveling to Spain.

1:25:37

I need to improve my Spanish, but traditional

1:25:37

language courses were too general.

1:25:42

The Spanish tutor for business professionals

1:25:42

gave me specific language practice for

1:25:46

my business meetings and negotiations.

1:25:49

My confidence in doing business in

1:25:49

Spain has greatly, greatly improved.

1:25:53

So that's one, again, it's like the

1:25:53

big languages outside English, uh,

1:25:58

which can be targeted and, and niched

1:25:58

down, like just for example, Spanish

1:26:03

for business, uh, professionals, but

1:26:03

it can be Mandarin to, for travelers

1:26:07

or French tutor for students as well.

1:26:10

So, If you're an entrepreneur,

1:26:10

this is the best time to be alive.

1:26:14

And the same goes of for STEM assistant.

1:26:18

So it's like middle school math

1:26:18

assistant, high school physics helper,

1:26:22

or college biology study study aid.

1:26:25

That's the same, same, same thing basically.

1:26:27

And if we go to the enterprise, so what we

1:26:27

cover now is basically what entrepreneurs

1:26:31

can take on, but what can enterprise.

1:26:35

Do or what can be done for

1:26:35

the enterprise as as a client.

1:26:39

So digital therapist platform.

1:26:42

So I struggled, so that's the testimonial.

1:26:45

I struggled with insomnia for years.

1:26:48

This platform, sleep therapy advisor helped

1:26:48

me understand the route of my sleep issues

1:26:53

and provided practical relaxation techniques.

1:26:56

I'm finally getting full nights of sleep.

1:26:58

Again, it can be spinned, uh, to stress

1:26:58

management as well, or PTs D counselor.

1:27:05

So basically providing mental

1:27:05

aid for your, uh, employees.

1:27:10

One of the best ROI thing ever.

1:27:12

We're gonna get into what kind of, uh, pers

1:27:12

you can get if you've worked for Atropic.

1:27:17

But one of the best ones in my

1:27:17

mind is they provide $500 a month

1:27:23

for valance and they understand.

1:27:26

So the company understands if you're

1:27:26

in good and health, then you are

1:27:30

doing better job and better output.

1:27:33

So, but also, but be covered. Code refactoring service.

1:27:36

So Legacy Code modernizer, Python,

1:27:36

refactoring Service Game Code Optimizer.

1:27:42

This is something which is quite common even

1:27:42

with the legacy code bases, thousands of

1:27:46

people writing some code in, I don't know,

1:27:46

a couple or four ton, and it has to be RRI

1:27:52

written and with these kind of tools that

1:27:52

the token bin was big, it's finally possible.

1:27:59

Uh, corporate training is something with,

1:27:59

with cybersecurity sales training course

1:28:05

can be fine tuned to each and every company

1:28:05

because you can feed into the company,

1:28:11

you can feed the sales script and you can

1:28:11

generate something which is specific for a

1:28:14

company and you can sell, sell it to them.

1:28:18

And you can also sell the services itself.

1:28:20

So people are going through

1:28:20

the course, uh, themselves.

1:28:24

Market research analyzes. This is something we also

1:28:26

covered and we do as well.

1:28:29

We do lots of research and what if now?

1:28:32

All the research can be fed into it

1:28:32

and it can make cohesive summaries.

1:28:37

Coding tutor as well. So like Swift Tutor for iOS development,

1:28:39

Python tutor for DA data scientists, react

1:28:46

native tutor for mobile app developers.

1:28:49

So the possibilities is

1:28:49

really endless, I guess.

1:28:52

And uh, uh, the final thing I just want

1:28:52

to say, uh, is that Zoom, as I said,

1:29:00

already invested and they integrating

1:29:00

them into the Zoom contact center.

1:29:04

So this is not, uh, out of

1:29:04

reality, which we cover now.

1:29:09

And uh, also notion AI is built

1:29:09

on top of Atropic models as well.

1:29:15

So, and they also have a junior learning

1:29:15

platform company, which is basically

1:29:23

helping coaching students and it's,

1:29:23

they're covering different subjects, math.

1:29:30

Uh, critical in reading and, and

1:29:30

those and, and these kind of things.

1:29:35

And it's already done,

1:29:35

but it's quite general.

1:29:37

So if you feel inspired, uh, I urge

1:29:37

you to just like create something which

1:29:45

is valuable for your niche and you can

1:29:45

niche down, like you can create of value.

1:29:50

Okay? And

1:29:51

we also did a community

1:29:51

around Anthropic, and we found

1:29:54

that, that Twitter is very strong

1:29:54

with a hundred thousand followers.

1:29:57

And, and when they announced their

1:29:57

most recent, uh, cloud model, then it

1:30:02

reached more than 2 million people. So it's very strong, but also, but I

1:30:04

didn't find any dedicated Facebook groups,

1:30:08

uh, who are focusing on cloud itself.

1:30:11

While there are several FA Facebook

1:30:11

groups, uh, fo focusing on mid

1:30:15

journey and, and GPT as well.

1:30:18

I guess the main reason for that is

1:30:18

it's already closed beta, the API access.

1:30:23

So developers can't really access it now.

1:30:27

The only way to access it either

1:30:27

way through po.com or through Slack.

1:30:31

So you can add their Slack bo

1:30:31

uh, bot and have a conversation.

1:30:35

But that gets kind of like niche. So it's, uh, Still, if you move on to the

1:30:37

next, uh, the, the final part of this, uh,

1:30:44

structure through recruitment, and we looked

1:30:44

at what kind of people they are hiring.

1:30:50

It's not surprising that they are researchy,

1:30:50

uh, institution first and foremost.

1:30:55

So they hire a lot of researchers, uh,

1:30:55

computer scientists, and so on and so on.

1:31:00

And, uh, now finally it

1:31:00

shows that they're working on

1:31:04

productizing what they're building. So they are looking for

1:31:06

product people as well.

1:31:08

And so it's, it's kind of like in the

1:31:08

process of, uh, gradually releasing

1:31:14

what they build to the community. And they're the very

1:31:16

first step, basically now.

1:31:18

So that's, that may be the biggest reason

1:31:18

why the community is not that strong.

1:31:23

And it also says, and shows that

1:31:23

most probably if you are into, uh,

1:31:29

community building, if you are into

1:31:29

developer edu education, if you're

1:31:33

into, uh, solution engineering for

1:31:33

clients, they're gonna need it.

1:31:38

So even though if they may not

1:31:38

have all these positions open now,

1:31:42

they're gonna need it for sure. So, and what does it mean?

1:31:46

It's, it's, that's kind of like

1:31:46

one of my biggest pet pet peeves.

1:31:49

It's like, if you like coding and you want to

1:31:49

quickly understand this field, like solution

1:31:54

engineering is one of the best field you

1:31:54

can be in because your day-to-day job is

1:31:59

basically talking to clients, solving their

1:31:59

problems, seeing how they solve problems,

1:32:03

seeing how they're struggling, what can

1:32:03

be done, and what can, what is analog.

1:32:09

And you're helping clients

1:32:09

to basically create value.

1:32:13

And it's kind of like the fast

1:32:13

lane of learning how to apply AI

1:32:17

to bus real business settings. So, uh, as I said, uh, obviously, uh,

1:32:19

this $500 per month for VanNess, uh,

1:32:27

steepens is, is something, uh, which is,

1:32:27

which I didn't, uh, come across before.

1:32:34

So I big kudos to them for that.

1:32:37

Uh, but also they, they, they cover

1:32:37

the usual, so they give equity, they

1:32:41

sponsor green card if you need it.

1:32:43

So it's kind of like they have

1:32:43

all the products, uh, others

1:32:47

are pro, uh, providing as well.

1:32:49

And I also found they are hiring

1:32:49

recruiters, so it means they're growing

1:32:53

fast. Yeah, yeah, yeah.

1:32:55

And then they need, need operators as well.

1:32:58

And, uh, I didn't really see

1:32:58

explicitly, but I guess they're gonna

1:33:04

need security, uh, expertise as well.

1:33:08

Uh, and as I said, more people with product

1:33:08

expertise, UX marketing, psychology.

1:33:14

What we already discussed with assembly

1:33:14

AI is they, they're focusing on

1:33:18

onboarding people to the, to the api.

1:33:21

So the obvious, the Anthropic is

1:33:21

that the API first company as well.

1:33:25

So they are going to need people

1:33:25

who are dedicatedly optimizing

1:33:30

the VE flow for developers.

1:33:32

And also dog fooding is my middle name.

1:33:35

That's my pet pee. And what is dog fooding Viktor?

1:33:39

So, so dog fooding again, is like,

1:33:39

uh, if you for walk the path yourself.

1:33:45

So you are, in this case specifically,

1:33:45

it means if they require every, everyone

1:33:50

in the company to build something on top

1:33:50

of their APIs and nobody the expertise.

1:33:56

So it's not just engineers and researchers,

1:33:56

but also managers, also customer service

1:34:01

people, and everyone, every single person

1:34:01

should build something on top of the api.

1:34:06

If they require that, so basically use

1:34:06

their own product, what would it unlock?

1:34:11

And that would be the single most

1:34:11

impactful decision they could make.

1:34:16

Because then every single

1:34:16

issue is just bubbling up.

1:34:19

If something is not working, it's not

1:34:19

clear, uh, they or they are not, no,

1:34:24

not enough tutorials for beginners.

1:34:27

It, it, it's gonna bubble up. So all, all these issues are going to bubble

1:34:29

up and it's going to continuously bubble up.

1:34:34

And the needs like, okay, I try to use this,

1:34:34

for example, for sci-fi, uh, writing, right?

1:34:40

And it's not good for that. And it turns out it's gonna, it,

1:34:41

it's going, going to turn out

1:34:44

because someone is trying to do that. So the research and development, uh,

1:34:46

is covered already and defining, uh,

1:34:53

good product market fit and also.

1:34:55

Providing an easy to onboard experience

1:34:55

would be skyrocketed if they're

1:35:01

just like requiring dog fooding from

1:35:01

every single person in the company.

1:35:05

So that, that would be the biggest,

1:35:05

most impactful decision they could make.

1:35:10

Okay. Thank you very much Viktor. And thank you very much Disruptors for

1:35:11

listening to us for this long time.

1:35:15

You will find every important link

1:35:15

and information the show notes.

1:35:19

Please go to this app digest.com. Viktor,

1:35:22

that's a wrap. Yeah, that's a wrap.

1:35:24

Thank you for listening. Um, see you guys.

1:35:27

Thank you.

1:35:28

Bye.

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