This is a little bit of a salacious document, but this document goes into a
whole bunch of different details on what likely went into training GPT-4, a
little thing called speculative decoding of all things. Ladies and gentlemen, boys
and girls, children of all ages, dogs, cats, robots, and everybody in between,
especially you slow and lazy chat GPT-4 responses. This is HTTTA, how to talk to
AI. I am your host, Wes the SynthMine, SynthMine Wes, and as always, in a world
where data reigns and algorithms dictate our future, one voice stands out to
decode the mystery, simplify the complexities, and navigate uncharted
pathways of artificial intelligence. Who, might you ask, is pioneering the
frontier between humanity and technology? Your guide, your mentor, your friend,
with her radiant knowledge, relentless curiosity, she dives into the realms
where machines learn and grow. She is your tech guru. It's go to go everybody.
Gee, how are you? Hi, I am great. For a special occasion, I think I should say
everyone, let's meet wistful, wise, and wonderful Wes the SynthMine. Oh, because
we are having a little celebration. Yeah. If you're not going to bring this up,
I'm bringing this up because I'm so incredibly proud of you. And thank you.
The Upwork, we need to talk about this. You are killing and making history. You
flatter me, Miss GoToGo. So yes, we got some super news in conjunction with
Upwork's AI Hub release. The last few weeks they've been cultivating and
interviewing and screening a whole variety of freelancers and freelance
agencies on the platform to add to this new AI Hub. This is a culmination of
your one-stop shop. If you're looking for someone that can consult, can develop,
can build, can prompt engineer, can make you some AI artwork. And if you happen to
scroll down on the AI Hub, we'll put the link up. There's your friendly
neighborhood SynthMine who happens to be one of the now Upwork certified top 1% AI
consultants, I guess. We need some sort of claps. I can do claps. There we go. Claps around for
everybody. Okay, let's take a step back. I remember when we started talking 10
years back in AI years, which is what? Maybe four months ago. Wow. Fourteen weeks
ago. Yeah. I remember when you told me that, hey, I listed on Upwork to start
doing some prompt engineering jobs. Let's see how it goes. And I remember when you
were like, oh, I just finished a job and like got rating. You're five out of five
stars now straight. So fast forward. This is just, you have the testament how
curiosity in prompt engineering and understanding how to talk to AI and
going, following basically that route, can grow into complexity, into
opportunities, into SynthMine's collective, coming together with everyone
else. I am grateful for a team that includes you, G and a bunch of other
folks that I've been lucky to link up with and meet that share a deep
passion and excitement for everything AI. And because of some of those folks who
also happen to be on Upwork, we have our SynthMine's agency on there. And in
conjunction with this release and us getting this notoriety and this badge on
our profile that we are an Upwork certified AI expert, they mentioned that
we are the fifth most, I don't know if it's popular is the right word for it,
but fifth most globally AI consultants in terms of like jobs that we've done,
finished and rating. That's the fifth largest grossing between me and some of
my team on SynthMine. So yeah, it's been a meteoric rise, I suppose. But hopefully
this is just the beginning. And you know, I remember there's all these discussions
like, oh, it's prompt engineering, who is actually really making money? If you're
creative and you're curious, there's so many opportunities, especially in the
field that people don't know what it is. And just knowing extra, going and
figuring out and helping people and being ahead of the time in that way.
Everything what I'm seeing, more and more stories coming out and just the
SynthMine's collective, everyone involved. It's just amazing. It's a testament that
yes, it is a real thing. It is early days. Maybe it's not going to be around for
five years, but it's going to change its form and shape. But the fundamentals
being first, being first listed on Upwork, that is huge.
And just for some context too, we've got 19 completed jobs. We've got a bunch of
other ones that were underway. And then there's my agency too, that has other
work. Like this is something that was started in March or July now, right? My
first job, I got $32 an hour to do prompt engineering on here. Not big bucks, not
paid consultancy meetings. Someone taking a chance on me based on the profile,
based on what I was putting out there and saying, all right, well, let's see
what you got. And grateful for that opportunity because on platforms like
this, like Upwork, like Fiverr, and we've talked about it before, we as humans
now, internet existing beings that do commerce, interact and talk and just live
online a lot of the time. What's our nature? Who's the best rated? Who's the
most reliable? Who's done the most jobs? Who's earned the most? It's tough to
break out or break onto the scene sometimes. So I'm grateful for that first
opportunity where it all started. Because to come to this point three, four months
later, and now being able to be in a class where Upwork has looked through
our offerings, evaluated how we interact with clients and our outputs, the
outcomes that we create for them. They've done all that and said, all right, you've
got what it takes to handle our enterprise tier of clients. So all right,
let's welcome to the big leagues.
And meaning that you had a physical like call with Upwork that they've been
going through asking questions. So I really like this approach that they're
taking this very seriously and actually doing the background check who is behind
how legit things are.
And let me tell you too, when they had me fill out and update some of the things
that I offer and the agency offers, even some of the things that they were saying,
like, hey, what models do you have experience in? What different coding
languages, what libraries you know how to use. It was clearly very well thought
out. And as a result of that call, you know, I got a little bit of insight into
their long term goals. The gig economy is definitely live and well in, I think
since obviously Uber is kind of the first thing that put it on the scene over a
decade ago, but just since the pandemic, this is I think a very real, you know,
people make their full time incomes from stuff like this, have entire businesses
on these type of platforms that they're hiring people around the world.
So just to, you know, have something on here where they're where they're forward
thinking enough to go, all right, let's try to have have that for everybody,
but also be able to serve big businesses in a way that they can feel a little bit
better about shopping or their choices and who to use for, you know, their AI
objectives, I think is very, very with the times.
And that kind of reminds me of what the ability I found her Mohammed Ahmad. He
was talking about who is going to be that consultants are running AI
departments at AI companies or any kind of institution. And he had very strong
standard, it's not going to be a conventional MBA or conventional
developer. And he was talking about incredible power of communities and also
open source communities. And he was saying that in his company, there is
like 19 year old PhD student, there is, I think he even mentioned someone from
Amazon warehouse who just literally self-taught how to program. So he was
pointing out that you have to be incredibly passionate and take part in a
way leading the way with everyone else involved. And this is how you build up
real expertise, not necessarily going to school and learning what worked in the
past. So I think, yeah, testament to gig economy and communities and
communities coming together to form this collective.
Yes, I am a grateful recipient of those benefits, both from our learn prompting
community that brought us together, G and also my team at SynthBinds. And we
are a complete reflection of what Mo over at Stability, their CEO, you know,
his approach as well. We have got 18 year old freshmen in college that are
just, you know, don't have the education or anything about coding or prompting or
anything like that, but just are super excited and passionate about it. All the
way up to my my old timers that have 25 years of development, leading dev teams
and at all levels of enterprise. We welcome their contributions.
Professors, architects, everyone. And I think another very important aspect is
people bringing their very specific expertise and knowledge. Yes, when you
are in a meeting and you talk about AI and you go like, okay, this is a company,
they want to integrate this chatbot. What other things we can put on top or
ideas? So if there is someone from marketing is if there is someone from
customer service, imagine just those conversations happening in the moment.
And then you throw, yeah, architect on top or I don't know, like someone from
bioengineering. These kind of such a diverse different perspectives, you'll
such innovative idea.
That is the ticket here. The diversity of a team is what really raises the bar,
especially in this because everyone's different viewpoint, different
background can contribute. And that's what all this AI is. It's the great,
great equalizer. And in my mind, so thank you for calling out our little happy
news. Feel free to come find us over on Upwork or elsewhere if you have some AI
goals.
What is the other place where everyone can meet us and engage with us and learn
from us, please?
Our Learn from thing Discord and synthminds.ai, howtotalk2.ai, all those
good places. Did you have something else in mind?
I'm getting more and more messages, people asking where we can learn and
meaning not just self paced learning, like learn prompting is great. Like
this is just to set your curiosity on fire what's possible. But learning from
experts, learning from people who actually do it, I have one place in mind,
which is Core-Eyes.
Yes, indeed. Where we'll be launching our first course offering with AI and
chat GPT for everyone. That will be the 21st of August is convening. The link
has been in the newsletter and in the show notes. We would love to see you
down there. We're going to be expanding on everything from every aspect in
learn prompting, tons of real world use cases, really just going to be the
foundation for hopefully a lot more offerings on there in the coming months
that maybe even we'll see data architect and renowned YouTube celebrity go to go
teaching something on Code Rise.
Definitely going to be supporting you in this and everyone else. Talking about
gig economy and work and expert also up work. I keep getting notifications from
Reddit about you know chat GPT being down. And then I went to read comments
because it's really feels like people are freaking out about it. And the
comment section is incredible. How many people became so dependent that there
was someone saying like, okay, so everybody throw ideas. How do I tell
that I'm not going to meet any of my deadlines? Because that's it. My
superpower is gone. Or someone was saying like, also, I need to now remember
how to code and go to this stack overflow. Like what there is all the
speculations about that GPT four is not as smart as it used to or not about
what's not as good as it should be. And I know we discovered something. So do you
want to just dive into that?
I mean, we definitely could. So this is, I mean, a little bit of a salacious
document, so to speak, but hey, I'm just we're commenting on it. We didn't do
anything with posting, you know, do not claim to be authors or necessarily
endorse or abide by any of the things put out in there. But to just reiterate,
this is leaked by individual by the name of Yam Peleg, scientist, entrepreneur,
founder and CEO of deep trading. This reason this is kind of a salacious post
and on a random pay spin site right now. And we'll put the link in the
description and in the show notes. But it very well may get taken down is because
this was part of a research study that was put behind a paywall that was
1000 bucks to access. And I think the the publisher, the author here just put
it up there. And he's already had a couple of takedown complaints. But it's
the internet. It was something from a study titled GPT for architecture
infrastructure that was behind a paywall that was 1000 bucks to even access the
article. So thank you, Yam for potentially taking the bullet for all of us so we
can learn a little bit. Yeah, I mean, that's what it is. But this document goes
into a whole bunch of different details on what likely this is not confirmed
information, but what likely went into training GPT for and specific to your
comment, go to where people are saying it's down or the quality is deteriorated,
you know, and it's it's no good anymore. There's something to that here in this
document, which is a little thing called speculative decoding. Of all things, they
actually talk about that in here. It's actually a function of the architecture
of GPT for why some of the quality might has reduced or people thinks that the
quality has gotten a little worse. This is my favorite insight from it. There's
this calculation chinchilla is optimal, which basically goes, hey, for this
number of parameters, you're going to need this number of tokens to train on.
And for GPT for if you use this ratio, it should have trained on twice the
amount of tokens that it actually ended up training on, which was 1.8128 trillion
parameters, 120 layers trained on 13 trillion tokens. So what that speaks to
is they are having trouble finding free quality data. So if you've already
scraped all all the Internet, all these resources, I think here's a couple of
things where they confirmed what was in their libgen, SciHub, all of GitHub, you
know, all the Wikipedia, these type of things. But that still is not enough.
Common crawl and refined web were both 5 trillion tokens. So that means that even
all of this all of GitHub, all of SciHub and libgen was 3 trillion tokens, and it
technically still should have had more.
This is something what I wanted to talk with you about AI data pollution and the
comparison to nuclear impact from 1945.
So you mentioned quality, right? It actually has the perceived reductions in
quality actually are speculatively due to this thing called speculative decoding
in GPT-4's inference. Again, author not 100% sure here. But basically, since
there's faster models underneath to decode the tokens in advance, that then
gets feed into the big GPT-4 large model, right? The quality of some posts might
have deteriorated because to save on costs, they're letting the smaller models
like answer the questions in some instances for like lower probability kind
of inputs. So that would mean you have these smaller models that are mainly just
meant to parse through a input prompt and then feed it to the right, you know,
because there's nine models that apparently make this up, make up GPT-4,
feed it to the right places to get the response in instances where it's like,
oh, well, this is a simple question, simple input. I'm only going to have the
lower quality speculative decoding models, the smaller faster ones respond,
not big GPT-4. So there's another case for prompt engineering right there.
Simple zero-shot prompt, not as good right now because to save costs, hey,
I'm going to have the smaller, more narrow model that's main job is just
translating and organizing for big GPT-4. I'm just going to have it answer it
because it saves us on costs and speed, which is the big limitation.
But it makes so much sense in a way because if you look at like population
who use charge GPT, a percentage of people asking absolutely basic questions.
So why would they let GPT-4, like why would they pay so much money for this
compute? So optimizing it and fine tuning and letting, as I said, lower quality
models do the job because that's just enough. And yeah, that's absolutely right
with it. If you're smart with your prompt and you know how to prompt, well,
you can kind of unlock the actual GPT-4.
Yeah. So here's the no kidding hard costs that they have to pay in power
and usage, you know, for a thousand tokens on GPT-4, the smaller GPT-4
inference model is 0.0049 cents per a thousand tokens to basically process
the data. Right. And since this is getting used so, so much at a time,
Chad, you know, even OpenAI is processing like our individual queries one at a
time, like, Hey, I'm going to write something in. It's going to go to, you
know, to some server farm somewhere that is running GPT-4 and answer my
question. It's literally all batched together second by second, everybody's
inquiry, right. And sent to it at once. And these things are bulk decoded by
these smaller inference models and executed by the big GPT-4 models. So
that saves on their utilization that by keeping these batch sizes high, but I
think when people, you know, use simpler prompts, when they're not asking more
complex questions, it's way more easy to just offload those responses in simple
ways, then continually push them in. Especially if that, you know, that model
determines there's probably some threshold because every time the
language model is responding where the transformer architecture works, it's
calculating this like singularity value. And if it, it seeds this little value,
which is the probably best probable response, if it probably doesn't meet
certain threshold, GPT-4 is just having these smaller organization models
inference them and respond. Why some people think, Hey, the quality has gone
down.
So if I go on Reddit and I see every post which says, Oh, quality is down.
I'm not achieving the results I used to. I can assume that prompting in not
exactly well made way.
Their prompting probably sucks.
This is like the vetting process.
You know, it's just one of those things where, Hey, we have a podcast so we can
hopefully get people to think about these things a little different and try
to get a little more juice out of the GPT-4 orange by some good prompting
engineering and some good little tips and tricks on how to talk to AI.
It's kind of built this way that if you don't try and shape and put some
thought and care and effort and maybe some technique into your things,
you're passing into GPT-4, it's just going to do what it was designed to do
and answer your question in the simplest, most likely way.
I think it was from Albert Einstein saying that if I have a problem, I
have a problem and I have one hour to solve it, I would spend 59 minutes
thinking about how to solve it or how to frame the question and then actually
do it, something between those lines.
So this is like being very intentional.
What are you trying to achieve?
How do you communicate that to actually get you the results is important.
But this is like, again, the subset of a population who really want to
use AI in an extraordinary manner.
However, the other 99% of people are just going to be happy and satisfied
with results that they get, even with simple prompting or already
pre-optimized prompting from their chatbot or organization, don't you
think, but not everyone needs to have access to superpowers.
Yeah.
And with what?
What's the line with great power comes great responsibility.
If you're going to be able to reach things, well, you know, it's probably
in your best interest to educate you and the best and the safest and
the most ethical ways to do so.
Now that we are back on a quote, writing that train, this is another
part is that, you know, if you give to the child, super advanced computer
and programming, and we don't even know how to use it, there is no point in that.
Right.
So this kind of like optimizing that, okay, for the lower quality prompts
or tasks, this model is good enough and AI can determine that, of course.
And for anything which is more advanced, okay, then we kind of, this is like
qualification, right?
Like where you pass the level to be able to actually communicate with GPT-4.
This is also interesting looking in the future, right?
There is going to be a group of people who are going to be
people who have access to the best of the best or know how to access
versus everyone else who have good enough.
It could be the great unifier or the great divider, at least when the way I
kind of think about it is it's all out there for you to kind of determine
your own destiny, your own future.
I wanted to talk about just, I wanted to mention the training cost.
What are those numbers?
That's the amount of processing power that it took to train GPT-4.
So that's 2.15 times E to the 25th.
So that means you take this little decimal point and move it 25 spaces to
the right and then put zeros in the spaces.
That's how many, many flops here was needed to train it.
So they trained on 13 trillion tokens of data.
They did it in six goes, right?
Two goes did all the text data and then four were required for all of
the code data trained on.
All right.
I think the most, I mean, just talk about a ordeal, 25,000 A100.
These are the top end machine learning GPUs for 90 to a hundred days, but they're
only able to use them at about 32 to 36% utilization.
And that's because at all these checkpoints, they kept having different
failures that needed to be restarted from.
So that's where that million lines of code comes from.
It's they're having to tune in different parameters and tweaks to even ingest
all this data into a model because there's so many different conflicts.
They said that speculating on technology that was available back
when GPT-4 was training, it took 90 to a hundred days, probably cost
about $63 million to train and that's just in server costs.
So if you now put in all your architect, your, all your architects, all your
designers, your coders, all your overhead.
I mean, like that's, that was a hundred million bucks to train a model
that can perform like that.
Today, some of that could have probably been done a little more efficiently
because they have these H100 GPUs now that Nvidia puts out probably going to
even be less could have done in half the time, third of the budget.
To me, that's fascinating to be like, wow, just the amount of, I mean,
like where do you even begin?
We like, oh, this is going to be a million lines of code to, to do this.
Like imagine starting at line one, like, all right, let's keep going.
It's insane. Like, you know what I would like to see someone or maybe we can even
do a translating this in the energy.
How much energy did this consume?
For 90 to a hundred days, 63 million, like investment in training.
How much that was also electricity bill.
I would love to see visualization just like powering mega cities.
That is the limitation right now.
And I think it's also why, and we think we've mentioned it on here, Sam Altman,
and there's a lot of AI, you know, experts and leaders that are very
invested into renewable energies, fusion technology, because if it cost that
much in just compute time, 63 million to train GPT-4 for a 1.8 trillion parameter
model, if their true mission is AGI, artificial general intelligence,
that thinks in reasons like a human does, well, our brain has a hundred trillion
parameters, basically connections between neurons.
So if we're just going to go apples to apples, if you've got to get a model
that's 50, 60 times bigger than GPT-4 to maybe achieve AGI, a one, how do you
think data to train that considering that they already identified that they
were 13 trillion tokens short in training this, but they're not going to
short in training this?
One AI.com is kind of floating around.
That's something interesting to look into.
They claim that it's AI, which is trained on your internal documents.
So it inherits the knowledge from your business.
And basically they provide a bunch of different use cases, products, like
custom skill development, biz, GPT, language analytics, multilingual AI, and so on.
I'm very curious to try it.
And I was thinking to book a demo definitely would be interesting to report on that.
What I love about something like this, just looking at it with my little like,
you know, AI hat from someone that builds things like this, this is essentially top
layer fine tuning with a lot, a lot of spit and polish on every, every aspect.
Folks can go out there with a handful of GitHub repos that are free as can be, do
this themselves, but I think it's a very real thing when you have big businesses
that are not AI experts or tech centric or whatever, they are more than happy to
just pay big dollars to go you there.
You have a shiny AI website, make me the thing where it can be chat GPT for my
info.
So kudos to them for taking advantage of it.
I mean, that's, that's what we're trying to do on a different or smaller scale.
Exactly.
It's you take everything what's available and you package it in a beautiful
website and then you go and sell it.
What's interesting to me as an outcome, because I worked in big data company
that you can bring anything like this, but if your data is a mess, if your
systems is a mess, nothing speaking with each other, the next natural step is
same company is going to be like, yeah, we used it.
It's not that good.
The response from this company should be like, oh yeah, of course, but we can
help you with, you know, introducing machine learning, looking through your
whole processes, optimization, and then we prepare your businesses for actually
using it.
So this is kind of double upselling and I think a lot of businesses is going to be
experiencing that.
And this is what I told you when I had conversation with the lady sitting to
seats below Sasha from Microsoft.
She will exactly talked about enterprise level that enterprises buy anything AI.
90% of them don't have infrastructure to actually fully use it.
And there's so much opportunity for not just consultancy, but how to integrate,
how to make processes run because it's like putting a lipstick on a pig.
What is the saying or garbage in garbage out?
What I understood is also what she was saying.
You have AI on your business data and that's basically the future.
And another interesting thing when we talked about hallucinations and like, I
was like importance of prompting.
And she was like, these things were never meant for factual data.
That's what you have Google for.
And I was like, okay, for Microsoft, they've been, you know, on it for a while.
And it was meant for internal how to optimize processes, PowerPoint things,
summarizing meetings, all if you think about it, really like these LLMs solve
those problems very much white collar day to day job problems, knowledge centric
jobs, exactly.
So, but those problems center around specific knowledge.
So now of course, there is a part of, you know, interesting things that are
happening in the world.
So, you know, internet and training as a knowledge and testing and improving and
all that.
The point is that for her, this prompt engineering absolute drop in the ocean.
She is on completely different level and scale of thinking.
For her is enterprise.
For her is like every enterprise, the clients of Microsoft enterprise, this is
where real money is.
For everything AI, every enterprise project, everything, what is not hardware.
She is the head of globally selling these things.
So she said everyone is buying it.
Everyone wants it.
90% don't know how to deal with this, what to do with this.
And it's the same problem as always.
If you have shitty processes inside your business, if already departments don't
speak, data is completely unstructured in random places.
You have to rethink the whole business infrastructure.
And she was saying that implementing AI into enterprises, this is where money is.
Oh, no doubt, without a doubt.
Yeah.
But you have to make some sacrifices to make things simpler, to make them bigger
and broader.
So I think that's why she probably doesn't think too highly about prompt
engineering.
And as a matter of fact, like I kind of, you know, the statement to say where
these things were never meant for factual data.
Well, no, they are, but you need alignment.
You need super alignment.
That's why opening AI is hiring a super alignment team to work through these
things.
It's a perfect example of, in this little article here on Pastebin that talks
about everything that goes into GPT-4, right?
There is over a million lines of code that is just for alignment related
purposes to get it to reason the way it should.
A million lines of code, you know, just for alignment.
And that's for a model that has 1.8, as it says, 1.8 trillion parameters, 120
layers.
So a million lines of code to get it to, and what alignment means is thinks and
reasons like a human.
That's what they're going for.
Just to say that her kind of stand for prompt engineering was just basically
that's not something on enterprise level.
That is a part of the whole bigger problem.
It's maybe a feature that a whole team of people have put into a single button.
This is very similar.
What I was looking at Prom Perfect, their new feature prompt as service.
Yeah, I love that.
Exactly.
This is what you place behind the button.
You click it, everything is optimized for your specific use case.
When we were drinking wine, she was celebrating that there is a reorg.
She's moving up, can't disclose that actual title now, but it's insanity.
She was kind of making us an analogy of it.
Apple is going to make pretty shiny thing as their VR headset and everyone is
going to want that.
But again, if you compare money to be made from enterprise AI, where Microsoft
is going 100% chips versus doing hardware VR headset and trying to compete with
Apple on all the aspects, it's just something completely not focused.
The whole VR experience or like augmentation of how we experience or
interface with hardware, the final view, the final how we experience it is not
going to be a headset sitting on your head, but to get there, that's one of the
tools that we are going to use till we have something better technologies there.
We talked about prompt engineering similarly, and we've been talking that
this is going to be relevant for like two, one to two years.
Immediately relevant for anybody to casually get the most out of AI.
If you want to use AI at a high level now, you have to learn some prompt
engineering to do that plunge different ways, pop over to learn prompting.
Hey, we happen to have a course.
On AI and chat GPT that we're launching here and with co-rise in August.
That's another great way.
There's tons of different resources, but you were right.
I think the days of that being a requirement to interface with
AI at a high level are numbered.
I also gave her like my, what I shared with you was two visions of easy and
effective and that it's 1% of people who are going to move to what is
actually behind that button, how it's getting done, how it's getting done.
And how it's getting optimized, how every organization has their own AI and
prompts designed for that organization, maybe versus everyone else using it.
But it's the same as it always been, right?
It's the same pattern going forward.
That 1% is amazing opportunity because this is also the reason why we
called this how to talk to AI and not prompt engineering podcast.
Yeah, we had no discussion.
Right.
So because how you talk with AI will be evolving to that crazy sci-fi levels.
Where one day I can just grunt at a Microsoft PowerPoint document and
it makes me my whole presentation.
It was just fascinating to see at what scale she's thinking.
It's completely different from anything I've seen.
We are not talking millions, hundreds of millions.
And if it's not hundreds of millions, then it's not interesting.
So that's definitely something.
And it was quite reassuring that the biggest companies are jumping on AI.
She said 90% of them have no clue what to do, how to do it.
So there's so much opportunity.
Maybe they need an AI expert, a certified AI expert.
This story of you and your journey, plus, you know, going from small $32 prompting job.
Do you still know the client?
We should bring them on the podcast.
He was a marketing guy and he actually, he had a tool for internal tool that he had
already built that would just basically like help his people respond to client
inquiries way faster and just built three little prompts that, you know, did some
specifics that he wanted from summarizing to email responses to different viewpoints.
As you can see, I still remember it, but yeah, it was $96 and then it upworked
you know, when you're a newbie, they take their cut.
So they take a 20% on made all like 70, 74 bucks or something like that.
But that was enough to get this train rolling.
This is how things start.
And nevertheless, this is absolutely inspiring and should be inspiring for anyone
that it's not by any means too late.
There is of course, first mover advantage, which you experienced, but the space is
still so early, like this is going to be just like, you know, like a
place growing and how starting with something smaller can lead you to almost
working at enterprise level.
This is just fascinating because this is where real problems and real money is
going to be made.
Is this place good as any, finish this podcast.
This place is as good as any go to go for G for yourself and West the Synth Mind.
That's me saying happy prompting everybody.
Happy prompting everybody.
Thanks for listening to How to Talk to AI with your hosts,
Go2Go and Wes the Synth Mind.
As always, you can check out the show notes and links at howtotalk2.ai.
That's all for this week's episode.
Happy prompting everyone.
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