So please humor me.
I've got a couple more admin things before we get into action.
So I wanted to talk about how I've positioned this course and whether it's right for you.
You may be new to coding someone that's never written a line of code before.
You might be someone that's already considers yourself an agent engineer.
Those are the two extremes.
Or you might be in the middle.
You're already a Python coder or you are an AI engineer.
Maybe you're even someone that's taken my LLM engineering course, in which case, thank you and welcome.
But I'm here to tell you that this course is designed to appeal to everyone across that entire continuum.
There is something here for everyone.
The people in the yellow boxes in the middle.
You're going to have the best time with this.
It's most well positioned for you, that is for sure.
That goes without saying.
For the people that are new to coding, it's going to be challenging.
There's no doubt you're going to have to have a lot of patience, but this course is okay.
You'll be able to get there and build amazing things.
For someone who's already an agent engineer that's built some agent platforms before.
You may want to speed through some of this, but I've made sure that there's advanced material covered
too.
We're going to be doing some really interesting things at every point and building some great projects
too.
So I'm here to say, if you're new to coding, take some time with the guides that I've written.
You'll find them in the guides folder when we install the repo.
And I've used that as a way to build up your basic, your foundational expertise so that you can hit
the ground running for Python coders and particularly for AI engineers.
If you've taken the LLM engineering course, it's perfect.
Then you're going to have a great time.
And for agent engineer, focus on the projects.
That's when we really put things into action.
I'm really proud of the projects we've built.
I think you're going to enjoy them, even if you've got some experience with this already.
And for anyone that hasn't taken the LM engineering course, I'll be sure to put links to it in the
course resources.
And it's something that I really do see that as complementary, and it's not necessarily a prerequisite
to this course.
You can come into this course without having taken it for sure, but if you have taken it, you will
be really well equipped for this course because it builds up a lot of depth of expertise in terms of
what it means to work to to choose and apply LMS to solve problems.
So in just a moment, we're going to be starting to code.
I know you're antsy.
I know you're waiting for this.
Can't he stop talking and get to it?
We will be.
But I do want to tell you that the first thing we're going to have to do is set up your environment
and setting up an environment, a big data science environment at the frontier of what's possible.
It can be a bit of a painful process.
I'm here to help.
I'm here to make it go smoothly.
But please do have something of a thick skin for knowing that you may hit some challenges.
We'll figure them out.
We'll get them fixed.
We'll get you up and running the.
This time, though, we're going to be helped out by a couple of really great friends in the form of
the platform cursor, which we'll be using as our IDE.
And I just love cursor.
Cursor, of course, is the AI platform that's powered by Llms that allows us to be so productive,
and it's built on VSCode and and it's just great.
And so it's going to be a lot of fun working with that.
But perhaps more specifically for building the environment, we're going to be using this product called
UV, which is you can think of as something which is replacing Anaconda that I used in the LLM engineering
course.
And it's a sort of built on top of, of using virtual environments.
It's really fast and it's really simple and it just works.
I am such a fan of UV.
It's actually a student on the LLM engineering course that that first drew my attention to it and said,
this is what we should be using.
And and it was too late for LM engineering.
But it's not too late for this course.
And it turns out that UV has become so popular that most of the agent frameworks that we'll be looking
at already have UV at their core.
Crew is, in a way, crew is kind of like built with UV, as you'll see.
So UV is really easy to use.
It is nothing like Anaconda for people that have done this before.
And it's it's basically very, very similar to using virtual AMS and you are going to love it.
So I know it's always annoying to have to bring in something new to the mix, but it will be worth it.
You're going to thank me for this.
You're gonna you're gonna love UV and it's written in rust and everyone loves things that are written
in rust.
So there you go.
Get ready for that.
There's actually one more difficult conversation that we have to have, and it's about APIs.
So look, this course does involve making calls to frontier models like OpenAI and Deep Seek.
And they come at a price.
There is a price point there.
And I want to make the point that it is completely up to you.
You can choose.
I use both open AI and deep seq, but you can choose to only use deep seq throughout for a much lower
cost.
And Gemini you can also use.
And right now Gemini has a free tier.
I don't know how long that will last, but so you can use Gemini for free for most of the course.
You can also use Alama completely free just running open source models locally on your computer.
No cost at all.
But there are some trade offs here.
If you want to use any kind of good model, it will take a very long time.
If you use the quick models like llama 3.2, then it might have troubles being coherent, and you certainly
won't get to see the kinds of results that I'll be getting when I'm using the frontier models, but
that allows you to have a sort of balance.
You can run things locally for free and see one set of results, and you can watch me do it with the
frontier models.
If you don't want to spend, the API charges yourself.
So what are these API charges?
Well, different countries might have different pricing.
What I can tell you is that for me, running this entire course, I spent well under $5 on any of the
LLM model costs.
It's very cheap.
OpenAI the first time you use it, you need to put down at least $5 upfront.
And I kind of pay as you go that you spend against.
So that's annoying.
So you might have to do that if you haven't done it before.
But other than that, the actual amount that we'll spend will be relatively small, a matter of a few
dollars.
And it's very hard to spend very much on deep sea because it's so cheap.
Now, I will say that in the last week I have an option to use a proper real time market data provider
that I use, and that cost me $20.
But that's not necessary.
There are free options as well, but if you get into it like I do then, then you might do that.
And you know, I just want to make the point at the end.
People do have like like people get frustrated about API costs.
There's something about them that are a bit galling, because it feels like everyone is sort of nickel
and diming you.
You're getting charged a little bit here, a little bit there, another API cost.
And I understand that even though I say these things are very cheap, a few dollars, it adds up.
And that's not always cheap.
And particularly with exchange rates and different economies, that might be quite a burden.
But I do want to make the point that you have to keep in mind what's going on.
When we run inference on these large llms, there are trillions of floating point calculations going
on.
This is heavy machinery.
And so it's not like these companies are just just making extra cash off you.
I'm sure they are quite profitable, but but most of it, the margins are relatively slim because there's
a lot of compute costs associated with running these massive models.
And if you think of the price of buying a new laptop or even a big like a gamer box that could actually
run larger models itself, you'd be talking about thousands of dollars.
So this kind of API pricing, while it can feel, as I say, a little bit galling sometimes having to
pay each of these different APIs.
It is worth keeping in mind the context of what's going on behind the scenes.
These companies need to pay their electricity bills.
There's a lot of compute, and we're getting real value from what we use.
So I don't know if that helps or not, but it gives you some context.
Please do keep in mind you don't need to spend anything on APIs if you don't want to.
So there are three things for you to remember.
The first of them is that there are resources that accompany this course, and they're really great.
There's a website where I've put links, and I've got extra content in the form of YouTube videos that
should should be helpful.
There's in GitHub, there's a whole section with different guides to teach different kinds of skills
that you might need.
And there's some troubleshooting guides in there too that will fix problems for you and the labs.
So the labs I'm updating them all the time, the labs you can think of them as like an ongoing a fluid
resource.
Every time you do a git pull to get the latest, you'll get refreshed with new content.
Maybe not every time, but most times I'm trying to keep them to be a living, breathing resource for
you.
Then remember to stay patient.
Remember that if you hit problems, if you hit roadblocks, that's actually a great learning opportunity.
Because figuring out what's going on, figuring out how to solve this is one of the best ways to learn
and try and go with it.
Try and enjoy it as much as you can.
There's some juicy projects and figuring out why they don't work first time.
That's part of the fun.
And then the third point is that if all else fails, or even if nothing else fails, then contact me.
I'm here.
I love this stuff.
I'm actually super responsive.
If you email me or if you LinkedIn with me, then I'll respond very quickly.
Unless I'm in a meeting or I'm traveling or something, or I'm asleep.
For people in different time zones, I do tend to reply really quickly.
People are always surprised.
In fact, a lot of people think that I'm I'm an AI agent.
When I reply, they're like, do you have an agent of yourself?
But no, it's the real me, I will reply, I love getting questions.
I love answering so, so do feel free to reach out.
Be in touch.
I welcome ideas and thoughts and questions and whatever you got.
And by all means, if you if you're up for it, then LinkedIn with me I some people uh, like to uh,
don't feel comfortable doing that for some reason, but I'm very open to it.
I love building a community of people in data science and that can support each other.
That can if people are looking for for to hire someone or if they're looking for jobs.
And I can help connect people.
So it's a really great way for me to build and contribute to the community.
And this is really important.
But one of the things that people did from my last course is they would post projects that they've done
and tag me on them, or things that they've done from the course, and I can then amplify it, because
I can then jump in with some comments and, and make some observations or say how great it is, they've
done something, and that will then be available to all the other students from the course.
You can weigh in as well.
And this has really happened, and it allows you to share things and amplify them.
And LinkedIn is a great place to do that.
And it will be something that that will be seen, perhaps by future clients of yours or perhaps by future
employers of yours as well.
So it's a really good thing to do.
So I strongly encourage it.
LinkedIn with me and share on LinkedIn.
It's a great resource and I can't wait to see what you're doing.
So at this point, you've put up with half an hour of me yammering away.
Finally, it's time for action.
We're going to the lab.
We're going to set up your environment.
The next video is for PC people.
We're going to have a video for PC setup, and then the video after that is for Mac people and Linux.
You should probably join in with the Mac people.
It'll be similar enough for you.
And then we will reconvene on the video after that.
So it's going to be PC, followed by Mac, followed by us all back together again with fully built environments.
Let's do it.