Well, just before we wrap up, let me introduce this week's challenge and talk about what we're going
to do.
And this is going to be, I think, again, a lot of fun.
Hopefully you're finding all of the projects that we're doing together fun.
I'm really trying to to come up with with interesting, juicy projects that stretch us in different
ways.
So I guess last time we've done, we built things like a system that generates minutes of meetings by
listening to their audio.
This time something completely different.
It's going to be about writing code.
And in particular, the idea is it's going to be about a code conversion, I guess somewhat inspired
by bloop.
And they're fiendishly brilliant idea.
So in this case, what we're going to try and do is write a product that is designed to improve performance
and performance critical code by converting Python to C plus.
Plus.
That's the idea.
So we want to to find out how we can convert Python to C plus.
Plus.
And we're going to do this using a frontier model.
And we're also going to do it with an open source model.
And we're going to compare the performance of the results.
But obviously we're going to have to start by selecting llms that are most suitable for the task.
So that is the challenge at hand.
I think it's going to be fun.
We're going to see how we perform at optimizing code with the help of an LLM.
But first, just a quick wrap up for day two of week four.
Let me do that thing one more time.
You're probably sick of this, but I do want to remind you of all the things you can do.
You can code with frontier models, including building AI assistants that use tools.
You can now also build open source solutions using hugging face the the Pipeline API across a variety
of inference tasks and hugging faces, face, Tokenizers and models, which is the lower level APIs
which give you more insight into what's going on and much more flexibility and will become essential
when we get to training later.
And now?
Now, hopefully you should be in a position where you can confidently choose the right LLM for your
project, backed by real results from leaderboards, from arenas, and from other resources.
And when I say choose the right LLM, typically you'd be choosing the right 2 or 3 llms that you will
then go into prototype with in order to then finally select the one that performs best after next time,
you should have a deeper sense of how to assess the coding ability of models, and you'll have used
a frontier model to generate code and built a solution front to back using Llms to generate code.
And that's going to be another another skill that you will have acquired on the path to being a highly
proficient LLM engineer.
I will see you there.