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AI Foundations: Introduction

By Cursor

Summary

## Key takeaways - **AI for programmers, not ML engineers**: This course focuses on developers using AI models and tools to be more effective, rather than on machine learning or training models. [00:04] - **AI tools require understanding models and limitations**: To effectively use AI tools, developers need to understand how the models work, the different types available, and their limitations. [00:23] - **AI code completion saves time**: AI-powered features like Cursor's tab completion can significantly speed up coding by suggesting and completing lines of code, imports, or even navigating between files. [02:05] - **Understanding AI is key to avoiding frustration**: Without a solid mental model of how AI works and its limitations, developers can face frustrating experiences with incorrect code generation and debugging cycles. [03:13]

Topics Covered

  • Choosing developer tools: Time, money, reliability, effort.
  • The evolution of coding tools: From Vim to AI.
  • Why a mental model of AI prevents coding frustration.

Full Transcript

Welcome to Cursor Learn. This course

will teach you how to use AI to be more

effective as a programmer. It's not a

course about machine learning or how to

train your own models, but instead a

course focused on developers writing

software using AI models and tools.

[Music]

Now, to really use AI tools, well, you

need to have an understanding of how the

models work, the different types of

models, and what some of their

limitations are. So, let me give you an

analogy. Imagine you're trying to get

across town. You could walk. Of course,

that's free, but may take a little

longer. You could ride a bike, but

that's going to require a little bit of

money and it's slightly faster. or you

could drive a car. It costs the most

money, but it's going to be the fastest

way. Now, you're probably thinking,

"What about public transportation?" And

I'm sorry if you live in New York or

somewhere in Europe. I get it. Now, the

reason I like this example is because

you have the choice between time, money,

reliability, and effort. Now, let's

apply the same idea to building a

software product. If we go look at

cursor for example, I might do export

default function page. You know, it's

just it could be really slow, but you

could get really good with your Vim

motions and all of your editor tricks to

just really nail text editing, which

isn't necessarily a bad thing, by the

way. Now, you can also use things like

IDE that have more built-in tools like

language server protocols. Now, let's

try that same thing again, but in an IDE

like cursor. So I can type export

default function. And you see we're

getting these helpful bits of feedback

from a language server. They can help

you move faster as you're writing code.

Now this is great and I would highly

recommend working with language servers.

And recently we've seen another

improvement. Integrating AI models into

how we write code. Now with a little bit

of practice and a little bit of effort,

they can help you move even faster. So,

let me try this same thing again, but

I'm going to turn on cursor tab, which

you can think of like a very helpful

autocomplete for your code. So, as I

type export, you're going to see this

ghost text. It's a little dimmed out.

And this ghost text, I actually can tab

to accept the recommendation. So, I'm

going to hit tab. That looks great. And

then it has another suggestion. So, I'm

going to hit tab to accept that as well.

Now, this can be very helpful as the AI

learns more about the patterns of how

you write code. and it tries to help you

just save time predicting the next most

likely action you want to take, whether

it's completing a line or adding an

import or even jumping to another file.

There are millions of us right now

curious how AI might help us be more

productive, but we want to understand

how to use these tools before we invest

further. And really, working with AI is

kind of like a new type of programming

entirely. Maybe in the most basic

example, I don't have to go dig through

Google or read through a bunch of

different documentation sites to find

what I'm looking for. But as the

complexity grows, maybe AI can help us

intelligently autocomplete our code like

I showed or even create entire files

based on our guidance and our existing

codebase. That sounds amazing, but if

you don't have a great mental model for

how AI works and critically its

limitations, it can be kind of a

frustrating experience. If I'm being

honest, the AI model might generate some

code that doesn't really work and then

you ask it to fix that change and that

still doesn't work. And then you get in

this cycle where you can't figure out

what's right and what's not right.

That's really frustrating. So what I

want this course to be is to help you

understand the foundations and also to

show you practical patterns for how you

can build software using AI models and

the latest tools. We're going to show

you some examples in cursor, but

generally a lot of these patterns apply

to really any model or product. All

right, let's get started.

[Music]

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