WEBVTT

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There are different categories of tools we
have to differentiate when it comes

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to AI tools for developers. We have

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external AI chatbots like ChatGPT
and Gemini, for

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example.
Those are great for asking code-related

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questions and getting answers
or discussing general

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concepts,
not specifically to a concrete code base

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might be working on
but instead for a new project

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planning where a lot of code-based
specific knowledge is not

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required, because, of course, ChatGPT
and Gemini and all these other AI

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chatbots don't have access to your code
base unless you copy and paste

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all those code files,
which nobody wants to do.

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That's where AI-powered completion comes
into play as

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a first in code-based tool.
Here we have GitHub

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Copilot and Cursor,
which we'll both see in this course here,

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and both these tools offer intelligent
autocompletion.

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That's not just completing based on type

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definitions or anything like that
but instead are context

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aware of your file,
the surrounding code of the

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area you are currently working on,
and that they offer...

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Provide smart suggestions for what you
might wanna type next.

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And we'll take a look at these smart
assistants and how they work later in the

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course, of course.
We'll also take a very close look at

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coding agents,
and here I'm talking about GitHub Copilot

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Cursor. Another example which
is not part of the course

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course, would be Claude Code.
Unlike the code completion,

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here we're not just talking about
completing parts of the code but

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instead having the AI agent generate vast
amounts of

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codes, entire code files
and working across multiple files in your

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code base. These agents are aware of your

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project.
You can also give them specific extra

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might need, and unlike ChatGPT and Gemini,
they do work

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in your code base
and understand your code base,

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significant amounts of codes with those
agents, and as a developer, you wanna

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learn how to control these agents.

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And that's specifically what we'll also
cover in this course, how to work

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with Cursor and GitHub Copilot
and how to provide the right

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instructions to these agents.
And then there

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also would be code review tools,
something like Bugbot by Cursor

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or CodeRabbit or other tools.
The idea here is that these are

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tools that typically run in the cloud
and automatically

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review pull requests
that might be opened on GitHub to have an

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extra pair of AI eyes on that code
and have some

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automated code reviews being done for
every pull request that's opened, for

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example. And they therefore may catch
and fix certain

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issues or provide a solid baseline for
human-based

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reviews so that you can build up on what
the AI already

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identified.
This will not be the focus of this course,

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another important category of tools to be
aware

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of.
