Learn Ramda, Rust and Strapi

**Developers aren’t the only ones working on sites.** 

Marketers, copywriters, designers, and other non-coding roles all need to be able to make content changes. If you don't want to be bothered to open a PR every time a typo needs fixed, you *need* a CMS. 

And Strapi is an *amazing* CMS option.  

Strapi helps you quickly build a robust content API. You can host it yourself, it supports both RESTful and GraphQL, handles your authentication, lets you bring team members and it's highly customizable with plugins and extensions. 

You can take those gains even further by adding **Next.js 13 to the stack.** 

Next.js is fast, supports both server and client-side rendering, and features dynamic routes. Once you are generating dynamic pages from your CMS you’ll see just how powerful the combination of Strapi and Next.js is!

**In this course you will do the following:** 

- Building Content Models with Strapi
- Fetching Data from your Headless CMS
- Using React Server Components
- Using the Next.js App directory
- Setting up pagination in Strapi and Next.js
- Parsing and rendering content in your Next.js application
- Connecting external services to your Headless CMS (like Cloudinary for Media Management)
- Deploying your Next.js and Strapi Applications to a production environment
- Setting up and using Strapi Plugins
Transferring data between your Strapi environments

By the end of this course you’ll be building pages and managing your content like a pro!

strapi

Modern CMS Driven Web Applications with Strapi and Next 13

There is a lot of extra functionality that you can import into your Strapi application through Strapi plugins. 

To demonstrate how easy this is, we'll install the `strapi-todo-plugin` and add a To-Do feature to our Strapi application that will help manage our content.

Install and Use a Strapi Plugin

Inviting users to your Strapi application is simple. First, we'll create a role for editors so they can't publish or delete content then we'll add the user and fill out name and email. This gives us a URL that we can send to the new collaborator and they can set their password and log in to the Strapi dashboard.

Invite a New User to Your Strapi App

When a new Strapi version is available, you'll see a notification in your admin panel. You can also check the [Strapi GitHub releases page](https://github.com/strapi/strapi/releases) that is linked in the notification to see what's new in the latest version and take note of the latest version number. 

Once we upgrade the Strapi dependencies and install again, you'll notice the notification is gone and when you check settings the version number has been updated. To update production, you push your changes to GitHub and run `git push heroku HEAD:main`.

Upgrade a Strapi App in Development and Production

There are times that when deploying your Strapi application you'll want the data and settings you developed with to be transferred to your production environment. To do this we can use Strapi's data transfer feature. 

To start the data transfer we will need to log into the production environment and create a transfer token. When the token is created we will just need to run `yarn strapi transfer --to :heroku-url` and paste the transfer token when prompted. You'll notice that all the data is present in your production enviroment as well as the roles you set up in development.

Transfer Data Between Environments with Strapi Data Transfer

Now that we've got a functioning application it's time to deploy. For Strapi, we'll use Heroku to deploy for production. To do so we will need to create a production environment file for `database.js` and `server.js`, then we log into Heroku and created a new app. Once logged in we will set all the environment variables in Heroku. Finally, we pushed our code to Heroku and saw our application running live on the web.

Deploy a Strapi Application to Heroku

cloudinary

We'll learn how to integrate Cloudinary into our Strapi application so that we can upload image assets in our Strapi dashboard and the resulting image will be stored in Cloudinary and ready for use in our application.

We'll create a `config/plugins.js` file and paste in some config code that sets Cloudinary as the provider with `cloud_name`, `api_key`, and `api_secret`.

Then we'll install the Cloudinary provider plugin with `npm install @strapi/provider-upload-cloudinary`, add the middleware security that lets us use Cloudinary, and finally add Cloudinary credentials to our `.env` file.

Install and Add the Cloudinary Provider to a Strapi Application

We'll cover the basics of building a new page in Next.js, specifically an "About Us" page. If you've used Next before, this will feel very similar to creating a new page in the `pages` directory. 

While doing this we will also be introduced to some of the components that come with the template, such as the `PageContainer`, `Main`, `Title`, and `Description` components which you can check out [in more detail here](https://github.com/malgamves/next-13-website-template/tree/main/components).


Create an About Us Page inside the Next.js 13 App Directory

GraphQL is a powerful technology that lets you query the exact data that you need when you need it on your front end. Strapi allows for GraphQL integration through a plugin.

You can install GraphQL with the following command:

```bash
yarn strapi install graphql
```
You'll notice this adds GraphQL in your plugins tab within Strapi. 

When that is installed you can navigate to `/graphql` in your Strapi project and query the data you have. 

Install and Query GraphQL API in Strapi

We have a problem right now in that if you were to try and fetch these posts from the API, you would get a 403 error. And that's because we haven't set up the end user permissions for the API to be able to access this content. So let's go ahead and do that now.

We'll modify the User permissions for the `Post` role and give public access to `find` and `findOne`.

Set up API End User Permissions to Make REST Queries in Strapi

The first thing we want to do is model the content that will be presented on our website. To do this we'll create a Post content type that will have several fields with types including text, rich text, date, and UID. 

After we create the content type you'll see how to manually create posts through the Strapi dashboard.

Create a Collection Type for a Post in Strapi

We'll start with the basics of creating a new Strapi project, naming the project API, and setting up the project to use an SQLite database.

Creating a New Strapi Project for Development with npx

In this course, Chris takes a README-driven approach to build out a Rust CLI that supports content authoring for a digital garden.

Specifically, you'll work through the first command for the CLI, writing files. You start by initializing a new Cargo binary crate through creating the `write` command that allows you to create files (and their contents) to the folder that you set. 

Step by step you'll learn about:
- Handling user input
- Error handling
- Rust Move semantics
- Rust Copy semantics
- Integration testing

...and more!

Rust is a systems programming language that runs blazingly fast, prevents segfaults, and guarantees thread safety.

rust

Creating a Digital Garden CLI with Rust

While we have `test_write_with_title`
which tests the garden CLI with a given
title flag, we can also set up additional
tests, such as for using a written title
in the document for the filename.

Copy/pasting the code from test one into
test two is a lot of duplication, and
setting up the fake editor isn't going
to change across tests, so let's abstract
the creation of the temporary directory,
the fake editor script path, and the
`Command` itself into a utility function.

The port is relatively straightforward,
with the only changes being that we cut
off the `cmd` modifications before the
`.arg` calls, and now have to return
the `cmd` and the `temp_dir` in a tuple.

We return the actual `Command` and `TempDir`,
yielding ownership to the calling function
because we have no further use for them here,
but the calling function will need to mutate
the `Command` more, and use `TempDir` to test.

```rust
fn setup_command() -> (Command, assert_fs::TempDir) {
    let temp_dir = assert_fs::TempDir::new().unwrap();

    let mut cmd = Command::cargo_bin("garden").unwrap();
    let fake_editor_path = std::env::current_dir()
        .expect("expect to be in a dir")
        .join("tests")
        .join("fake-editor.sh");
    if !fake_editor_path.exists() {
        panic!(
            "fake editor shell script could not be found"
        )
    }

    cmd.env("EDITOR", fake_editor_path.into_os_string())
        .env("GARDEN_PATH", temp_dir.path());
    (cmd, temp_dir)
}
```

The new write tests are considerably shorter
now and focus mostly on which subcommand to
call, and what the user interaction with that
subcommand is.

```rust

fn test_write_with_title() {
    let (mut cmd, temp_dir) = setup_command();
    let assert = cmd
        .arg("write")
        .arg("-t")
        .arg("atitle")
        .write_stdin("N\n".as_bytes())
        .assert();

    assert.success();

    temp_dir
        .child("atitle.md")
        .assert(predicate::path::exists());
}

fn test_write_with_written_title() {
    let (mut cmd, temp_dir) = setup_command();
    let assert = cmd
        .arg("write")
        .write_stdin("N\n".as_bytes())
        .assert();

    assert.success();

    temp_dir
        .child("testing.md")
        .assert(predicate::path::exists());
}
```

Abstracting test utilities inside integration test files

We can print lines to the terminal and
ask the user for various input. So let's
make the interface a bit more usable by
using colored output.

We can do this by bringing an extension
trait for formatting into scope:
`OwoColorize`.

Extension traits are one-off traits that
are used to add functions to a type that
our crate doesn't own. A crate can create
the new trait, then implement the
functions defined by that trait on a type
the crate doesn't own and if we bring that
trait into scope, the type will get the
extra functions. In this case, `OwoColorize`
is used for adding color functions to
formatters like Display and Debug.

We'll start with `ask_for_filename`

```rust
use color_eyre::owo_colors::OwoColorize;

fn ask_for_filename() -> Result<String> {
    rprompt::prompt_reply_stderr(&format!(
        "{}",
        "\
Enter filename
> "
        .blue()
        .bold(),
    ))
    .wrap_err("Failed to get filename")
    .map(|title| slug::slugify(title))
}
```

In `confirm_filename` we can use multiple
formatters to get different colors:

```rust
let result = rprompt::prompt_reply_stderr(&format!(
    "\
{} {}
Do you want a different title? (y/N): ",
    "current title:".green().bold(),
    raw_title,
))
.wrap_err("Failed to get input for y/n question")?;
```

Using owo_colors Extension Traits to colorize terminal output

`PathBuf` lets us check to see if a file
exists before writing to it, so we can
take advantage of that to make sure that
we don't overwrite a file that already
exists, even if the user has indicated
a title that would overwrite another file.

```
let mut i: usize = 0;
loop {
    let dest_filename = format!(
        "{}{}",
        filename,
        if i == 0 {
            "".to_string()
        } else {
            i.to_string()
        }
    );
    let mut dest = garden_path.join(dest_filename);
    dest.set_extension("md");
    if dest.exists() {
        i = i + 1;
    } else {
        fs::rename(garden_tmpfile, &dest)?;
        break;
    }
}
```

Checking for file existence with PathBuf to ensure unique filenames

We have been ignoring implementing tests for the write subcommand. Now is the time that we'll test the command we have been building.

We'll build out a fake editor script that our test will use to check if the command is functioning properly. We will then use `arg` to build the command as we would normally use it ourselves.

After writing the test, we'll see how to clean up after ourselves so that we aren't leaving testing artifacts leftover just from running a test.

---

How to integration test a cargo binary that calls
out to `EDITOR` for modifications and asks the
user for input.

First we'll create a fake editor script to use
instead of vim, etc.

`tests/fake_editor.sh` is a one-liner.

```shell
echo "testing" >> $1;
```

Then we'll write our test using `assert_cmd` to
build up a `Command` that points to the `garden`
binary in out `Cargo.toml`.

We grab the fake editor path script using
`std::env::current_dir()`, make sure the file
exists (which means we've grabbed the right
path), and spawn a new `Command` with the
`EDITOR` variable set to the fake editor
script. The equivalent call to our bin is

```shell
EDITOR=path/to/fake-editor.sh garden write -t atitle
```

And the rust code to execute.

```rust
fn test_write_with_title() {
    let mut cmd = Command::cargo_bin("garden").unwrap();
    let fake_editor_path = std::env::current_dir()
        .expect("expect to be in a dir")
        .join("tests")
        .join("fake-editor.sh");
    if !fake_editor_path.exists() {
        panic!(
            "fake editor shell script could not be found"
        )
    }

    let assert = cmd
        .env("EDITOR", fake_editor_path.into_os_string())
        .arg("write")
        .arg("-t")
        .arg("atitle")
        .write_stdin("N\n".as_bytes())
        .assert();

    assert.success();
}
```

Note that we're asserting that the command
exited sucessfully, and that we're inputing
`N\n` to the bin's stdin, which responds to
a request for user input after the editor
closes.

Note that this doesn't actually test for
the resulting file existence, and also that
we're writing our files out directly into
our default digital garden! We can solve
this with `assert_fs` and `predicates`.

We'll bring in both preludes (a prelude is
a bunch of stuff the library author thinks
will be useful to have around all the time)
and create a new temporary directory.

We'll use the `GARDEN_PATH` environment
variable to pass the temporary directory in
to the `garden` bin and that's it, we've
added a temporary directory to our test.
No more polluting the default garden!

```rust
use assert_fs::prelude::*;
use predicates::prelude::*;

fn test_write_with_title() {
    let temp_dir = assert_fs::TempDir::new().unwrap();

    let mut cmd = Command::cargo_bin("garden").unwrap();
    let fake_editor_path = std::env::current_dir()
        .expect("expect to be in a dir")
        .join("tests")
        .join("fake-editor.sh");
    if !fake_editor_path.exists() {
        panic!(
            "fake editor shell script could not be found"
        )
    }

    let assert = cmd
        .env("EDITOR", fake_editor_path.into_os_string())
        .env("GARDEN_PATH", temp_dir.path())
        .arg("write")
        .arg("-t")
        .arg("atitle")
        .write_stdin("N\n".as_bytes())
        .assert();

    assert.success();

    temp_dir
        .child("atitle.md")
        .assert(predicate::path::exists());
}
```

Now that's enough, but we can also check
to make sure the file we expect to exist
actually exists. `temp_dir.child` gives
us the possible file, and we can assert
on that file with a test from `predicates`
that tests to make sure the file exists.

```rust
temp_dir
    .child("atitle.md")
    .assert(predicate::path::exists());
```

Using assert_fs and predicates to integration test with a real temporary file system

What user experience do we want for the
act of writing?

We have a few options, including letting
users write directly in the console or giving
them a prepared file that they can then open
and write in.

Writing directly in the console doesn't give
a user access to their text editor of choice.
Which means lacking autocomplete, syntax
highlighting, snippets, and other functionality.

Creating a file and handling the path back to
the user doesn't quite work for us either, as
that reduces the utility of the garden write
command to the equivalent of running `touch
filename.md`.

So for our use case, we'd ideally like to open
the user's preferred editing environment,
whatever that happens to be, then wait for them
to be done with the file before continuing.

Since we're working with a CLI tool, we
can safely assume that our users either
know how to, or are willingly to learn how
to, set environment variables. This means we
can use the `EDITOR` variable to select an
editor to use. This is the same way `git commit`
works.

---

In Rust, there is a crate that handles not only
`EDITOR`, but also various fallbacks per-platform.
We'll take advantage of [edit](https://crates.io/crates/edit)
to call out to the user's choice of editor.

The quick usage of edit allows us to call the
`edit::edit` function, and get the user's data.

```rust
pub fn write(garden_path: PathBuf, title: Option<String>) -> Result<()> {
    dbg!(garden_path, title);
    let template = "# ";
    let content_from_user = edit::edit(template).wrap_err("unable to read writing")?;
    dbg!(content_from_user);
    todo!()
}
```

This results in a filename that looks like:
`.tmpBy0Yun` "somewhere else" on the filesystem,
in a location the user would never reasonably
find it. Ideally, if anything went wrong, the
user would be able to take a look at the
in-progress tempfile they were just working on,
which should be in an easily discoverable place
like the garden path.

*We don't want to lose a user's work.*

Additionally, the tempfile doesn't have a file
extension, which means that the user's editor
will be less likely to recognize it as a
markdown file, so we want to add `.md` to the
filepath.

```rust
use color_eyre::{eyre::WrapErr, Result};
use edit::{edit_file, Builder};
use std::io::{Read, Write};
use std::path::PathBuf;

const TEMPLATE: &[u8; 2] = b"# ";

pub fn write(garden_path: PathBuf, title: Option<String>) -> Result<()> {
    let (mut file, filepath) = Builder::new()
        .suffix(".md")
        .rand_bytes(5)
        .tempfile_in(&garden_path)
        .wrap_err("Failed to create wip file")?
        .keep()
        .wrap_err("Failed to keep tempfile")?;
    file.write_all(TEMPLATE)?;
    // let the user write whatever they want in their favorite editor
    // before returning to the cli and finishing up
    edit_file(filepath)?;
    // Read the user's changes back from the file into a string
    let mut contents = String::new();
    file.read_to_string(&mut contents)?;

    dbg!(contents);
    todo!()
}
```

We will use the `edit::Builder` API to generate
a random tempfile to let the user write content
into. The suffix is going to be `.md` and the
filename will be 5 random bytes. We also put
the tempfile in the garden path, which ensures
a user will be able to find it if necessary.

`wrap_err` (which requires the `eyre::WrapErr`
trait in scope) wraps the potential error
resulting from these calls with additional context,
making the original error the source, and we can
keep chaining after that. We `keep` the tempfile,
which would otherwise be deleted when all handles
closed, because if anything goes wrong after the
user inputs data, we want to make sure we don't
lose that data.

After the file is created, we write our template
out to the file before passing control to the
user. This requires the `std::io::Write` trait
in scope. We use a const for the file template
because it won't change. To make the `TEMPLATE`
a const, we also need to give it a type, which
is a two element byte array. Change the string
to see how the byte array length is checked at
compile time.

Since we have access to the `file` already, we
can read the contents back into a string after
the user is done editing. This requires having
the `std::io::Read` trait in scope.

And now we've let the user write into a file
which will stick around as long as we need it
to, and importantly will stick around if any
errors happen in the execution of the program,
so we lose no user data and can remove the
temporary file at the end of the program
ourselves if all goes well.

Passing control to the user's favorite editor (such as VSCode) from the CLI

Given the potential `document_title` we now
want to ask the user if the title is still
what they want to use so that we can write
the file to disk. This will involve two
paths depending on if we have an existing
potential option or not.

We will need to use `ask_for_filename` in
`confirm_filename`, so we'll write that branch
first.

```rust
let filename = match document_title {
    Some(raw_title) => {
        // confirm_filename()?
        todo!()
    }
    None => ask_for_filename(),
};
```

`rprompt` allows us to ask for a small amount
of input from the user (compared to the  much
larger input possible by passing control to
the user's editor).

We'll use `rprompt::prompt_reply_stderr` to
get a response from the user, and `.wrap_err`
to add context to the error if anything goes
wrong.

```rust
fn ask_for_filename() -> Result<String> {
    rprompt::prompt_reply_stderr(
        "\
Enter filename
> ",
    )
    .wrap_err("Failed to get filename")
}
```

We'll use the same behavior for the first
part of `confirm_filename`. We'll also
need to use our first lifetime to account
for the shared reference argument.

`match` can match against multiple values
for the same branch, so we'll take advantage
of that to handle branches for Ns and Ys, as
well as the default case. If anything goes
wrong, such as someone inputting an "a",
we'll fall through using `loop` and ask again
until we get a usable answer

```rust
fn confirm_filename(raw_title: &str) -> Result<String> {
    loop {
        // prompt defaults to uppercase character in question
        // this is a convention, not a requirement enforced by
        // the code
        let result = rprompt::prompt_reply_stderr(&format!(
            "\
current title: `{}`
Do you want a different title? (y/N): ",
            raw_title,
        ))
        .wrap_err("Failed to get input for y/n question")?;

        match result.as_str() {
            "y" | "Y" => break ask_for_filename(),
            "n" | "N" | "" => {
                // the capital N in the prompt means "default",
                // so we handle "" as input here
                break Ok(slug::slugify(raw_title));
            }
            _ => {
                // ask again because something went wrong
            }
        };
    }
}
```

While filenames can technically have spaces
in them, we're going to slugify our filenames
like urls for a few reasons. One is that
when starting out, many programmers fail to
quote filename arguments in their bash scripts,
which results in filenames with spaces being
treated as separate arguments. Another is
that this is a digital garden CLI, and
digital gardens are often stored in git,
accessed from multiple file systems, as well
as from URLs. While strictly speaking we don't
need to slugify our filenames, we will here
so as to adhere to a safer set of characters.

```
cargo add slug
```

We'll map over the `Result` from `rprompt`
which allows us to operate on the internal
value if it's `Ok`.

```rust
fn ask_for_filename() -> Result<String> {
    rprompt::prompt_reply_stderr(
        "\
Enter filename
> ",
    )
    .wrap_err("Failed to get filename")
    .map(|title| slug::slugify(title))
}
```

We'll also use slugify in `confirm_filename`

```rust
match result.as_str() {
    "y" | "Y" => break ask_for_filename(),
    "n" | "N" | "" => {
        // the capital N in the prompt means "default",
        // so we handle "" as input here
        break Ok(slug::slugify(raw_title));
    }
    _ => {
        // ask again because something went wrong
    }
};
```

The fact that `confirm_filename` and
`ask_for_filename` have the same return type
is important because the branches of a `match`
need to return the same type.

Prompting the user for input with rprompt and loop

We want to build out functionality to let the user
write into a file in their garden. To do that, we
have to know the location of their garden.

We'll start by implementing a function to get the
default garden path.

```rust
fn get_default_garden_dir() -> Result<PathBuf> {
    let user_dirs = UserDirs::new().ok_or_else(|| eyre!("Could not find home directory"))?;
    Ok(user_dirs.home_dir().join(".garden"))
}
```

This uses the user's system directories via the
`directories` crate to find their home dir. We
then add `.garden` to the home dir for the default
garden.

Ideally, we would want the user to be able to
specify their garden path. Using structopt, we
can let them specify it as a short, long, or
env var.

In our README, we'll specify how this should work.

````

```shell
GARDEN_PATH=~/github/my-digital-garden garden write
garden -p ~/github/my-digital-garden write
garden --garden_path ~/github/my-digital-garden write
```
````

And in our `Opt` struct, we can tell structopt how to
handle it. Putting this in our `Opt` struct instead of
our `Command` enum make it a global flag that can be
specified for all subcommands.

```rust
struct Opt {
    #[structopt(parse(from_os_str), short = "p", long, env)]
    garden_path: Option<PathBuf>,

    #[structopt(subcommand)]
    cmd: Command,
}
```

structopt is only handling the user input, so in
`main()` we still need to fall back to the
default if the `garden_path` is not specified.

```rust
let garden_path = match opt.garden_path {
    Some(pathbuf) => Ok(pathbuf),
    None => get_default_garden_dir().wrap_err("`garden_path` was not supplied"),
}?;
```

if we force an `Err` in `get_default_garden_dir`
We can see how the wrapped errors end up stacking.

```
Error:
   0: `garden_path` was not supplied
   1: Could not find home directory
```

We'll also change the signature of `write` to
accept the pathbuf.

```
Command::Write { title } => write(garden_path, title),
```

Note that we need to import `PathBuf`. We can also
`dbg!` the garden path and title to see their
values.

```rust
use std::path::PathBuf;

pub fn write(garden_path: PathBuf, title: Option<String>) -> Result<()> {
    dbg!(garden_path, title);
    todo!()
}
```

Adding support for global flags that apply for all subcommands with structopt

In our subcommand match we'll add a new function called
`write` to handle our `Write` subcommand. We'll pass it
the argument title and we'll "import" it from a library
called `digital_garden`. 

```rust
use digital_garden::write;

fn main() -> Result<()> {
    color_eyre::install()?;

    let opt = Opt::from_args();
    dbg!(&opt);
    match opt.cmd {
        Command::Write { title } => write(title),
    }
}
```

To create this library, add a new section in `Cargo.toml`.
We can give our library a name and set which file to treat
as the entrypoint. `src/lib.rs` is the default file for
a library crate, but we can also be explicit about what we
want to happen.

```toml
[lib]
name = "digital_garden"
path = "src/lib.rs"
```

This leaves us with two crates, a binary crate, of which we
can have any amount, and a library crate, of which we can
only have 0 or 1.

We'll create the `src/lib.rs` file with the following
contents.

```rust
mod write;

pub use write::write;
```

Rust's module system is explicit, so when we write
`mod write`, we are saying that the library `digital_garden`
has a module `write`, such that the path to the module is
`digital_garden::write`.

In our case `digital_garden::write` corresponds to a file
at `src/write.rs`.

Secondly, the `pub use write::write` says that we have a
function called `write` in the module called `write` and
that the function will be exposed from lib publicly, so
people who have the `digital_garden` library installed
will be able to use the write function as
`digital_garden::write`.

In JavaScript the closest analogue is exporting a function
from another file that exports it.

```js
export { write } from './write.js';
```

By using use, we're shortening the public path to the write
function. A private module "write" with a function "write"
also exists at `digital_garden::write::write`. If you try
to use this path, Rust will tell you there is a private
module there.

Finally, in `src/write.rs`, we can write a public function
that matches the return type of our main function, since
we're using it as the return value from our match function.

We'll accept an `Option<String>` as an argument as well,
since that's what we expect from the cli arguments.

```rust
use color_eyre::Result;

pub fn write(_title: Option<String>) -> Result<()> {
    todo!()
}
```

The purpose of creating this module path is because we
know that we're going to set up more modules for each
subcommand in the future, and we can control both the
placement of our code on disk, as well as the path users
will interact with to use our module.

Creating a library crate to support a Rust binary crate in the same package

Hardcoding your script to search for only one type of file (like `.md`) is a major limitation. This lesson shows you how to make your script smarter by teaching it to infer the desired file type directly from the user's natural language prompt.

You'll implement a two-stage AI process. The first stage analyzes the user's request to determine the file extension they're interested in (e.g., `md`, `txt`). The second stage then uses that file type to dynamically search the file system, find the relevant files, and execute the command.

**Workflow demonstrated in this lesson:**

* Add a preliminary `generateObject` call to extract a `fileType` from the user's initial prompt.
* Define a list of supported `fileTypes` (e.g., `['md', 'txt']`) and use a detailed prompt to guide the AI, instructing it to choose from the list or use a default.
* Dynamically construct the `globby` pattern using the inferred `fileType` (e.g., `*.$[fileType]`).
* Implement error handling to inform the user if they request an unsupported file type or if no files of the specified type are found.
* Demonstrate how the script defaults to searching for Markdown files but can correctly search for other types when specified (e.g., "summarize my profile from my pdf").

This technique adds a significant layer of intelligence to your script, allowing it to adapt its behavior based on the user's intent and making it a far more flexible and intuitive command-line tool.

John Lindquist

Dynamically Inferring File Types from User Prompts in Local AI Scripts

Hardcoding command logic and prompts directly into your script makes it rigid and difficult to extend. This lesson demonstrates a more flexible and powerful pattern: defining your script's commands as individual Markdown files in a dedicated directory.

This approach allows you to easily add, remove, or modify commands without touching the core script logic. You'll learn how to dynamically load these command files, present them as options to the AI, and inject the corresponding instructions into your prompt, creating a truly modular and extensible tool.

**Workflow demonstrated in this lesson:**

* Create a `commands` directory to house your command definitions (e.g., `summarize.md`, `translate.md`).
* In each file, write the specific prompt or instructions for that command.
* Use `globby` to discover all available command files at runtime.
* Create a `commandMap` that maps the filename (e.g., "summarize") to its file content.
* Update the Zod schema to use an `enum` of the dynamically discovered command names.
* Dynamically construct the final text generation prompt by injecting the instructions from the user-selected command file.
* Add a `profile.md` file to demonstrate how the script can act on user-created content with the new file-based commands.

By the end of this lesson, your script will be architected for easy expansion, allowing you to build a robust library of custom commands simply by adding new text files.

Creating Dynamic, File-Based Commands for Local AI Scripts

AI models won't always give you the answer you expect; sometimes they'll return invalid data or "hallucinate" a response that doesn't fit your schema. This lesson teaches you how to make your local AI script more robust by implementing essential error handling for user commands.

You'll learn how to validate the AI's output against a predefined list of valid commands and gracefully catch errors when the model generates an unexpected response, ensuring your script fails predictably and provides helpful feedback to the user.

**Workflow demonstrated in this lesson:**

* Define a list of `validCommands` (e.g., `summarize`, `translate`, `review`) to constrain the AI's choices.
* Wrap the `generateObject` call in a `try...catch` block to handle cases where the AI's output doesn't match the Zod schema.
* Implement a type guard function (`isValidCommand`) to check if the command returned by the AI is in your list of valid commands.
* Provide clear error messages to the user, indicating what went wrong and what the valid commands are.
* Test the script with invalid commands (like "taco" or "delete") to confirm that the error handling works as expected.

By adding these checks, you make your script significantly more reliable and user-friendly, preventing it from crashing on bad input and guiding the user toward correct usage.

Handling Invalid Commands and AI Errors in Local Scripts

Once your script can understand a user's command and identify a target file, the next step is to perform an action. This lesson shows you how to use the Vercel AI SDK's `generateText` function to process a file's content and produce a relevant, AI-generated response.

You'll learn how to read the content from the file path you've already identified, combine it with the inferred command into a new, more detailed prompt, and then pass it back to your local AI model to generate a final text output, such as a summary or a review.

**Workflow demonstrated in this lesson:**

* Import the `generateText` function from the `ai` SDK.
* Read the file content from the `filePath` returned by `generateObject`.
* Construct a new, structured prompt that includes the original command (e.g., "summarize") and the full content of the file.
* Pass this new prompt to the `generateText` function.
* Log the resulting AI-generated text to the console.
* Run the script with commands like "Summarize our TypeScript config" or "Review my package file" to see the full, context-aware output.

This lesson completes the core cycle of your AI script: understanding a command, reading the relevant context, and generating a useful, text-based result, all on your local machine.

Generating Text with a Local AI Model Based on File Content

A command is useless without knowing what to act upon. This lesson elevates your local AI script by giving it context from your project's file system. You'll learn how to find all relevant files and empower the AI to intelligently select the correct one based on your prompt.

The core technique involves using the `globby` package to scan for files and then feeding that list into a Zod `enum`. This constrains the AI's choices to a valid set of files, allowing it to accurately determine which file you're referring to in a natural language command.

**Workflow demonstrated in this lesson:**

* Install and import the `globby` library to find files based on a pattern (e.g., all `.json` files).
* Add a `filePath` field to your Zod schema.
* Define the `filePath` as `z.enum(files)`, where `files` is the array of file paths returned by `globby`.
* Pass a natural language prompt like "Summarize our typescript config" to the script.
* Observe how the AI correctly identifies `tsconfig.json` from the list of available JSON files and returns it as structured data.

This lesson is a crucial step toward building powerful, context-aware tools that can intelligently interact with your project's files based on simple, human-readable commands.

Providing File System Context to Local AI Scripts with Globby and Zod Enums

Hardcoded prompts are great for testing, but a truly useful script needs to respond to dynamic user input. This lesson shows you how to make your local AI script interactive by capturing command-line arguments.

You'll learn how to use Node.js's built-in `parseArgs` utility to take a user's natural language command directly from the terminal and pass it to your local language model for processing.

**Workflow demonstrated in this lesson:**

* Import the `parseArgs` function from the `node:util` module.
* Configure `parseArgs` to accept positional arguments from the command line.
* Replace the static, hardcoded prompt with the dynamic input provided by the user.
* Run the script with different sentences (e.g., `"Download a file"` or `"I want to download a file"`) to demonstrate that the AI can correctly infer the core command regardless of the phrasing.

By the end of this short lesson, your script will be able to accept and process commands directly from the user, making it a flexible and reusable command-line tool.

Processing Dynamic User Input in Local AI Scripts

While command-line chats with local AI models are useful, the real power comes from scripting. This lesson walks you through the initial setup for creating powerful, local AI-driven scripts by combining Ollama, the Vercel AI SDK, and Zod.

You'll learn how to take a natural language command, like "Concatenate all of the markdown files in my Downloads folder into a single file," and use the `generateObject` function to intelligently extract structured, actionable data from it.

**Workflow demonstrated in this lesson:**

* Install and set up the `ollama-ai-provider`, `ai`, and `zod` packages.
* Create a local AI model instance using Ollama and the Gemma 3 model.
* Define a Zod schema to specify the exact data structure you want to extract (e.g., `{ command: string }`).
* Use `generateObject` to pass a natural language prompt to the local model and receive a structured JSON object that matches your schema.
* Run the script using `bun` to see the local model successfully infer the "concatenate" command from a full sentence.

This foundational lesson unlocks the ability to build sophisticated, automated workflows by translating human language into machine-readable commands, all running entirely on your local machine.

Scripting Local Language Models with Ollama and the Vercel AI SDK

### **Build Powerful, Local AI-Driven Command-Line Scripts**

Go beyond simple AI chats and unlock the true potential of local language models by learning to script them. This course will guide you step-by-step through the process of building a flexible and intelligent command-line tool that can understand natural language, interact with your file system, and automate complex tasks—all running entirely on your local machine.

Starting with a blank TypeScript file, you'll learn how to combine the power of **Ollama**, the **Vercel AI SDK**, and **Zod** to create a script that's not just functional, but smart. We'll begin by teaching the AI to parse a simple command from a sentence. From there, we'll progressively add layers of sophistication: processing dynamic user input, making the script aware of your project's files, and generating meaningful text-based output.

You'll also learn critical patterns for building robust and extensible tools. We'll cover how to handle invalid commands and unexpected AI responses gracefully. Then, you'll refactor the script to use a modular, file-based system for its commands, allowing you to easily add new functionality without touching the core logic. Finally, we'll add a layer of intelligence that allows your script to infer exactly what you mean, right down to the type of file you want it to process.

By the end of this course, you will have built a powerful, personalized AI assistant and gained the foundational skills to create sophisticated local automations for any task you can imagine.

**What You'll Learn:**

* How to script local language models using Ollama and the Vercel AI SDK.
* To parse and process dynamic user input from the command line.
* To use Zod schemas, including `enums`, to validate and structure AI model output.
* Techniques for providing file-system context to your scripts with `globby`.
* How to generate context-aware text based on file content.
* Robust error handling for invalid user commands and unexpected AI responses.
* To build a modular and extensible tool by defining commands in separate files.
* To intelligently infer user intent, such as dynamically determining file types from a prompt.

Build a local AI-powered command-line assistant from scratch. Learn to use Ollama, Vercel AI SDK & Zod to parse input, automate tasks, and run scripts offline.

This lesson demonstrates how to give Cursor direct access to your terminal output using the [Nx MCP extension](https://nx.dev/getting-started/ai-integration?utm_source=youtube&utm_medium=videos&utm_campaign=Nx-AI&utm_id=Nx-AI), enabling automatic error detection and fixing in monorepo projects.

**Workflow demonstrated in this lesson:**

- Encounter a runtime error when serving a Vite application in an Nx monorepo
- Install and configure the Nx Console extension with MCP integration
- Ask Cursor to check your terminal for errors and fix the underlying issues
- Watch as the AI accesses running task details and terminal output through Nx MCP tools
- Observe how it analyzes workspace relationships to identify incorrect imports and missing dependencies

**Key benefits:**

- Seamless Error Handling: AI gains direct access to terminal output and running task information
- Automatic Diagnosis: Accurately identify the root cause of errors without manual copying
- Context-Aware Fixes: Leverage workspace knowledge to implement proper solutions
- Reduced Context Switching: Eliminate the need to manually relay information between terminal and AI



Juri Strumpflohner

Enable Cursor to directly access the terminal to read the output and fix issues

See how Nx MCP gives AI coding assistants direct access to terminal output and running tasks, enabling automatic error detection and fixing.

This lesson demonstrates how to solve the predictability problem when using AI to generate code in monorepos by combining Cursor with Nx generators.

**Workflow demonstrated in this lesson:**

- Explore how Nx generators provide predictable scaffolding for new libraries
- Set up [Nx Console with MCP](https://nx.dev/features/enhance-AI?utm_source=egghead&utm_medium=videos&utm_campaign=Nx-AI&utm_id=Nx-AI) integration to expose generator capabilities to Cursor
- Use the AI assistant to brainstorm a new feature with the help of the Nx MCP tools
- Have Cursor leverage the Nx MCP tools to trigger the appropriate Nx generator for creating a React-based feature library
- Observe how the AI assistant takes the generated library and further integrates it into the codebase

**Key benefits:**

- **Predictable Code Structure**: Ensure generated libraries follow your project's and organization's conventions
- **Consistent Setup**: Maintain uniform configuration across TypeScript projects and package.json files
- **Guided Implementation**: Combine AI instructions with structured, predictable code generation

Enable Cursor to generate predictable project setups

Learn how to combine Cursor AI with Nx generators for predictable library creation in monorepos, ensuring consistent structure across your codebase.

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