Learn Alpine, Cloudinary and Turborepo

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

Turborepo is a fast build system for JavaScript and TypeScript.

turborepo

Turborepo is a task runner that helps speed up and manage your Monorepos. It's not a dependency management tool. It knows nothing about your dependencies. 

It speeds up Monorepos by using local and remote caching as well as incremental builds. It helps to manage your repo with very handy and easy-to-use scripts. 

Here is the [npm-workspaces repo](https://github.com/alejandronanez/npm-workspaces)

So let's hop in and learn about Turborepo!

Note: This course is a continuation of the course [Introduction to Monorepos with NPM Workspaces](https://egghead.io/courses/introduction-to-monorepos-with-npm-workspaces-c03f500b) where we initially build out the Monorepo that is used in this course. It is highly suggested to watch that course first before jumping into this one. 

What is Turborepo?

At the root of our project, we'll run `npm install turbo --save-dev` to install Turborepo.

We'll also look at how to configure Turborepo in the `turbo.json` file. We'll look into the [$schema](https://turborepo.org/schema.json) and how it affects our configuration. 

Install Turborepo in an Existing Monorepo

With our npm scripts, if utils can't build, then the dashboard and blog can't build or run their tests. Turborepo Pipelines changes that. Using the pipeline, we can set up caching, what our scripts depend on, and where we want our logs to be output. 

Also since we now have Turborepo installed and configured our pipelines, we can remove concurrently from our scripts and dependencies. 

Migrate NPM Scripts to Turborepo Pipelines

We are going to be going through Turborepo speeds up your builds and testing with caching. When a command is first run in Turborepo, it gets cached. This way if something has changed in one of your repos in your Monorepo, it will detect that change. For the repos that haven't changed, it will pull their info from the cache inside of `node_modules` instead of fully re-running that command. For the repo that has changed, it will run that command solely on that repo. 

Understand Turborepo Local Cache

It's fairly straight forward to create repo specific pipelines with Turborepo. The script goes like this: `{monorepo-name}/{repository}#{script-name}`. For example, if we wanted to run the typecheck command in just the dashboard repo, it would look like this: `@monorepo/dashboard#typecheck`. Lastly, we will make sure that it `dependsOn` the `utils#typecheck` pipeline. 

Create Repository Specific Turborepo Pipelines

A dependency graph is exactly what it sounds like! A visual graph created by Turborepo to show you your dependencies and what dependencies the projects rely on. For our Monorepo, we'll run `npx turbo run build --graph` in the root of our project and that will build the dependency graph for all of our projects.

We can also do one with a graphic but that requires the [Graphviz](https://graphviz.org/download/) library. We can edit our command to have some arguments: `npx turbo run build --graph=graph.pdf` and it will create a graphic in a .pdf file. 

Generate a Dependency Graph for Monorepos Using Turborepo

You can set up all of your specific pipelines in the turbo.json file, or you can filter through your pipelines in the terminal. If we wanted to run the `typecheck` command in just the `utils` package, our command would look like this: `npx turbo run typecheck --filter=@monorepo/utils`. You can remove `@monorepo` as long as the `utils` package is unique. 

Turborepo also gives you the option to filter your commands based on what files have changed on your branch using `--filter="[HEAD^1]". 

Now that we know a bit more about Turborepo, we can edit all of the scripts that we build in the previous course to use `turbo` instead of `npm`. 

Filter Pipeline Tasks Using Turborepo

By default, Turborepo will stop its execution if it runs into an error. We may not want that if we want Turborepo to continue in the projects that don't have an error. We can do this by adding the `--continue` flag to the end of our command. 

Prevent Turborepo Exiting Early on Failing Tasks

Another really good tool that Turborepo offers is the `--dry` flag. The `dry` flag will display details about the affected workspaces and tasks that would be run. It will help you to know what Turborepo is doing under the hood. 

Preview Turborepo Tasks at the Pipeline Level

The logs produced by Turborepo can get really really long. Turborepo give us a flag, `--output-logs` and if we add on the argument `new-only`, it shortens the logs to just the new information for the repositories that have changed. 

Shorten Turborepo Logs using --output-logs

Remote caching is key when coworking on a project. Using Turborepo, you can upload your cache to the cloud and use it everywhere else. For example, if your coworker switches to your branch and runs the build, they can have your most recent cache saving them time. 

We will start off by logging into Vercel and Turborepo then linking our Turborepo account to our Vercel account. Once that is done, we run `npx turbo link` to connect to our remote cache. Then in our Vercel dashboard, we will need to do some configuring to finish it up. 

Enable Remote Caching in Vercel

Now that we have Turborepo installed, configured, and we have some knowledge about it, let's deploy our two client-side applications, dashboard and blog, to Vercel. We'll link our Github to Vercel and point it towards our Monorepo. We'll then run through all of the configurations and commands needed to get our apps deployed. 

Deploy the Client-Side Apps in a Turborepo Monorepo to Vercel

Github Actions helps to automate the building, testing, and deploying of your applications. We'll configure our actions in the `ci.yml` file in a new directory called `.github/workflows`. When that file is configured, we can update our scripts in our package.json to run off of those. We will also need to make/set up our `TURBO_TOKEN` and our `TURBO_TEAM` which we will create on Vercel and Github. 

Enable Github Actions with Turbporepo Remote Caching

Now you might have a project that currently isn't in your Monorepo that you want to add to it. We can copy that project over into our `apps` folder and then update the `package.json` to update the name to make it consistent across our apps. We'll update the dev port to make sure it's not going to run on the same port as one of our other applications. Lastly, we'll update the build pipeline to include our new application. 

Migrate a VueJS Application to an Existing Turborepo Monorepo

Media is an important part in creating a good UX for potential customers of your store, but when serving large unoptimized images, you can end up giving them a poor experience with long page loads.

We can instead use Cloudinary to automatically optimize our images and resize them on the fly with Cloudinary's programmable media API to deliver a more performant online store.

* [Cloudinary URL-Gen for JS - cloudinary.com](https://cloudinary.com/documentation/sdks/js/url-gen/index.html)

Optimize Media with Cloudinary to Deliver Smaller and Performant Images

🚨The Cloudinary UIX has been deprecated. If you are using it, you need to replace it with the App. [Here](https://hygraph.com/docs/guides/integrations/connect-cloudinary#migration) is a step-by-step guide on how to do it. 🚨

We will take an existing UI extension, Cloudinary, clone it down, and create our own instance, installing it into our GraphCMS content editor. Once we add that Cloudinary asset as a field, we will select those images directly from our Cloudinary account and serve those images right inside of our app.

Add and Configure Cloudinary UI Extension to Add Images From a Cloudinary Media Library

Alpine.js offers you the reactive and declarative nature of big frameworks like Vue or React at a much lower cost.

You get to keep your DOM, and sprinkle in behavior as you see fit.

alpine

Welcome to this beginner level series on Alpine JS! 👋🏞

Alpine JS is a rugged, minimal framework for composing JavaScript behaviour in your markup. It brings declarative, reactive, data-driven nature of libraries like React or Vue to your HTML templates.

This is the series intro video. Let's get started!

Series Intro - Beginner Level Alpine JS

In this lesson, we build a little app that fetches dog photos from the dog.ceo API, based on a "breed" search field. We want the API call to happen again when the search value changes, and to do so we use the `$watch` property from Alpine JS, which lets us define what state property we want to watch, and what callback function to run when a change happens.

We also use the `debounce` modifier on the `x-model` property to avoid firing an API call on each keystroke.

Refetch API data when a state value changes with the `$watch` property in Alpine JS

Welcome to this beginner level series on Alpine JS! 👋🏞

Alpine JS is a rugged, minimal framework for composing JavaScript behaviour in your markup. It brings declarative, reactive, data-driven nature of libraries like React or Vue to your HTML templates.

In this series, you will learn about Alpine's handy **directives**. You'll discover how to define a component scope with `x-data`, run code on mount with `x-init`, attach event listeners with `x-on`, display data with `x-text`, decide wether or not to show an element with `x-show`, iterate through data with `x-for`, retrieve a DOM element with `x-ref`, learn how to achieve _two-way data binding_ with `x-model`, and much more.

You'll also learn how **"modifiers"** can be applied and chained to directives to provide additional functionality: control the animation transition of entering and leaving DOM elements, debounce an event listener, etc.

We'll also touch on what Alpine calls **"magic properties"**, which give you access to native browser events (`$event`), reference to DOM elements (`$refs`), and more.

You'll gradually discover Alpine's API and syntax, from building simple tabs to a mini app that fetches a dog picture based on a "breed" search field. 🐶

**Get ready to breathe some fresh air** - it's time to walk into cool, green Alpine pastures! 🏞

Sprinkle declarative, reactive behaviour on your HTML with Alpine JS

In this lesson, we see how the `x-model` directive makes achieving "two-way data binding" effortless in Alpine JS.

We see what it would take to recreate the same functionality with an `x-bind` directive for the input value, as well as an `@input` event listener which, thanks to an access to the browser's native events via the `$event` magic property, sets the state value to `$event.target.value`.

Keep a DOM input and state value in sync with the `x-model` directive in Alpine JS

You will learn how to create and manage complex, nested schemas using a real world example (room bookings with guest details) used throughout all lessons. All created schemas are tested against potential bugs and we verify error messages.

The course covers topics such as:

- Utilizing Zod's shape and unwrap features to perform partial validations without losing the overall schema structure.
- Managing optional, nullable and nullish properties.
- Implementing unions and intersections to handle different types of data.
- Ensuring type safety with sets and maps, including custom validation logic for primitive values.
- Creating custom error messages for improved user feedback.

Get a solid understanding of how to leverage Zod to enhance runtime data validation in JavaScript/TypeScript applications 🔥

Tomasz Ducin

Advanced Type Checks with Zod

Get a solid understanding of how to leverage Zod to enhance runtime data validation in JavaScript or TypeScript applications

Get started with MongoDB and Docker really quick 🔥 even if you have no backend/fullstack experience.

This lesson guides you through installing Docker Desktop and setting up a MongoDB image.

We explain Docker images and containers, and their roles in providing isolated software environments.

We run a MongoDB image in 2 ways: using the terminal and Docker Desktop

We demonstrates configuring port settings for a container and accessing the MongoDB shell (`mongosh`) to interact with the database.

We learn how to manage Docker containers, configure settings, and run commands inside the container.

Grab the CLI command: 🔥 `docker run --name some-mongo -d mongo:latest` 🔥

Install Docker to run local MongoDB Container

Learn to install Docker Desktop, set up a MongoDB image, and manage containers. Configure ports and access the Mongo shell

[https://github.com/johnlindquist/mcp-cursor-tool-starter/tree/main](https://github.com/johnlindquist/mcp-cursor-tool-starter/tree/main)

Cursor's AI is powerful, but it can't do everything. What if you could give it the ability to interact with your specific workflows, tools, and data? This lesson shows you how to do exactly that by building a custom MCP tool – and we'll do it in under two minutes!

We'll leverage the Model Context Protocol (MCP), an open standard that bridges the gap between AI agents (like Cursor's Composer) and your custom code. By creating an MCP tool, you're essentially giving Cursor a new skill.

Here's the breakdown:

Understanding MCP: We'll briefly cover what the Model Context Protocol (MCP) is and why it's the key to extending Cursor's functionality. In short, it enables communication between the AI and your tools in a standardized way.

Fast Track Setup: We'll use a ready-made starter project (a single TypeScript file and package.json) to skip the boilerplate and get straight to the core concepts.

Step-by-Step Integration: We'll walk through the simple process of:

Cloning the starter project.

Installing dependencies with pnpm install.

Running the starter server.

Adding the tool to Cursor's MCP settings (under Features > MCP).

Verifying that your tool is working

Real-World Example: We'll demonstrate the tool in action, using Cursor's Composer to create a new GitHub issue using the gh CLI, all driven by our custom MCP tool.

This lesson is about more than just building one tool; it's about understanding how to extend Cursor's capabilities to fit your unique development needs. You'll learn the foundational steps to create a whole ecosystem of AI-powered tools.

Build Your First MCP Tool in Cursor in Just 2 Minutes

Displaying data in tables has been notoriously hard to implement. Naive implementations of data tables will leave your app slow and janky and many table libraries out there are restricted to a few select UI frameworks they use or aren't very customizable.

Tanstack Table is a 'headless' option meaning you have complete control of the presentation while taking advantage of the data side of the equation.

You'll be able to fetch and display remote data, handle pagination, and implement row virtualization for better performance.

Once you've implemented basic functionality you can take your tables to the next level customizing cells with your own JSX components, adding features like toggling column visibility, and ensuring your table is mobile-friendly with CSS pseudo-elements.

By the end, you'll have the skills to create efficient, user-friendly tables that elevate your applications. You can take what you learn here (using React) to any of the popular modern UI frameworks that fit your project requirements.

Ian Jones

Headless, Mobile-friendly Data Tables with Tanstack Table and React

Learn to display, paginate, and virtualize data effectively in a table with Tanstack table and React 19.

Install `ghx` with `npm i -g @johnlindquist/ghx`

https://github.com/johnlindquist/ghx

You've copied and pasted code, set up your project, and... *error*.  "Stream is not readable."  Sound familiar?  Generic Google searches and outdated Stack Overflow answers often leave you more confused than when you started.  You need more than just documentation; you need *context*.  You need to see how real developers solve this *exact* problem.

This lesson shows you a better way to debug, using real-world GitHub examples and the power of Cursor's Composer.

Here's the workflow we'll cover:

1.  **Identify the Error:** You've already done this!  You have a specific error message ("Stream is not readable") and the code that's causing it.
2.  **Find Real-World Solutions with `ghx`:** We'll use `ghx`, a tool for searching GitHub, to find *actual projects* that have encountered (and solved!) this same issue.  No more generic advice – you'll see working code.
3.  **Consolidate and Analyze:**  `ghx` brings those relevant code snippets into a single, easy-to-read file.
4.  **Leverage Cursor's Composer:** We'll use Cursor's Composer to directly interact with the examples and your problematic code.  Paste in your error, reference the `ghx`-generated file, and ask targeted questions like:
    *   "Based on these examples, how should I modify my code to fix the 'stream is not readable' error?"
    *   "What's the common cause of this error in the context of an MCP server?"
    *   "Show me the specific lines in the examples that handle stream readability correctly."
5. **Fix the Error:** Implement the solution using this workflow.

Instead of guessing, you'll learn directly from proven solutions.  You'll gain a deeper understanding of your issue and how to troubleshoot it effectively.  This approach transforms debugging from a frustrating hunt into a targeted learning experience.

Fix Real-World Coding Errors with GitHub Context through ghx and Cursor Composer

Learn how to test custom ESLint rules with Vitest and RuleTester.

In the [previous video](https://egghead.io/set-up-a-custom-es-lint-rule~kq49h), we created a custom eslint rule that gives us a warning when some of the class names don’t start with a lowercase letter.

Here, I'll show you how to test this or any other custom ESLint rule using unit tests without manually restarting the ESLint server or modifying production code to check if the rules work correctly. We'll look at the structure in which correct and incorrect code samples should be specific to RuleTester.

We'll also learn how to make our rule fixable and verify that the applied fix works as expected.

Test Custom ESLint Rules with Vitest and RuleTester

Learn to test a Custom ESLint Rule using Vitest and RuleTester.

AI is amazing for generating code, but often leaves us staring at functions and libraries we've never seen before. This creates a huge problem: When things break (and they will break), we're lost. We don't understand the individual pieces, let alone how they interact. Knowing one part isn't enough; we need to see how unfamiliar components work together.

The solution? Real-world examples.

This lesson introduces ghx, a tool I built to do exactly that. ghx searches GitHub, finding actual code examples that use the specific combinations of functions and libraries that are tripping you up. Instead of generic documentation, you get concrete implementations. You can find the ghx repository at https://github.com/johnlindquist/ghx.

Here's the workflow we cover:

Identify the Unknown: Pinpoint the AI-generated code you don't understand – specific functions, libraries, or their interactions.

Search with ghx: Use ghx to search GitHub for examples using those terms together. ghx finds repos where those elements co-exist.

Consolidate Examples: ghx combines the relevant code snippets from those examples into a single, digestible file.

Ask the AI (Again, but Better!): Feed this consolidated code to an AI (like a coding-focused model in an AI studio). Now, instead of asking general questions, you can ask targeted questions based on real-world usage:

"Explain how these functions are used together in these examples."

"What are some common use cases for this combination of libraries?"

"Show me alternative code snippets that achieve the same result."

"Can you identify potential problems or areas for improvement, based on these examples?"

Iterate. Use your improved understanding to make changes, and then repeat the process to debug.

By leveraging ghx and targeted AI prompting, we transform from being lost in a sea of unfamiliar code to actively learning from how others have solved similar problems. We gain a deeper understanding of our AI-generated codebase, making debugging, modification, and future development far more manageable. This gives you the tools to understand, debug and improve your own code.

How to Work with AI-Generated Code You Don't Understand with ghx and Gemini

In this less we create a custom Zod schema using `z.custom`.

The example case covers room numbers validation: it must start with an uppercase letter followed by three digits. `z.custom` allows the creation of a brand new custom schema with a callback function that returns a boolean indicating validity.

We highlight a challenge with type inference in Zod's custom schemas: callback parameter types don't get aligned automatically - type-safety must be explicitly defined using type parameters.

We run tests against various room numbers and demonstrate how to effectively validate inputs and manage error messages.


Create Custom Zod Schemas and Validate project-specific data

Create custom validation logic in Zod using z.custom, for primitive values, ensuring type safety and proper error messages for room numbers.


In this lesson we make use of partial validation of large and nested schemas in Zod.

Instead of creating many small schemas, which can complicate maintenance, Zod allows for the extraction of specific parts of a schema using the concept of ".shape". It "unwraps" the object schema, enabling access to sub-schemas within specific properties.

Additionally, we can use the `.unwrap` to get into element schemas of altered schemas such as `.optional` or `.array`.

Simplify working with complex schemas without the need to break them into hundreds of small schemas.

Use Zod Schemas partially using shape and unwrap

Manage large and nested Zod Schemas by extracting partial validations using shape and unwrap utilities

In this lesson we cover implementing sets and maps in Zod.

Using `z.set`, a schema for string-based amenities is created. We demonstrate how validation works by intentionally introducing errors and customizing error messages for better clarity.

Next, a map is created for existing guestDetails schema using `z.map`, with string keys and an existing `z.object`-based schema. The validation process is shown again by deliberately breaking the schema, such as omitting the age property, which triggers appropriate error messages.


Validate maps and sets using Zod schemas

Use sets and maps in Zod for schema validation, ensuring type safety for both keys and values.

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