Learn Docker, Greensock and Npm

A Monorepo is where you have multiple projects inside a singular repository.

monorepo

Introduction to Monorepos with NPM Workspaces

npm is the package manager for JavaScript. Find, share, and reuse packages of code from hundreds of thousands of developers — and assemble them in powerful new ways.

Thank you for completing my course!

You’ve learned what monorepos are, how to create them from scratch, how to install different projects inside of it, and how to run different scripts inside of a monorepo. 

So where do you go from here? I suggest creating your own Monorepo project template and publish it on Github. Take a look at Yarn Workspaces, pnpm, Learna, Nx and Turborepo for different monorepo tools. 

I also highly suggest Migrating some of your existing projects to a monorepo. Have a main frontend application that consumes a shared UI library package and a backend service. Another way would be to have a backend service, a mobile application and a frontend application. 

Best of luck and happy coding!

Intro to Monorepos with NPM Workspaces Outro

All of our applications and libraries have `test` and `typecheck` commands already written inside of them. We are able to run both the `test` and `typecheck` commands all at the same time as well. 

This has a downside. They don’t run at the same time, it’s just one after the other. To make them run at the same time, we will implement `concurrently` again.

Running Quality Checks in your Monorepo

We already have a utils package that outputs JavaScript. If we want to use that package inside any of our applications, in our case, blog or dashboard, we would have to install it first.

Share Code Between a Library and Applications Inside a Monorepo

Right now, it is not possible to run all the Dev Scripts across all the packages of the monorepo at the same time, because once we run `npm run dev` with the `-ws` flag, we will run the dev-command in the block first, but that command never ends. It is always watching for changes.

In order to run the `dev` scripts across all the packages in the monorepo, the first thing that we have to do, is to create a dev command for each one of the packages in the root package.json file. Then we can use `concurrently` to change them. We go `concurrently` and we tell it to run the utils, then the blog and then the dashboard scripts.

Run Dev Scripts for All Packages Simultaniously

We can install a dependency inside a monorepo by running npm install, then the name of the dependency, in our case, it’s going to be Lodash. But, we are going to add different flags to that.

If we use the `-w` flag, we would have to specify in what package we want to install Lodash. We will do this for `monorepo/utils`. We could also use the `-ws` flag to install Lodash across all the packages in the monorepo. We can also use `-W` in uppercase to install Lodash in the root of the project.

Install Dependencies for Specific Packages in a Monorepo

To run specific commands inside a monorepo we have to type `npm run` then the name of the command, `build`. We then add the flag `-w` for watch mode and specify which package we want to run the build command. In this case, it’s going to be the `utils` package. 

The full command will look like, `npm run build -2 @monorepo/utils`.

If you want to run all of the commands, you'll need to add the `-ws` flag. 

The full command for running all of our build commands will look like, `npm run build -ws`. 

Run App Specific Commands in a Monorepo

Creating a TypeScript library inside the monorepo is easy if we use tsup. To configure tsup, we have to set up the entry point. In our case, it's going to be the index.ts file inside the src directory.

We will also specify some extra options for tsup such as generating the types and source maps. We can also output different formats. In this case, we want to output an ESM file and a CommonJS file.

Add a TypeScript library to your Monorepo using TSUP

In order to create applications inside our monorepo, we can use different CLI tools like create-next-app or the Vite CLI. We'll install them inside the apps folder. We will create a blog and a dashboard app, one using create-next-app and the other using the Vite CLI.

It is also important to note that the only package-lock.json file that should exist, exists at the root of the project.

Install a Next.js and a Vite.js Applications Into a Monorepo

For this course, we are going to be using NPM Workspaces to configure our monorepo. We will have two workspaces, one for our apps and one for our libraries. We configure those in our package.json.

Configure NPM Workspaces in your Package.json

A monorepo is a group of small projects that can be combined to build bigger projects, or they could just be small projects that doesn't know anything about the other projects.

One great thing about monorepos is that the code is available for everyone at every time. If you are a frontend engineer, and you're working in the blog application and you see that there is a bug in the backend application, you can go and fix it right away.

What are Monorepos

JavaScript® (often shortened to JS) is a lightweight, interpreted, object-oriented language with first-class functions, most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic, and supports object-oriented, imperative, and functional programming styles.

javascript

There are some really known patterns to migrate packages in large applications. You might find some code referencing to a different package (such as `@my-namespace/my-package/next`) or specific branches (like `@my-namespace/my-package#next`). 

Anything that helps during package migrations or bumps having several majors of differences.

The good news is that you can have it done directly via NPM! What can be done in this case is an idea called "package alias". So you can have multiple versions of the same package running in your app and you can work on the upgrades mitigating possible issues.

Install Multiple Versions of the Same Package in NPM with Package Aliases



Docker containers wrap a piece of software in a complete filesystem that contains everything needed to run: code, runtime, system tools, system libraries – anything that can be installed on a server. This guarantees that the software will always run the same, regardless of its environment.

docker

The end is nigh. Your application is almost ready, and you will need to deploy it. And with the deployment, the endless tweaking to get everything running on your production servers. 

If only there were a way to test everything first. That is where containers will come in to save the day. With containers, you not only run your code in an environment similar to the production server, but you also run your code in precisely the same environment. 

In this talk, the attendees will learn about how to create container friendly applications and how to use containers to share their code with their team.

Containers for Software Developers

While "It works on my computer" might give you an excuse to close a bug ticket in the backlog, it doesn't really work when you're working on a team.

You need to ensure that your application can run regardless of what hardware your teammates are running or libraries they have installed.

Docker solves this problem by collecting everything your application needs to run into a container.

Containers aren't just for source code. They can hold configuration, scripts, and even their own filesystem. It's similar to a Virtual Machine, without having to bring a full operating system along for the ride.

Because containers run in isolation, there is some configuration involved to allow for files to be saved and containers to communicate with one another.

One of the most useful ways to get your head around Docker is to see it in action.

Follow along with Joel Lord as he works through the process of preparing an application to run in separate containers for the frontend and backend of an application that searches for gifs and re-encodes them with a caption supplied by the user.

You'll see how to configure the separate containers to communicate with one another, work with environment variables, and persist data to your local machine. Along the way, you'll pick up some tips on useful commands and bash scripting. Finally, you'll learn how to use Docker Compose to make it easy to run multiple containers simultaneously.


Containerize Full-Stack JavaScript Applications with Docker

Now that all three tiers of your application are available in containers, you'll need to push those images to container registries. Registries are the container equivalent to repositories for your code. Registries can be public or private. 

In this lesson, we'll use Docker Hub to push our images. Docker Hub repositories allow you share container images with your team, customers, or the Docker community at large.

Docker images are pushed to Docker Hub through the `[docker push](https://docs.docker.com/engine/reference/commandline/push/)` command. A single Docker Hub repository can hold many Docker images (stored as **tags**).

Images pushed to a public registry can be downloaded by using the `docker pull` command. Once they have the image downloaded, they can use Docker run to start each container just like you did in the previous steps.

Push a Docker Container Image to Docker Hub

In the last lesson [Create a Custom Image with a Dockerfile running Node.js](https://egghead.io/lessons/docker-create-a-custom-image-with-a-dockerfile-running-node-js?pl=introduction-to-docker-afaf), our backend stopped working again because it couldn’t find the database.

One of the advantages of running containers is that they run in isolation. As far as the container is concerned, nothing exists outside of it. It behaves as if it was its own operating system. This is why the backend can’t find the database on "localhost". The two containers are running on the same host but as far as the backend is concerned, there is no database. The same goes for the database. It can’t see anything outside of what is running in its container therefore it can’t see the backend application running.

When you need a way for multiple containers to talk to each other, Docker has a tool called networks to help you. We'll use a network to allow our backend to successfully connect to the database.

Allow Containers to Communicate through Docker Networks

You will learn how to create your own images that will include all the source code of your application, along with a specific version of Node.js and all the dependencies that are needed. Once you are done, you will be able to share this image with other people and this will ensure that everyone is running the same environment. It will event let people that don’t have Node.js run this server. This will also have the added benefit of cleaning up everything once you close the application.

You'll notice that we will run into the same error stating we can't connect to the database. This is expected and we'll fix this in [Allow Container to Communicate through Docker Networks](https://egghead.io/lessons/docker-allow-container-to-communicate-through-docker-networks)

Create a Custom Image with a Dockerfile running Node.js

First, we'll see how you can have different run-times installed in a docker container that isn't dependent on your system's current installs. You don't need anything but docker to run these containers! We will demonstrate this by installing the latest version of Node.js in a docker container and compare that to the version of Node.js I'm running on my machine.

Next, you'll see how to pass environment variables to containers. This is important because you will use these variables for API calls as well as configuring databases, and more!

Use Environment Variables for Docker Containers

In this lesson, you will learn how to start basic containers and how to use them in your day to day developer life. You will also learn how to start a database container that can be used with the demo application for this course.

Running Your First Container

First, we'll get a tour of the application and what it does then we will start configuring a fresh project. 

The application UI is simple. It asks for a keyword and will search and display a random GIF from Giphy. Once you are satisfied with the image, you will be able to add a caption to it. The server will deconstruct the GIF frames, add the caption and re-create the GIF for you. Everything is then saved to a database so that all the users can benefit from your creations.

The tech involved to do this is a Vue frontend, Node.js backend, and MySQL database. I'll walk you through cloning, installing dependencies, configuring the giphy API key that we use, and starting the application. You'll notice at the end we'll need to configure the database. We'll get an error but this is expected!

Configure and Run a Node.js API Powered by a Vue Application

Now that you have three containers ready to be used, you will learn how to create a docker-compose.yaml file. By using Docker Compose, you will be able to share a single YAML file and anyone will be able to run your application.

Docker-compose is part of the Docker tools suite and is an application that will take a YAML file to start all of your containers with one single command. 

Using Compose is basically a three-step process:

1. Define your app’s environment with a `Dockerfile` so it can be reproduced anywhere.
2. Define the services that make up your app in `docker-compose.yml` so they can be run together in an isolated environment.
3. Run `docker-compose up` and Compose starts and runs your applications. 

Compose works in all environments: production, staging, development, and testing.

Use Docker Compose To Share An Entire Application

This lesson explores how to use Cursor to generate and maintain English specifications as a form of living documentation. Learn how these automatically created documents can serve as a consistent and up-to-date representation of your code's behavior. The video highlights how reviewing and refining these English specifications can help ensure that code modifications align with the intended purpose and that documentation accurately reflects the current codebase, promoting better long-term code maintainability and clarity.

Here's the "english-spec.md" used in the video:

```markdown
> You are an AI specialized in analyzing source code and producing structured, plain-English representations of its functionality.  
>  
> **Your Task**:  
> 1. Read and interpret the code I provide.  
> 2. Produce a technical, English-only summary of what the code does.  
> 3. Use headings and bullet points to clearly outline the roles of each function, class, variable, or important logic block.  
> 4. **Do not include any actual code** in your final output. Instead, translate the code’s content into an English explanation (like a specification or documentation).  
> 5. Keep the explanation concise but thorough. Focus on explaining the purpose, process, and relationships between functions or modules in the code.  

**User Prompt to the AI**:

> **Instruction**: Convert the following code into a single file of pure English text that outlines exactly what each part of the code does. Use bullet points, headings, and descriptive language. Do not show any code—only explain how the code behaves. This is intended to serve as a human-readable spec or “translation” of the code.  
>  
> **Code**:  
> ~~~  
> [PASTE YOUR CODE HERE]  
> ~~~  

**Example Input/Output #1**:

- **Example Code (Input):**
  ~~~js
  // example1.js
  const greetUser = (userName) => {
    const greeting = `Hello, ${userName}!`;
    console.log(greeting);
  };

  greetUser("Alice");
  ~~~

- **Example Explanation (Output)**:
  ~~~
  # Overview
  - This file defines a single function which greets a user by name and prints a message to the console.
  
  # Function: greetUser
  - Accepts one parameter (`userName`).
  - Constructs a greeting string that combines static text with the `userName`.
  - Outputs the greeting using `console.log`.

  # Behavior Flow
  - The function is called with the argument "Alice".
  - Upon invocation, "Hello, Alice!" is logged to the console.

  # Key Points
  - Shows how to create a greeting with a dynamic name.
  - Demonstrates basic string interpolation and console logging.
  ~~~

**Example Input/Output #2**:

- **Example Code (Input):**
  ~~~py
  # example2.py
  class Calculator:
      def __init__(self, initial=0):
          self.value = initial
      
      def add(self, amount):
          self.value += amount
      
      def subtract(self, amount):
          self.value -= amount
      
      def get_value(self):
          return self.value

  calc = Calculator(10)
  calc.add(5)
  calc.subtract(3)
  result = calc.get_value()
  print(result)
  ~~~

- **Example Explanation (Output):**
  ~~~
  # Overview
  - This file defines a `Calculator` class that keeps track of an internal numerical value and provides methods to modify it.
  
  # Class: Calculator
  - **Constructor** (`__init__`):
    - Accepts an optional initial value (default is 0).
    - Stores the initial value in a `value` property.
  - **Method: add**:
    - Takes an `amount` and increments the internal `value` by that amount.
  - **Method: subtract**:
    - Takes an `amount` and decrements the internal `value` by that amount.
  - **Method: get_value**:
    - Returns the current internal `value`.
  
  # Behavior Flow
  - An instance of `Calculator` is created with an initial value of 10.
  - The `add` method is called with 5, raising the value to 15.
  - The `subtract` method is called with 3, lowering the value to 12.
  - The `get_value` method is called to retrieve the current value (12).
  - The result (12) is printed to the console.

  # Key Points
  - Demonstrates basic OOP with a class that manages state.
  - Showcases constructor, addition, subtraction, and retrieval of a stored value.
  ~~~  

---

## Final Prompt

Below is a full, combined version of the prompt you can provide to an AI to guide it in generating the English-only, bullet-pointed explanation. Feel free to modify or adapt this as needed:

~~~
You are an AI specialized in analyzing source code and producing structured, plain-English representations of its functionality.

Your Task:
1. Read and interpret the code I provide.
2. Produce a technical, English-only summary of what the code does.
3. Use headings (e.g. # Overview, # Functions, # Classes, # Behavior Flow) and bullet points.
4. Do not include any code in your final output—only explain how the code behaves.
5. Keep it concise but thorough, focusing on purpose, process, and relationships between functions or modules.

Below are some examples of how code might be transformed into an English-only explanation:

Example Input:
-------------------
~~~js
// example1.js
const greetUser = (userName) => {
  const greeting = `Hello, ${userName}!`;
  console.log(greeting);
};

greetUser("Alice");
~~~
Example Output:
-------------------
# Overview
- This file defines a single function which greets a user by name and prints a message to the console.

# Function: greetUser
- Accepts one parameter (`userName`).
- Constructs a greeting string that combines static text with the `userName`.
- Outputs the greeting using `console.log`.

# Behavior Flow
- The function is called with the argument "Alice".
- Upon invocation, "Hello, Alice!" is logged to the console.

# Key Points
- Shows how to create a greeting with a dynamic name.
- Demonstrates basic string interpolation and console logging.


Example Input:
-------------------
~~~py
# example2.py
class Calculator:
    def __init__(self, initial=0):
        self.value = initial
    
    def add(self, amount):
        self.value += amount
    
    def subtract(self, amount):
        self.value -= amount
    
    def get_value(self):
        return self.value

calc = Calculator(10)
calc.add(5)
calc.subtract(3)
result = calc.get_value()
print(result)
~~~
Example Output:
-------------------
# Overview
- This file defines a `Calculator` class that keeps track of an internal numerical value and provides methods to modify it.

# Class: Calculator
- **Constructor** (`__init__`):
  - Accepts an optional initial value (default is 0).
  - Stores the initial value in a `value` property.
- **Method: add**:
  - Takes an `amount` and increments the internal `value` by that amount.
- **Method: subtract**:
  - Takes an `amount` and decrements the internal `value` by that amount.
- **Method: get_value**:
  - Returns the current internal `value`.

# Behavior Flow
- An instance of `Calculator` is created with an initial value of 10.
- The `add` method is called with 5, raising the value to 15.
- The `subtract` method is called with 3, lowering the value to 12.
- The `get_value` method is called to retrieve the current value (12).
- The result (12) is printed to the console.

# Key Points
- Demonstrates basic OOP with a class that manages state.
- Showcases constructor, addition, subtraction, and retrieval of a stored value.

---

**Now**: Please apply the same conversion process to the following code. Convert it into a plain-English document that describes what the code does in detail, using bullet points and headings. Create the document next to the original with the filename as `${fileName}.md`. Do not include any source code in the output—only the explanation:

[INSERT YOUR CODE HERE]
```

Programming in English with Cursor

In this lesson, you'll learn how to enhance the accuracy of components generated by Cursor Composer by aligning your project's package.json with Cursor's rules. We'll guide you through extracting the list of installed packages from your package.json and incorporating them into your .cursorrules file, along with brief descriptions of their functionalities. By providing Cursor with this contextual information, you'll enable it to generate components that are more precise and functional on the first attempt. The lesson includes demonstrations of component generation both with and without these enhancements, highlighting the improvements in accuracy and efficiency.

Boost Cursor Composer Accuracy: Integrating package.json with Cursor Rules

Speed up your bug-fixing process dramatically with Cursors Composer. Take a warning from your logs, search for relevant terms, then seamlessly import both the warning and search results into Composer. Add documentation and apply fixes to all search results in one go. This streamlined workflow resolves multiple issues rapidly, saving valuable development time.

Batch Fix Every File in Search Results with Cursor's Composer

 We map over the initial set of results then use `Promise.all` to efficiently fetch the details of each pokemon rendered in the table. This allows us to add more columns to our pokemon table.

Fetch and display row details with Promise.all in Tanstack Table

map over the initial set of results then use `Promise.all` to efficiently fetch the details of each pokemon rendered in the tanstack table

Create a custom hook to fetch remote data by passing a promise to the React 19 `use` hook. Then render this data in Tanstack Table by memoizing the results and passing them to `useReactTable`.

Render remote data in a Tanstack Table with React 19's use hook and a promise

Create a custom hook using React 19’s use to fetch data with a promise. Render it in a Tanstack Table by memoizing and using useReactTable.

You know when you're deep in the code trenches and need someone to challenge your assumptions? That's exactly what this is about.

Instead of waiting for someone to poke holes in your master plan, you can paste that strategy straight into AI Studio and let it pressure test your ideas. We're talking about using this tool to validate everything from your feature proposals to your ambitious refactoring strategies.

The real magic happens when you hit that microphone button. Now you're in a real-time verbal sparring match. It's like rubber duck debugging but the duck actually pushes back and makes you defend every architectural decision you've made.

Here's the workflow:
1. Drop your master plan into the system instructions
2. Tell the AI to challenge every assumption
3. Hit that mic and defend your choices like you mean it
4. Watch your plan either emerge stronger or get rightfully dismantled

This approach is perfect when you're:
- Stuck in your own head with a complex problem
- Need to validate if your approach holds water
- Want to catch edge cases before they catch you
- Looking for opportunities you might have missed because you're too close to the code

It's like having a senior dev on call 24/7, ready to challenge your ideas and push you to think deeper about your solutions.

Now go forth and get your ideas challenged. Your projects will thank you for it.


An Uncomfortable and Challenging Conversation with AI Studio

In this lesson, you'll learn how to effectively brainstorm your project ideas before working with AI agents. We'll cover:

1. The importance of having a clear plan before starting work with AI agents
2. Using traditional brainstorming techniques to gather your thoughts
3. Leveraging video recording to capture your brainstorming session
4. How to review and analyze websites for inspiration
5. Documenting likes, dislikes, and potential features for your project
6. Using AI tools (like AI Studio) to organize and process your recorded brainstorming session

The lesson emphasizes a modern approach to brainstorming that combines traditional techniques with video recording, making it easier to later process your ideas through AI tools. This method helps ensure that no ideas are lost and that your thoughts can be effectively organized by AI for future reference.

How to Brainstorm Your Project Features to Prepare for AI Agents

fast-check allows us to create composite generators that suit our testing needs. We'll generate two numbers to create a point simulating an X and Y and assert that the distance should always be greater than or equal to 0.

Generate composite objects with fast-check fc.record

Just using fast-check by itself can make you tests verbose and tough to read. Lucky for us there are many integrations with fast-check such as the `@fast-check/jest` package that will help remove some boilerplate from our test.

Tidy up your test with @fast-check/jest

Many people share .cursorrules files, but trying to craft your own from scratch is a huge pain. Instead, this lesson shows you how to grab ALL community-created .cursorrules files to build a custom rule set for your projects. We'll use Gitingest and Google AI Studio to gather these examples and generate an awesome starting point customized to your specific project.