Learn Docker, Reactfire 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

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

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

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

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

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.

Generate Custom .cursorrules for Your Project Based on Community Examples

The GitHub CLI allows you to search issues and filter on different criteria like amount of comments. This allows you to search for real-world situations and pains/solutions people have using different technology. 

With the GitHub CLI in hand we'll drop 100s of issues as context in Googles AI Studio and have Gemini determine the 10 most frequently asked questions. This will give us a sense of how people use a library and where people get hung up.

Search 100s of Issues with GitHub CLI as Context for AI Studio

I often find myself wondering how to use certain APIs in certain situations, and I have a couple of keywords that I try and search up on Google. 

What you can do in this situation is turn to GitHub and see how other people have actually used this code on public projects. The GitHub CLI enables us to run searches like this, and we can take the results and work on them programmatically.

Inside of Cursor with their Composer AI agent, we can search for the files we want, tell the agent to download them, and inspect them for better understanding of how those things work together.

Search All of GitHub's Code and Pass the Results to AI Agents in Cursor

Implement virtual scrolling to handle large datasets in an HTML table. Start with a basic table that renders 10,000 rows of data. The browser can struggle with this amount of DOM nodes to render at one time, especially on low end devices. 

We implement tanstack react virtual to render only the necessary amount of rows the user is viewing. The implementation includes proper column sizing, accessibility considerations, and convenient scroll controls, resulting in a smooth and performant table experience regardless of the dataset size.

Create a row virtualized HTML table with Tanstack Virtual

Learn how to implement virtual scrolling in React tables using TanStack Table and Tanstack Virtual with the useWindowVirtualizer React hook.

The most difficult tasks will require the strongest AI models. And those models simply aren't available as AI agents inside of your tooling. So the best workflows are to drag and drop the files you need to be changed and analyzed inside of a reasoning model such as OpenAI's O1 Pro, then letting it create a giant plan for your dumber AI agents to work on. The key here is that instead of just copying and pasting the entire plan into Cursors Composer or your AI agent, it's to create a file inside of your project and ask your agent to tackle the plan one step at a time. This allows you ample opportunity between each step to test and verify the micro steps along the way. Otherwise, as I've experienced many times, a session can go way off track and you burn a lot of time and burn a lot of AI requests Cursors Composer and it starts churning through a plan Cursors Composer and making lots of unwanted changes. Cursors Composer and it's to guide an AI agent to hold its hand along the way, especially through the most complex tasks. Cursors Composer and the most complex tasks Cursors Composer and the most complex tasks require a step-by-step plan.