Learn Flexbox, Next and Supabase

Dive into the world of real-time web development with this hands-on course! You’ll learn how to build a dynamic chat application using Fauna Streams and Next.js 14, focusing on the essentials and best practices.

**What You’ll Learn:**
- **Next.js Best Practices:** Master Next.js best practices for building fast, maintainable apps.
- **Server-Side Rendering (SSR):** Learn SSR techniques and master features like server actions, server side form submission etc.
- **Database Access Patterns:** Efficiently connect and retrieve data using Fauna.
- **Real-Time Updates:** Implement real-time updates with Fauna Streams.
- **Basic Authentication:** Secure your chat app with simple authentication methods.

**Course Highlights:**
- Practical, project-based learning.
- Clear, step-by-step instructions.
- Focus on modern, real-time web development.
- Tips and tricks for optimizing performance.

By the end of this course, you’ll have a working real-time chat app and a solid grasp of the tools and techniques used to build it.


Next.js is a minimalistic framework for server-rendered React applications.

Build a Real-time Next.js 14 Chat App with Fauna 

Our app is in a good stopping point. A good challenge for you to implement from here is to implement a navbar with a `logout` button.

Challenge: Implement a NavBar for the Application with Logout Functionality

Now that we have user Authentication, we can redirect users to sign in if we don't find a cookie to do so. 

Throughout our app we can now replace our fauna token we have set in environment variables to the user token that we have access to. 

Implement Auth Guards with Tokens throughout an Next.js App

React is one of the web’s most popular frameworks for building JavaScript applications. 

If you know what you’re doing, React can drastically simplify how you build, use, and maintain code.

Whether you’re a React newbie or you’re ready for advanced techniques, you can level-up with egghead. Explore our in-depth courses, lessons, and community resources to build more powerful applications and crack open your career.

react

For user signin, we'll implement a proper server action by creating a `actions.js` file where our server actions can live. In our login page, we'll set the component to be a client component to hold some state. This way we can use the new `useFormState` hook from `react-dom` and pass in our login server action.

Within the action we'll call our `UserLogin` function from Fauna and set a cookie with the secret we get back that we can use throughout our application.

Implement Fauna Signin with Next.js Server Actions and useFormState React Hook

Now it's time to actually sign users up for our appliaction.

We'll implement the `doSignup` function that we created earlier. This function will run on the server with the `'use server'` directive so we can instantiate a fauna client and call `UserRegistration` within this function.

From there we'll redirect the user to the `/signin` page which will error out because it's not implemented yet.

Implement Signup Functionality through Fauna with Next.js 14 Server Actions

To take advantage of Next.js app router, we need to separate our client components that need state from the rest of the page so we can load data on the server.

To do this we'll refactor the list of rooms into it's own `RoomList` component which will take the client-side React hooks with it. This way we can load all our rooms on the server and just pass it to `RoomList` which will start to stream the rooms as we create them.

The final step will be to turn the form for creating rooms into a server action. You'll notice that we will need to instantiate a new fauna client within the server action even though we have a client available in that same page. 

Refactor a Next.js 14 Page to Utilize Both Server and Client-side Rendering 

You'll notice that at the top level, we've set `'use client'` for our page. This means that everything on the page is a client component and we miss out on some benefits Next.js gives us by rendering on the server which includes a smaller bundle where sending to the client. 

We'll fix this in the next lesson.

Understand Server and Client Rendering and Data-fetching

FaunaDB is a global serverless database that gives you ubiquitous, low latency access to app data, without sacrificing data correctness. It eliminates layers of app code for manually handling data anomalies, security, and scale, creating a friendlier dev experience for you and better app experience for your users.

faunadb

To stream messages, we'll implement logic very similar to how we stream the creation of rooms from earlier.

First, we need to convert `MessageList` into a client component. We'll get an interesting error saying that only plain objects can be passed to client components from server components. This happens because the response we get back from Fauna is pretty complex. To fix this error we'll create a simple messages array and pass that down to the `MessageList` component.

In the `MessageList` component we'll create a `useEffect` that will initiate a Fauna stream within a ref and push new messages into state that we are holding in the component.

Stream Chat Messages in Real-time in Next.js from Fauna

Now it's time to actually create messages and display them in rooms within our application.

We'll start by creating a `MessageForm` component that will send a `Message.create` query to Fauna on form submission.

Next, we'll display these messages by updating our room query to also get all the messages for that room. You'll see that we can still just perform one query because we pass in the room id and call `Message.where(.room == room)` to the query.

One note here is if you console log your messages in your page component, you'll notice that the logs show up in the terminal and not the browser. This is because we are using a server component.

Create and Display Messages using Fauna within a Room in Next.js

With rooms being created and displayed on the index page, it's now time to create dynamic routes for each room so that when you click on a room name, you are actually navigated to that room.

We'll do this by using Next.js dynamic route syntax `/rooms/[...id]/page.js`. This will render a page on any `/rooms/:id` passing the component we define the `:id` as params.

Here we'll take that id and fetch the room data from fauna with a `Room.byId` query.

Create Dynamic Routes in Next.js for Chat Rooms

The first step to building a chat app is to have rooms to chat in. We'll need to create a Room collection (or table) in Fauna that we'll be adding to.

In our Next Application we'll create a simple form that has a submit handler that will create those rooms in Fauna. We need to hold some state in our component so we'll turn the page into a client component with `'use client'`.

There will be a little bit more Fauna set up to get things working. We need to create a key for our application to interact with our database so we'll create a key by running `Key.create` in our `fauna shell`. Then we'll install the `fauna` client and make a `client.query` to create the room.

Create a Fauna Room Collection in Next.js with the fauna Client

The first step is to install and configure our database that will be backing our Next.js application. We'll be using Fauna so we head over to the Fauna website and create a database.

Back in our terminal we will install `fauna-shell` so we can interact and manage our database from within our project. After some initializing steps from the CLI we'll be set up to build the app out.

Initialize and Configure a Fauna Database inside a Next.js project with fauna-shell

We'll start off by creating our project with `npx create-next-app`. This is a standard install so we'll select some of the options the CLI offers us.

Next, you'll notice that I'm running into some errors importing packages. This is because we need to update our eslint config so we'll edit `.babelrc` and `.eslintrc` to get those errors to go away.

Set up a Next 14 Project with create-next-app

In this lesson, you'll learn about the `path` configuration for Keystatic collections and singletons. It gives you a lot of flexibility over where your content should be stored on your file system.

Define where Keystatic should store your content

Supabase is an open source Firebase alternative. A service to:

- Listen to database changes.
- Query your tables, including filtering, pagination, and deeply nested relationships (like GraphQL).
- Create, update, and delete rows.
- Manage your users and their permissions.
- Interact with your database using a simple UI.

Supabase is not a 1-to-1 mapping of Firebase. Most notably, Supabase uses Postgres rather than a NoSQL store. 

supabase

In common workflows we do not want to push directly to the remote database from our machine. We will setup a github action that will run whenever we push to our main branch of our github repo. 



Deploy Local Database Changes to Supabase Remote Through Github Actions

It is common, when working on a team, for changes to have been applied to a remote database and for your local instance to be out of sync.

By running `npx supabase db pull` we can pull any changes to our remote database schema to keep our local database in sync.

Pull Remote Database Changes with Supabase

When `npx supabase init`, `npx supabase start`, or `npx supabase db reset` is run, supabase will look for our `seed.sql` file and seed our local database.

Since we have already created the project and we want to add seed data, we can run `npx supabase db reset`. 

Add Seed Data to Local Database with Supabase

With Supabase Link we can link our local project to a remote database. Then we can use the command `npx supabase db diff -f <fileName>` to create a local migration file. 

Then we use `npx supabase db push` to apply our local changes to the remote database.

Link Local Database to Remote and Run a Migration with Supabase

With the project setup, we can now start our local instance of supabase. This gives us a local running version of the supabase studio that we can interact with. 

We will create a table in called todos in the studio

Start Supabase Locally and Create a Table

With our project setup we will use our supabase dev dependency to run the supabase cli. We will login to our supabase account and then init the project locally. 

To run supabase locally you need to have Docker installed and running on your computer. To install Docker and get it setup visit this link - https://docs.docker.com/desktop/

Use Supabase CLI to Login and Init Project

Create an application and install packages 

Generate a New NextJS with Supabase App

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.

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.