Learn Html, Jwt and Serverless

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.

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Do you want to allow users of your app to create an account and sign in with their Google profile? If so, this 10-minute video will show you how to set up within your Auth0 tenant! 

The Google social connection allows users to log in to your application using their Google profile. By simply adding the connection within your Auth0 dashboard, enabling it for your application, and creating a project within your Google developer console, you can be up and running within no time!

How To Add Google Authentication with Auth0

We'll discuss the minimum set of files that are required to get up and running with Vue.

We'll add an `index.html` file as our entry point, a `main.js` file that will contain our JavaScript code and a `styles.css` that will contain all of our CSS. We also load Vue into our project with UNPKG which exposes the Vue object to us.

Set up the Minimum Files Needed to Run Vue 3

JSON Web Tokens are an open, industry standard RFC 7519 method for representing claims securely between two parties.

Now that we have both the user and the token we can encode a JWT. This is made easy by the `jsonwebtoken` library that we will be using. 

To create a function that encodes a jwt, the function will need the user and token. Inside the function, definition create a `jwtPayload` object that just contains the essential data for encoding. Then, pass the payload to the `jsonwebtoken` library `sign` method and return the signed token.

Finally we store the jwt in the user's localStorage.

Encode User Data with JSON Web Token (JWT)

Now that we have our encoded JWT we need to be able to decode it!

The `jsonwebtoken` library makes this very easy. All we have to do is pass the `jwt` and our `token` to a decoder function. In that function definition we pass the arguments into the `verify` method and return the result.

Identify Users with JWT

We are going to use our JWT to make a protected request. 

All we have to do is create a button with a click event that calls our `fetchRepos` function. In the fetchRepos function make a fetch request to the `/repos` endpoint. In the fetch, pass an Authorization header with your JWT as a Bearer token. Then just await the response!

Use JWT to Make a Protected Request

We are going to simulate user verification when making a protected request. To simulate the database we are just going to use a simple array.

We verify the user by grabbing the JWT off of the authorization header in the request. We then look for a user in the database with a matching JWT. If the verification is successful we can give the user the data they requested.

Handle a Protected Request with JWT

 Every developer who has attempted to demystify OAuth 2.0 must be presented with mind-bugging complicated flow diagrams. It becomes worse when you try to understand all the terminologies of OAuth 2.0 and Open ID protocols.

There is no getting around the OAuth 2.0 flow being complicated with back and forth between your browser, the service you are authenticating with, and the application you want to access. 

This course gets straight to the point without any application code distracting you from the core takeaway -- You will learn how to authenticate and authorize yourself using GitHub as your Auth Server.

We'll first cover the OAuth 2.0 flow for authentication and then integrate the Open ID protocol for authorization.

[**LIVE EVENT**: Register here for a live webinar with Christian on OAuth.](https://egghead.zoom.us/webinar/register/WN_Sxy1FEcoTea8UA6F7EDzLw) Bring any questions you have from taking this course!

Add Github Login to Your Web App with OAuth 2.0

In this video you'll learn how to deploy a React app to AWS using AWS Amplify Hosting's drag and drop folder functionality. The app will also automatically be globally distributed on AWS's global CDN.

Deploy a React App to AWS in 24 seconds

HTML 5 is the structure of our web pages. It is the markup that represents the DOM (document object model).

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We'll round out the HTML for our form fields by adding radio buttons and checkboxes, including each in a checked, unchecked and disabled state. You'll learn about best practices for both field types semantically as well as when to use each.

Radio buttons should be used when you want a single selection out of a set of options while checkboxes should be used when you want multiple selections out of a set. A key difference between radio/checkbox and other inputs is that you wrap the label around the options which creates a direct relationship between the label and input so a `for` attribute is not strictly necessary.

Create Semantic, Accessible HTML for Radio Buttons and Checkboxes

Continuing on from our text inputs, we'll make our next field type including a select with the `multiple` attribute. Additionally, we'll include these fields in a disabled state. Both types will use the `.form-group` created in the previous lesson for a touch of layout styling.

You'll notice the same pattern here as with text inputs in that to make select inputs fully semantic and accessible you will need to add a `for` attribute that matches the `id` of the select element

Create Semantic, Accessible HTML for Form Selects

Learn how to ensure accessibility of text form inputs from the start by defining semantic HTML. To do this, we'll need a label for each input that we create. This label needs to be associated with the input through a `for` attribute which allows screen readers to know that a label is associated as well as allows a click of the label to focus the input.

We'll create HTML for text and textarea, as well as disabled versions of both.

Create Semantic, Accessible HTML for Text Form Inputs

Learn how to make a fully functioning Tic Tac Toe game that doesn't need any client-side JavaScript!

For this course, we'll be using Pug and Stylus which are preprocessors for HTML and CSS respectively.

Learn how to:
- Generate markup with loops and template literals
- Leverage native HTML behavior
- Create SVG
- Use CSS variables and scope
- Implement game logic with HTML forms
- Animate SVG with CSS
- Use CSS Animation to provide motion
- Use Pug Mixins
- Disable motion when users don't want it
- Apply visual effects using CSS filter and backdrop-filter
- Generate random animations using Pug and CSS variables

Cascading Style Sheets bring order and beauty to our applications.

Tic Tac Toe with CSS and SVG

If you're repeating blocks of markup or code in Pug, you can use a mixin to reduce the maintenance overhead. Here, we look at some of the ways you can use Pug mixins.

Use Pug Mixins for Reusable Blocks of Markup

We can use SVG to create a cross with one path. But, what if want to animate a cross as if it's being hand-drawn? We can do this by defining more than one path for our SVG.

Use SVG to Create an Animatable Cross

Use the circle element in SVG to define a radius and center points for a circle.

Use SVG to Create a Circle

Use SVG to create lines with the path element. We can hand-code a path definition and set a stroke width and stroke linecap. We can adjust the path definition to accommodate for the SVG viewBox.

Use SVG to Create Lines

You can use an element with the type "reset" within a form to reset a form on click. It's particularly useful when we don't want users to refresh a page in order to get back to an initial state.

Use Type Reset to Reset Form State

Use the HTML preprocessor Pug to generate markup for a form. Here we generate a set of labels and checkboxes. We link these using ES6 Template Literals with a loop index.

Use Pug For Loops to Generate Form Markup

Starting from a prebuilt frontend, Jason Lengstorf guides you through the creation of a "Corgi Up-boop App" that uses Netlify Functions and Hasura. You can view [the deployed final product on Netlify](https://egghead-serverless-functions.netlify.app/).

You'll set up the Netlify CLI, and configure a local development environment before writing and deploying functions with a few different use cases.

The first example function bypasses CORS restrictions to retrieve corgi data from an example third-party API.

Next, you'll configure environmental variables to securely communicate with the Unsplash API without exposing private keys to the client.

Serverless functions don't persist data by themselves, so we will use  Hasura to create a GraphQL API for tracking the number of "boops" each corgi receives.

This course is an excellent illustration of the speed of Serverless functions, and how fast you can implement functionality in your web applications.

An all-in-one workflow that combines global deployment, continuous integration, and automatic HTTPS.

netlify

Build a Corgi Up-boop Web App with Netlify Serverless Functions and Hasura

Environment variables set in a `.env` file can be imported using the Netlify CLI with the following command: `netlify env:import`.

This allows you to manage environment variables directly on Netlify, providing the benefit of security and flexibility of sharing environment variables with your team. 

From inside Netlify, you can access the environment variables in:

`Site settings` > `Build & deploy` > `Environment` > `Environment variables`

Import and Set Environment Variables from a .env file using Netlify CLI

CORS limits websites from communicating with other domains without the full consent of both sites. Consuming data from a 3rd Party REST API makes it difficult since we can't properly configure the appropriate CORS headers. To solve this, we'll set up a proxy server to request the data using a Netlify function that avoids CORS altogether.

The Third-Party API that we'll be working with will provide the following data: `id`, `name`, `favoriteSong` for each corgi. 

[http://no-cors-api.netlify.app/api/corgis](http://no-cors-api.netlify.app/api/corgis)

We will also use `node-fetch` a light-weight module that brings the `fetch` API to fetch the data into our application.

Circumvent CORS when Accessing a Third-Party API using Netlify Functions 

 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.

Planning AI Agent Sessions with Reasoning Models

AI agents will inevitably make mistakes—it's just a matter of when. A key tool in your toolbox, once they do make mistakes after working on a problem for a while, is to use **git diff** to capture all of the changes they've made. 

Once you have those changes in a text file, you have a portable document that you can test against other AI models. This allows you to move the problem into smarter reasoning models with smaller context windows, helping you isolate and focus on the issue at hand.

**git diff** has been invaluable when working with AI agents because they'll often make many good changes but also slip in one tiny error. Finding that one small mistake is **git diff**’s superpower.

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