Learn Html, React-router and React-storybook

Fast, easy and reliable testing for anything that runs in a browser.

cypress

[Storybook](https://storybook.js.org/) is already powerful for being able to develop components in isolation as well as using those stories as living documentation for others. [Nx](https://nx.dev) goes a step further. It leverages the fact that Storybook exposes an `iframe.html` that allows to access the rendering of the story without the actual Storybook frame. On top of that, Nx generates a Cypress configuration to run tests against the Storybook stories.

Prefer to read along as well? Here's the [accompanying article](https://blog.nrwl.io/use-cypress-with-next-js-and-nx-to-battle-test-your-react-components-da273f664f81).


Write Cypress Tests for our React Storybook Components with Nx

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

In this lesson, we develop a new React component in an [Nx](https://nx.dev) library with Storybook. We explore how to leverage [Storybook](https://storybook.js.org/) to develop the component in isolation, how to expose the component props s.t. we can then dynamically change them in the Storybook rendering and much more.

Prefer to read along as well? Here's the [accompanying article](https://blog.nrwl.io/use-storybook-with-tailwind-in-an-nx-workspace-5ceb08edad71).

Develop React Components in Isolation with Storybook and Nx

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

Developing UI components is sometimes a highly visual effort. We continuously want to have feedback on how a particular change affects our component rendering and adjust accordingly. Most often, as a developer, we include these components in their target applications, just to get that visual feedback. This is far from ideal though.

[Storybook](https://storybook.js.org/) solves exactly this! It allows you to develop components in isolation and experiment with the inputs and outputs of a component. Furthermore, once the Storybook story is fully developed, it can serve as documentation for other developers to explore.

Prefer to read along as well? Here's the [accompanying article](https://blog.nrwl.io/use-storybook-with-tailwind-in-an-nx-workspace-5ceb08edad71).

Configure Storybook for your React Based Nx Workspace Library

Building a component library is a great way to gain efficiencies in creating accessible React apps and Storybook is a great tool for doing that. Using the accessibility add-on to Storybook you can test your components in isolation for accessibility issues before composing them into an application.

If you can write and test each of these components initially with accessibility in mind then you now have:

* A library of reusable components with accessibility baked in.
* You won’t need to solve the same problems over and over again.

## Resources
* https://storybook.js.org/
* https://www.npmjs.com/package/@storybook/addon-a11y

Audit Component Accessibility Using Storybook

In this lesson, we include the `getServerSideProps` and `getStaticProps` exports into a story, using Storybook loaders. We mock the fetch request using MSW.

Mock getServerSideProps and getStaticProps Request with MSW and Storybook Loaders

In this lesson, we add the Mock Service Worker (MSW) library to Storybook for API endpoint mocking in Storybook. We configure MSW by hand for use with Next.js features like `getServerSideProps` and `getStaticProps` in stories.

Integrate MSW and Storybook

In this lesson, we configure `next/image` for use in Storybook. `next/image` is a Next.js component that creates highly performance-optimized images to users based on device capability and browser dimensions. It can be unoptimized for compatibility in development environments like Storybook.

Configure next/image Component to be Unoptimized

In this lesson, we add Storybook controls to page stories using Storybook args. With args, stories can be manipulated and transformed in the browser. Additionally, we configure all stories in a module to use default args.

Add Controls to Next.js Page Props with Story Args

In this lesson, we configure Storybook to load global stylesheets for all stories. This is a great way to get general CSS into all stories and useful practice for adding resets, fonts, and globally shared stylesheets.

Import Global Stylesheets for all Stories in preview.js

In this lesson, we create a story for a Next.js page. Pages are components. So we can easily document and test them in Storybook.

Create Stories for Next.js Pages

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

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

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

html

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

 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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