Learn Chrome, Gatsby and Mdx

MDX is an authorable format that lets you seamlessly use JSX in your markdown documents. You can import components, like interactive charts or notifications, and export metadata. This makes writing long-form content with components a blast.



We will combine our knowledge of Dynamic routing to pass a dynamic parameter to a function that will query a single post from our Sanity CMS. When we fetch our blog post data this is a great place to do some processing of the content that we get back. If you didn't process your blog post markdown, it would come back as a string which doesn't look too great on the page.

To make our markdown pretty we will install and use the `MDSvex` library to use it's compile function and render our markdown.
  
We'll see that `MDSvex` gives us a Promise from `compile` because it's an async function so we'll use the Svelte `await` block to conditionally show the post body when it is ready, otherwise we'll show a loading state.

Load a Single SvelteKit Post Dynamically & Render Markdown with MDSvex

By leveraging the [dependency graph](https://nx.dev/l/r/structure/dependency-graph), [Nx](https://nx.dev) is able to understand which parts in the monorepo changed and thus trigger actions on those nodes, such as building an app, running tests or linting. This already works out of the box. However, with our portfolio application, all of our markdown articles reside in the root-level `_articles` folder. Those are not captured by the Nx dependency graph and thus Nx would not correctly detect a change to our Next.js site application whenever we change or add a new markdown article.
In this lesson, we learn how to register our `_articles` folder s.t. it appears as a dedicated node in the Nx dependency graph.

Prefer to read along as well? Here's the [accompanying article](https://blog.nrwl.io/publishing-a-next-js-app-to-vercel-with-nx-df81916548f5).

Register our Markdown Articles with the Nx Dependency Graph

**NOTE:** `workspace.json` is deprecated, you can update to `project.json` [here](https://nx.dev/deprecated/workspace-json).

One cool aspect of Next.js is its [fast refresh](https://nextjs.org/docs/basic-features/fast-refresh) support. Whenever you change some Next.js component it automatically refreshes the page without hard reloading everything. However, you might notice that whenever we change something within our MDX markdown files, fast refresh is not capturing that, hence we need to manually refresh the browser window.

In this lesson, we learn how to leverage a feature of Nx to create a "[custom Next.js development server](https://nx.dev/l/r/next/server#customserverpath)". We are going to use [chokidar](https://www.npmjs.com/package/chokidar) to watch our articles folder and then invoke a private API on the Next.js server instance to trigger a reload command.

Prefer to read along as well? Here's the [accompanying article](https://blog.nrwl.io/hot-reload-mdx-changes-in-next-js-and-nx-c05252cf450a).

Add Fast Refresh Support to our MDX Files with Nx Support for Custom Next.js Servers

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

Next.js is all about creating performant web applications. However, the more React components we pull into our MDX via `next-mdx-remote`, the heavier our JavaScript becomes which drains our site's performance. Also, while we might have quite a few React components to be included in our Markdown rendered content, we might not always use all of them in our articles.

In this lesson, we learn how to leverage Next.js dynamic imports to just pull the minimum necessary components into our final JavaScript bundle. Also, we look into why we might want to do deep imports into our Nx library setup to decrease the size of the bundle even more.

Prefer to read along as well? Here's the [accompanying article](https://blog.nrwl.io/component-hydration-with-mdx-in-next-js-and-nx-90f46ea0431c).

Dynamically Loading React Components with next-mdx-remote and Next.js Dynamic imports

The markdown rendering includes a lot of native HTML tags as well of course. Assume for instance we want to change the behavior of links within our rendered document. We can definitely create a custom component and include that in our MDX document, but we’d rather not want to have to use a specific `<Link>` component and rather be able to keep the native way of rendering links in Markdown, that is `[some link](https://juri.dev)`.

Turns out that with `next-mdx-remote` we are able to automatically replace all rendered `<a>` tags (or really any other tag we want) with a React Component.

Prefer to read along as well? Here's the [accompanying article](https://blog.nrwl.io/component-hydration-with-mdx-in-next-js-and-nx-90f46ea0431c).

Use React Components for Native HTML Tags with MDX and Next.js

When rendering our markdown we might want to enrich the experience with some dynamic content. Many static site generators allow to include dynamic content placeholders for like embedding a Youtube video, generating table of contents, code blocks with interactive functionality and more. One way to achieve this in the React ecosystem is by using MDX. MDX is similar to JSX but for Markdown content, and allows us to directly embed React components within it. This gives an enourmous flexibility to what we can display within a page.

In this lesson we’re going to generate a new Nx library to host our “mdx elements” and learn how to leverage the `MDXRemote` component to embed these React components into our markdown rendering flow.

Embed React Components in our Markdown with MDX in a Next.js App

Once we have separated the front-matter from the rest of the markdown document, we can proceed to convert it to HTML, for then finally render it with our Next.js page component. In this lesson we use `next-mdx-remote` to parse the markdown string into HTML and we learn how to leverage the `MDXRemote` component to include it into our page component.

Prefer to read along as well? Here's the [accompanying article](https://blog.nrwl.io/read-and-render-md-files-with-next-js-and-nx-89a85c1d9b44).

Render Markdown With next-mdx-remote in our Next.js Page Component

Many markdown documents have a metadata section at the very beginning, which contains information such as the title, author and creation date. This section is the so-called "front matter". 

In order to render our markdown-based blog articles on our portfolio site we want to parse them and separate them into the front-matter and main article part. `gray-matter` is a library that helps us with that.

Prefer to read along as well? Here's the [accompanying article](https://blog.nrwl.io/read-and-render-md-files-with-next-js-and-nx-89a85c1d9b44).

Parse a Markdown Document With gray-matter

In a plain normal Next.js application you would place your markdown processing logic somewhere within a folder insider your Next application. We could do the very same in an Nx monorepo, however, we get much more benefits by moving it into a dedicated library in the `libs` folder.

[Nx workspace libraries](https://nx.dev/l/a/structure/grouping-libraries#example-workspace) allow you to nicely encapsulate your functionality as well as to easily share it with other apps that might potentially live in our workspace. In this lesson, we learn how to generate a new library with Nx generators and how to import the library into our Next.js application.

Prefer to read along as well? Here's the [accompanying article](https://blog.nrwl.io/read-and-render-md-files-with-next-js-and-nx-89a85c1d9b44).

Generate an Nx Library to Implement our Markdown Rendering for our Next.js App

“a11y” stands for “accessibility.” It is a numeronym, with 11 representing the count of letters between the letter a and the letter y.

Accessibility is broken down into 4 categories of disability: visual, cognitive, auditory, motor. 

Regardless of the category of disability, accessibilities aim in web development is to make applications and websites usable for everyone.

a11y

To audit our app for accessibility issues at runtime we can use Cypress to run our application in a browser environment and then with the aid of the axe-core `cypress-axe` library we can analyze the current page for any accessibility issues.

## Resources
* https://www.cypress.io/
* https://github.com/component-driven/cypress-axe
* https://mochajs.org/#bdd
* https://www.chaijs.com/guide/styles/

Write E2E Tests for Accessibility Using aXe-core and Cypress

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

To audit our app for accessibility issues at runtime we can use Selenium WebDriver to run our application in a browser environment and then with the aid of the aXe-core `webdriverjs` library we can analyze the current page for any accessibility issues.

## Resources
* https://www.selenium.dev/documentation/getting_started/installing_browser_drivers/
* https://github.com/dequelabs/axe-core-npm/blob/develop/packages/webdriverjs/README.md 

Test Accessibility at Runtime with Selenium WebDriver and aXe-Core

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 install the [Redux Dev Tools](https://github.com/zalmoxisus/redux-devtools-extension#installation) extension into Chrome and demonstrate how it lets you inspect the various actions passing through your redux store as well as all of the data it contains. We also briefly demonstrate the time travel debugging capability as we walk back through and un-add items to our shopping cart.

The key here is that because we used [configureStore](https://redux-toolkit.js.org/api/configureStore) from RTK which automatically includes support for redux dev tool without having to explicitly turn it on.

Using the Redux DevTool Support Built-in to Redux Toolkit

Cascading Style Sheets bring order and beauty to our applications.

In this lesson, we take a look at how to animate and transition custom properties with CSS Houdini @property. It hasn't been possible to animate and transition background-images and other properties until now. This demo will show you how to define custom properties and define type checking for them.

Check for [Browser Compatibility](https://caniuse.com/mdn-css_at-rules_property).

Use CSS @property to Animate and Transition Custom Properties

In this lesson, we take a look at the CSS aspect-ratio property and see how we can define different aspect ratios for elements.

Check for [browser compatibility](https://caniuse.com/mdn-css_properties_aspect-ratio).

Use CSS aspect-ratio to prescribe an aspect ratio for an element

CSS Media queries are heavily used across web pages for different reasons, such as make your web pages responsive, but what if we can use them to improve web accessibility as well?

In this video, I’m sharing How to use CSS Media Queries for web accessibility in your web app, combining the user preferences choice with `prefers-color-scheme` media query and enabling dark mode in your web app.

Create a Dark Mode Theme in Your Web App Using CSS Media Query

Optional chaining ?. in javascript is a new feature introduced in ES2020. Optional chaining changes the way properties are accessed from deeply nested objects. It fixes the problem of having to do multiple null checks when accessing long chain of object properties in javascript.

Optional Chaining

The Chrome Developer Tools (DevTools for short), are a set of web authoring and debugging tools built into Google Chrome.

chrome-devtools

Lighthouse is a tool that analyses web apps and web pages, collecting modern performance metrics and insights on developer best practices.

It's built directly into the Chrome DevTools and in this quick lesson we're going to learn how to run the audit, how to read its results and why you should always run the Lighthouse audit in Incognito mode.



Run a Lighthouse audit in Chrome Devtools

Sometimes you encounter a backend issue which seems to occur only on your computer, with a very specific sets of headers, cookies etc. Because of that, it's useful to have a way of easily sharing network requests sent by your browser with other members of your team.

In this quick lesson we're going to learn how to copy a network request as `fetch` (so we can test it in the Chrome DevTools console/in a node.js script) or as cURL (so we can test it in a terminal).

Copy a network request as fetch or cURL with Chrome DevTools

Sometimes when debugging our CSS styles it's useful to be able to see what a DOM element is going to look like in a given state (e.g. `:focus`, `:active, `:hover`) etc.

Unfortunately it's not possible to both hover over an element and debug it in Chrome DevTools at the same time (since we only have a single cursor!).

In this quick lesson we're going to learn how to address this problem by explicitly forcing an element state to `:hover` with DevTools

Force a DOM element state in Chrome DevTools

In this lesson, we look at how you can use `content-visibility: auto` to significantly improve page load performance by applying it to `section` elements. The property tells the browser to skip rendering an element if it is off-screen and below the fold on the initial load. 

This can introduce some scroll jumping which we walk through how to combat with `contain-intrinsic-size`.

• Read more about `content-visibility` and `contain-intrinsic-size` here: https://web.dev/content-visibility/

Use content-visibility: auto to Improve Initial Page Load Time

Kathleen McMahon walks us through a real-world MDX digital garden implementation

Digital Gardening with MDX Magic

While much focus in AI has been on optimizing input and generating concise outputs, a new model in Google's AI Studio dramatically expands output possibilities with a capacity of 65,000 tokens. This represents a significant shift, opening doors to new application scenarios, especially for developers and creators. This lesson explores a practical and immediately useful scenario: leveraging this large output capability within Google's AI Studio to generate extensive sample datasets for your projects. Whether you are developing a new application, building a resource, or require robust datasets for testing and validation, this model offers a powerful solution. We'll demonstrate how to move beyond the constraints of short responses and utilize the full potential of large output models to generate the data volume and variety you need, enhancing your development workflow through Google's AI Studio.

AI Studio's New 65k Output Opens Exciting Possibilities

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.