Learn Aria, Playwright and Reactfire

WAI-ARIA, the Accessible Rich Internet Applications Suite, defines a way to make Web content and Web applications more accessible to people with disabilities. It especially helps with dynamic content and advanced user interface controls developed with Ajax, HTML, JavaScript, and related technologies. 

aria

We'll go over the work it takes to use ARIA to show why Semantic HTML is almost always the safer bet. 

Create a Semantic Checkbox vs an ARIA Checkbox

Debug Computed Accessibility Properties with Chrome Dev Tools

In this lesson, we use the element inspector in Chrome to test and improve our contrast to meet WCAG AA Contrast Ratio Standards.

Resources:
- [WebAIM - Contrast and Color Accessibility](https://webaim.org/articles/contrast/)

Debugging Accessible Contrast with Chrome Dev Tools

In this lesson, we are creating the audio player interface more visually appealing using icons from the React Icons library and Font Awesome, in this process, we are also ensuring that the interface remains accessible to screen readers by providing text descriptions for the icons, first by using the `title` prop on each icon, and then by implementing a `visually hidden` technique that hides the text descriptions from sighted users but makes them available to screen readers. This involves copying and pasting some CSS code to create a visually-hidden class, adding an `aria-hidden` attribute to each icon, wrapping each icon in a React fragment, and then adding a `span` element above each icon with the corresponding text description and the `visually-hidden` class.

**Think about:**  
- Why use `aria-hidden` or visually hide spans? 
- What value does this add to screen reader users?

Design Accessible Buttons Using Icons

In this lesson, we will create event listeners for the play and pause events on an audio player element and use these listeners to update the value of a variable called `isPlaying` that tracks the current state of the player (e.g., whether it is currently playing or paused). The event listeners, named `onPlay` and `onPause`, will be triggered when the user presses the play or pause button on the audio player element itself, not on a custom play button that may have been created.

Add Play and Pause Events to the Audio Player

We are creating an input of type range with an associated label, this input is to represent the current playback position in the audio, and the user will be able to change the playback position using the input.

The input value is set to be the media time state, it is added a minimum value of 0 and a maximum value as the duration. The demonstration shows how the media time state updates accordingly when the user changes the time using the input range.

A function called `onScrubberChange` is added, which takes in an event as an argument and `console.logs` the value of that event. The `onChange` event is added to the input, ensuring it works as expected. A variable sets the media time state and the current time on the audio ref.

**Think about:** Why do we use the `event.target.value` to set the new `mediaTime` (reminder that the value of the range is bound to `mediaTime`....maybe take a look at that with the formatter)

Create the Audio Scrubber to Control Playback Position

In this lesson, we are creating a new state called `mediaTime` with an initial value of 0, updating sample text in the markup to display the value of `mediaTime`, and adding an event listener to the audio element for the `timeUpdate` event.

In the `timeUpdate` event listener callback function, the `mediaTime` state is set to the audio element's current time, which can be accessed using the `audioRef.current.currentTime`. A function is added to the `onTimeUpdate` event of the audio element, to be able to track the current time. And a time formatter is added to format the time for displaying elapsed time and total time.

**Think about:** How often does onTimeUpdate update? (every second, not every millisecond) 

Tracking Audio Playback Progress by Adding Elapsed Time 

We created a duration state with an initial value of zero, then added an event listener to the audio element for the `loadedMetaData` event. In the event listener callback function, you set the duration state's value to be the audio element's current duration. This ensures that the value of the duration state is only set once the audio element metadata is loaded, allowing you to display the accurate total time of the audio file.

**Think about:** Why do we need to set the duration on the `onLoadedMetadata` event?

"The `onLoadedMetadata` event occurs when metadata for the specified audio/video has been loaded." You can read the docs [here](https://www.w3schools.com/tags/av_event_loadedmetadata.asp) 


 Implementing a Duration State 

Playwright is a framework for reliable end-to-end testing of modern web apps.

playwright

Playwright provides a simple library that you can install via npm for automating various browser engines. That is what we demo in this lesson.

Playwright Library for Browser Automation

In this lesson we look at the Playwright Test concept of Fixtures. We demo the creation of various fixtures and explain the idea behind browser, context and page.

Playwright Test Fixtures, Browser Context and Page

In this lesson we demonstrate how you can create Page Objects in Playwright. The Page Object Model (lovingly called pom) is a great way to simplify your test code and make it more easy to understand and maintain in the long run.

Page Object Model Pattern with Playwright

In this lesson we look at various ways you can select different elements on page using Playwright Selectors. We also demo playwright selector engines and selector chaining

Playwright Selectors

A common post mortem debugging tool for e2e tests is screenshots and videos. These can be enabled via options in the Playwright config and that is what we demo in this lesson.

Capture Failed Test Videos and Screenshots with Playwright

Playwright provides a powerful tool in the form of the trace viewer to help you debug any issues in your test after they have failed. That is exactly what we demo in this lesson.

Postmortem Debugging Failed Tests with the Playwright Trace Viewer

In this lesson we introduce the codegen feature of Playwright which can be used to quickly generate automation / test code.

Quickly Generate Playwright Tests with Codegen

In this lesson we look at how you can use the playwright inspector to debug any issues in your tests. We run through a sample test and guide you through the various portions of the playwright inspector UI.

Debugging Playwright Tests with Inspector

GitHub fosters a fast, flexible, and collaborative development process that lets you work on your own or with others.

github

In this lesson we look at why you would need to generate snapshots on your CI server. We follow this up by creating a simple Github Action that will generate new snapshots against a pull request once you leave a specially formatted "slash command" style comment.

Generate Playwright Snapshots on your CI Server

In this lesson we look at how you can do simple VRT (visual regression testing) using Playwright tests.

Visual Regression Testing with Playwright

Visual Studio Code is an integrated development environment (IDE) built and maintained by Microsoft. It is offered free of charge.

vscode

We look at the various features of the VSCode Extension for Playwright and how it can help you with your testing workflow.

Playwright VSCode Extension

In this lesson we look at how you can add playwright to your existing web application. Along the way we also look at other neat stuff like beforeEach, describe, async web-first matchers and the hasText locator option.

Adding Playwright to your Web App

This lesson covers the basics of Playwright's testing API such as the page object, locator, locator methods and expect.

Playwright Tests API Basics

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

In this lesson, you'll learn how to leverage the GitHub CLI to efficiently search through issues across multiple repositories and use that data with AI Studio for deeper insights.

The lesson covers:

- Using GitHub CLI to search for specific issues across repositories
- Filtering issues based on criteria like comment count
- Extracting issue content and comments
- Using the collected data as context in AI Studio
- Creating practical applications like FAQs from issue discussions

The example demonstrates creating a Framer Motion FAQ by searching GitHub for issues with multiple comments, but the technique can be applied to any framework or library.

This workflow helps developers:
- Research common problems and solutions
- Understand recurring issues in specific technologies
- Generate useful documentation from real-world problems
- Make better use of existing GitHub discussions

This approach combines the power of GitHub's vast knowledge base with AI tools to create valuable insights for development work.

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. And what you can really 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. And inside of Cursor with their Composer AI agent, we can search for the files we want, and we can tell the agent to download them and then work on them and inspect them and teach us 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.

Leverage Git Diff to Fix AI Agents' Mistakes

AI agents are notorious for getting tasks done, but also taking some shortcuts in TypeScript, and falling back to using things like Any, which you might not want in your codebase. This creates a scenario when it's done all of the work, you've tested and verified everything, but it's left you with many things untyped. So in this situation, when you run git commit, you can set up a commit hook to expose all of the linting, expose all of the TypeScript errors. And then you just let the AI agent run that commit hook and it can find all of the mistakes that it made. And go through And fix everything without you ever having to touch the code. This lesson covers a real world scenario of a commit hook running based on a refactor that was made when extracting some types. And it shows how running commit with an agent, if you allow the commit to give exhaustive information on what went wrong, the agent can continue on and make all the changes for you.

Commit Hooks are Critical with AI Agents in Cursor

If you ever want to see data represented in a certain way, such as a chart or a graph, to better understand what's going on in the world, you can use AI's to grab the most recent data and then convert it into any visual format you want. This is enabled in AI studio with Gemini through what's called grounding from web search, structured output formatting data, and code execution, which can run Python and generate charts. This lesson is going to walk you through the challenges of using grounding, especially when working with numbers which need to be accurate. How you can use image recognition and convert that to structured output. And then the challenges of using code execution. Especially when AI's are confident that the code ran even though the code failed. At the end, we still get to generating a chart through the online editor and then we take it also to set up in Cursor in a local environment where we can run all the way through YOLO mode and just click around some buttons and get our chart.

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