Learn Firefox, Openapi and Reactfire

Cascading Style Sheets bring order and beauty to our applications.

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

Firefox DevTools has an awesome feature that helps you determine Inactive CSS, to know why it's inactive, it gives you hints on how to fix it, and even links to documentation for additional help.

Understand why CSS has no effect with the Inactive CSS rules indicator in Firefox DevTools

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

chrome-devtools

The axe browser extension (for Chrome and Firefox) is an immensely helpful tool for auditing web pages for accessibility defects.  This extension is powered by the same rules engine developed by Deque Labs in their axe-core open source library which also powers the react-axe development tool.  Each reported defect offers a ton of information about the defect, why it's a problem, severity level, groups impacted and ways to fix the defect.  This can be a great resource for learning more about how to write accessible code.

## Resources
* [Chrome browser extension](https://chrome.google.com/webstore/detail/axe-web-accessibility-tes/lhdoppojpmngadmnindnejefpokejbdd)
* [Firefox browser extension](https://addons.mozilla.org/en-US/firefox/addon/axe-devtools/)

Use the axe Browser Extension to Audit a Web Page for Accessibility Issues

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

tota11y is a great tool to add to your accessibility auditing toolkit.  tota11y is a browser extension available for Chrome and Firefox that helps visualize accessibility violations (and successes), while educating on best practices.  It can make for a really helpful supplement to other accessibility auditing tools.

## Resources
* [About tota11y](https://khan.github.io/tota11y/)
* [Chrome browser extension](https://chrome.google.com/webstore/detail/tota11y-plugin-from-khan/oedofneiplgibimfkccchnimiadcmhpe?hl=en)
* [Firefox browser extension](https://addons.mozilla.org/en-US/firefox/addon/tota11y-accessibility-toolkit/)

Use tota11y to Visualize Accessibility Issues

Using our accessibility auditing toolkit we can find issues concerning landmark regions in our web application.  The react-axe library will report findings in the JavaScript console of the browser.  We could also run the axe browser extension to see the same results.  The tota11y browser extension will annotate any landmark regions on the page and therefore if none are annotated it may indicate that they aren't any (_NOTE: tota11y will only annotate landmark regions using aria roles and not those using HTML5 elements_).  Finally using the VoiceOver Rotor in Safari will show us whatever landmark regions have been defined in the Landmarks menu. 

Test for Landmark Region Accessibility Issues in React

Animations can make people sick, or worse! By adding animation toggles and listening in to the user's system preference for reducing motion on OSX and iOS, we can give them more control over our interfaces. Animation can be a safety issue; let's do something about it!

Warning: there is a flashing animation in the video.

In this lesson:
- [prefers-reduced-motion](https://webkit.org/blog/7551/responsive-design-for-motion/)

WCAG 2.1, Guideline 2.3: Seizure risks
- https://www.w3.org/WAI/WCAG21/quickref/?versions=2.0#qr-seizure-does-not-violate

Your Interactive Makes Me Sick
- https://source.opennews.org/articles/motion-sick/

Accessible animations with reduced motion

Open API Initiative (OAI) is focused on creating, evolving and promoting a vendor neutral API Description Format based on the Swagger Specification

openapi

Swagger is a project used to describe restful APIs using the OpenAPI Specification. It allows you to document your API so consumers understand the endpoints, parameters, and responses. In this lesson, I'll show you how to install the swagger command line tool, create a new API project using swagger, and introduce you to the swagger API editor.

Create an API with Swagger

There are a lot of great monitoring tools available for Node.js. It is also incredibly easy to build monitoring into your application. In this lesson, we will add precision monitoring to the function calls in the Todo API server, log it to the console, and persist those results to Elasticsearch where they can later be reviewed, graphed, and analyzed.

Build Monitoring into your API Server

In this lesson, I will show you how to update a simple, skeleton React application to work with the Todo API server built with Swagger. Using existing components, you will learn how to display all Todo items, update an existing Todo item, and add a new Todo item.

Connect an existing React application to an API server created with Swagger

The last step before deploying your new API server into production is to load test it. Load testing allows you to better understand the performance characteristics as well as forecast load and capacity. Fortunately, load testing is incredibly easy and I'll show you exactly how to create a load test plan, test the response from the API server to ensure it is responding correctly, and scale your test up to simulate as many users as needed.

Perform Load Tests on an API Server using Apache jmeter

This lesson shows you how to delete a Todo item from Elasticsearch using the Todo ID from the URL parameters according to the specifications defined in your Swagger API docs.

Create a Node.js function for an HTTP DELETE request for a Swagger API

This lesson will teach you how to create the javascript functions defined by the Swagger specification to return all Todo items to the client when requested with an HTTP GET method.

Create a Node.js function for an HTTP GET request for a Swagger API

This lesson shows you how to create the functions defined in the Todo API specification to create new Todo items when received in HTTP POST methods. You will also see the errors you will get when CORS is not properly configured and how to resolve them by installing the NPM CORS package.

Create a Node.js function for an HTTP POST request for a Swagger API

This lesson will teach you how to update a Todo item in Elasticsearch when received from an HTTP PUT method in your API server built with Swagger.

Create a Node.js function for an HTTP PUT request for a Swagger API

In this lesson, you will learn how to create a function in your Node.js Todo API server that gets the ID of the Todo item being requested from the URL and retrieve the Todo item from Elasticsearch.

Create a Node.js function for an HTTP GET request with URL parameters

In this lesson, you will learn how to define an HTTP GET endpoint using Swagger with URL parameters to identify the ID of the Todo item being requested.

Define an HTTP GET Endpoint with URL Parameters for a Node.js Server with Swagger

In this lesson, you will learn how to create an HTTP GET endpoint for a node.js server using Swagger. This endpoint will be used to return all of the Todo items from Elasticsearch to the client.

Define an HTTP GET Endpoint for a Node.js API Server with Swagger

This lesson demonstrates how to access the ID of a Todo item being deleted from the URL parameters when used with an HTTP DELETE method.



Define an HTTP DELETE Endpoint for a Node.js API Server with Swagger

In this lesson, you will learn how to define HTTP POST operations for your Todo API server using Swagger. Creating the endpoint, passing the todo in the request body, and parsing the todo are all demonstrated.

Define an HTTP POST Endpoint for a Node.js Server with Swagger

In this lesson, you will learn how to define multiple parameters in the API specification to identify the ID of the Todo item being updated in the URL, as well as the contents for the updated Todo item in the body of the request.

Define an HTTP PUT Endpoint for a Node.js API Server with Swagger

APIs created with Swagger have a built-in mock function, allowing you to mock responses from your API prior to writing the backend code to make it functional. In addition to learning how to enable this feature, I will show you how to write your own functions to enhance the mock responses returned when using the Swagger mock feature.

Mock API Responses with Swagger

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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