Learn Angular-material, Firefox and Rails

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

Rails is a web-application framework that includes everything needed to create database-backed web applications according to the Model-View-Controller (MVC) pattern.

rails

When you create a User model with Devise using the command `rails generate devise User`. Devise will create  default columns in the database for your User model. To add your own columns for your users you can create a new Rails migration and add any columns you want for your users.

Add Custom Fields to a Devise User Model with a Ruby on Rails Migration

If you want to give the users the ability to follow or become friends with each other. Rails lets you create a join table in your database and have it reference the user database table. 

You can use Active Record to associate the new table with User table by using the `class_name` method

Create Active Record Self-Referential Relationships In Ruby on Rails

If you make a mistake when naming your database column, and have data that you want to keep unchanged. You can use the change method with the rename_column definition in your migrations files to update a single column name in your database.

Rename A Database Column with Ruby on Rails Active Record Migrations

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

Learn how to test custom ActiveRecord validations using RSpec.

In this lesson we'll write a test that expects the `email` to be found on the `invitations` table. We'll see that at first this test fails because we need to add the custom validation on the user model. Once we implement the validation and create the `InvitedValidator`, the test will pass.

Test Custom ActiveRecord Validations

Learn how to test ActiveRecord validations using RSpec.

In this lesson we'll write a test that expects the `name` attribute to be present on the User model instance. We'll see that at first this test fails because we need to add this validation on the model.

Test ActiveRecord Validations

Learn how to test ActiveRecord named scopes using RSpec.

In this lesson, we will test a `User` model that will have a `by_ci_email` method 
that we will define on the model. We will test this method in our `user_spec.rb` file and pass in a lower case email argument to demonstrate that the `by_ci_email` method is functioning correctly by finding the correct user.

Test ActiveRecord Named Scopes

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

AngularJS is an open-source JavaScript framework, maintained by Google, that assists with running single-page applications. Its goal is to augment web-based applications with model–view–controller (MVC) capability, in an effort to make both development and testing easier.

angularjs

Themes convey meaning through color, tones, and contrasts. And the theme color palettes, alphas, and shadows deliver a consistent tone to your application and a unified feel for all Angular Material UI components. Theming allows developers to change the intention and meaning of the application by changing the colors and shadows. 

Accessibility is a huge - often overlooked - feature in modern web applications. Angular Material components all implement ARIA features whenever possible. And for usage scenarios where the ARIA attributes are not explicitly provided and cannot be inferred, then Angular Material will provide details console warnings alerting the developer.

Let’s use the Angular Material Theming service to change the background, primary, accent, and warning colors used within the entire application. These will be configured at startup using the $mdThemeProvider.  

Let’s also resolve the two ARIA warnings shown in console. These originate from icon buttons where the ARIA label cannot be inferred. So let’s add a static aria-label and a dynamic, interpolated aria-label to those buttons.

> This course presented by Thomas Burleson and Aaron Frost

Angular Material: Theming and ARIA

Most Angular Material UI components are declarative and used directly in the HTML markup. Some components, however, are programmatic and must be configured and invoked from Javascript. Programmatic components have a short-term, limited DOM lifespan and include Dialogs, Toast, and BottomSheet.

Let’s use the BottomSheet service to configure and display an Angular Material BottomSheet container which in turn will display a nested, custom Contact Sheet view.

> This course presented by by Thomas Burleson & Aaron Frost.

Angular Material: Using a BottomSheet

Both flexbox CSS and the Angular Material Layout allow web applications to `respond` to the size of the browser at any given point. No matter what the browser width may be, the web app adjusts its layout sizing accordingly. Combine responsive functionality with media query support and your web applications will have `adaptive` features to adapt to the width of the browser at a specific points. More than fluidly adjusting the widths and heights of elements, Adaptive applications can change the rendered UX with new positions, new components, and new styles. Angular Material Layout hides the complexities of implementing adaptive features with a singularly unique and simple HTML syntax.
Let’s use the Adaptive extensions of the Angular Material API to hide the SideNav on small devices (like iPhone or Android). We will explore the use of the $mdMedia service to easily lock open the SideNav on larger devices. And we will see how the $mdSideNav service can be used to programmatically close the SideNav after a user is selected.

> This course presented by Thomas Burleson and Aaron Frost.

Angular Material: Adaptive Layouts

Angular Material UI components have been designed from the ground-up to use dynamic, live data. Supporting AngularJS interpolation, expressions, and databindings… each component exposes an intuitive API and fully supports the existing ng-<xxx> directives published in AngularJS.

Let’s replace the hard-coded, static HTML with dynamic, mock data using AngularJS data bindings, ng-repeat, and the ng-click directives. Our UX remains unchanged, yet now we are using custom application logic and dataservices.

> This course has been created by Thomas Burleson & Aaron Frost.

Angular Material: UI Components with Dynamic Data

Angular Material is an Angular-native, UI component framework from Google. It is a reference implementation of Google's Material Design and provides >30 UI components and services essential for mobile and desktop AngularJS applications.

In this course, we will quickly build a real-work application Angular UI application, take a tour of Angular Material, and learn how ngMaterial can be used in your projects.

*presented by Thomas Burleson and Aaron Frost*

Introduction to AngularJS Material

The Angular Material Layout API wraps the flexbox CSS features with an amazingly easy, powerful HTML markup API. The Layout API hides the complexities of the CSS, is intuitive to use, and eliminates the need for developers to become flexbox experts to implement modern page layouts. 

In this lesson we will learn how to use the Angular Material Container components and the Layout API to implement the layouts specified in design wireframe.

> This course has been created by Thomas Burleson and Aaron Frost. 

Angular Material: Containers with the Layout API

Learn how to install Angular Material with `npm install`  and load the required libraries and stylesheets into your blank web page.

> This course has been created by Thomas Burleson and Aaron Frost

Angular Material: Installing with NPM

Angular Material provides over 30 UI components and services. The framework offers a suite of components and containers: from simple Button components with built-in ink and hover effects, theming, and ARIA support… to more complex container components like mdList and Tabs.  

Let’s use some of those components in our SideNav and Content containers. For now we will use hard-coded, HTML markup. This allows us to confirm the UX matches that specified in the design docs before we start working on our application logic.

> This course has been created by Thomas Burleson and Aaron Frost.

Angular Material: Intro to UI Components

Create a search directive with AngularJS and Rails. You'll want to take a look at the related [Rails TODO API Part 1](https://egghead.io/lessons/angularjs-rails-todo-api-part-1) and [Rails TODO API Part 2](https://egghead.io/lessons/angularjs-rails-todo-api-part-2). This is **not** a Rails how-to, so some knowledge there is expected to get up and running.

Search Directive with Rails

Hardcoding your script to search for only one type of file (like `.md`) is a major limitation. This lesson shows you how to make your script smarter by teaching it to infer the desired file type directly from the user's natural language prompt.

You'll implement a two-stage AI process. The first stage analyzes the user's request to determine the file extension they're interested in (e.g., `md`, `txt`). The second stage then uses that file type to dynamically search the file system, find the relevant files, and execute the command.

**Workflow demonstrated in this lesson:**

* Add a preliminary `generateObject` call to extract a `fileType` from the user's initial prompt.
* Define a list of supported `fileTypes` (e.g., `['md', 'txt']`) and use a detailed prompt to guide the AI, instructing it to choose from the list or use a default.
* Dynamically construct the `globby` pattern using the inferred `fileType` (e.g., `*.$[fileType]`).
* Implement error handling to inform the user if they request an unsupported file type or if no files of the specified type are found.
* Demonstrate how the script defaults to searching for Markdown files but can correctly search for other types when specified (e.g., "summarize my profile from my pdf").

This technique adds a significant layer of intelligence to your script, allowing it to adapt its behavior based on the user's intent and making it a far more flexible and intuitive command-line tool.

John Lindquist

Dynamically Inferring File Types from User Prompts in Local AI Scripts

Hardcoding command logic and prompts directly into your script makes it rigid and difficult to extend. This lesson demonstrates a more flexible and powerful pattern: defining your script's commands as individual Markdown files in a dedicated directory.

This approach allows you to easily add, remove, or modify commands without touching the core script logic. You'll learn how to dynamically load these command files, present them as options to the AI, and inject the corresponding instructions into your prompt, creating a truly modular and extensible tool.

**Workflow demonstrated in this lesson:**

* Create a `commands` directory to house your command definitions (e.g., `summarize.md`, `translate.md`).
* In each file, write the specific prompt or instructions for that command.
* Use `globby` to discover all available command files at runtime.
* Create a `commandMap` that maps the filename (e.g., "summarize") to its file content.
* Update the Zod schema to use an `enum` of the dynamically discovered command names.
* Dynamically construct the final text generation prompt by injecting the instructions from the user-selected command file.
* Add a `profile.md` file to demonstrate how the script can act on user-created content with the new file-based commands.

By the end of this lesson, your script will be architected for easy expansion, allowing you to build a robust library of custom commands simply by adding new text files.

Creating Dynamic, File-Based Commands for Local AI Scripts

AI models won't always give you the answer you expect; sometimes they'll return invalid data or "hallucinate" a response that doesn't fit your schema. This lesson teaches you how to make your local AI script more robust by implementing essential error handling for user commands.

You'll learn how to validate the AI's output against a predefined list of valid commands and gracefully catch errors when the model generates an unexpected response, ensuring your script fails predictably and provides helpful feedback to the user.

**Workflow demonstrated in this lesson:**

* Define a list of `validCommands` (e.g., `summarize`, `translate`, `review`) to constrain the AI's choices.
* Wrap the `generateObject` call in a `try...catch` block to handle cases where the AI's output doesn't match the Zod schema.
* Implement a type guard function (`isValidCommand`) to check if the command returned by the AI is in your list of valid commands.
* Provide clear error messages to the user, indicating what went wrong and what the valid commands are.
* Test the script with invalid commands (like "taco" or "delete") to confirm that the error handling works as expected.

By adding these checks, you make your script significantly more reliable and user-friendly, preventing it from crashing on bad input and guiding the user toward correct usage.

Handling Invalid Commands and AI Errors in Local Scripts

Once your script can understand a user's command and identify a target file, the next step is to perform an action. This lesson shows you how to use the Vercel AI SDK's `generateText` function to process a file's content and produce a relevant, AI-generated response.

You'll learn how to read the content from the file path you've already identified, combine it with the inferred command into a new, more detailed prompt, and then pass it back to your local AI model to generate a final text output, such as a summary or a review.

**Workflow demonstrated in this lesson:**

* Import the `generateText` function from the `ai` SDK.
* Read the file content from the `filePath` returned by `generateObject`.
* Construct a new, structured prompt that includes the original command (e.g., "summarize") and the full content of the file.
* Pass this new prompt to the `generateText` function.
* Log the resulting AI-generated text to the console.
* Run the script with commands like "Summarize our TypeScript config" or "Review my package file" to see the full, context-aware output.

This lesson completes the core cycle of your AI script: understanding a command, reading the relevant context, and generating a useful, text-based result, all on your local machine.

Generating Text with a Local AI Model Based on File Content

A command is useless without knowing what to act upon. This lesson elevates your local AI script by giving it context from your project's file system. You'll learn how to find all relevant files and empower the AI to intelligently select the correct one based on your prompt.

The core technique involves using the `globby` package to scan for files and then feeding that list into a Zod `enum`. This constrains the AI's choices to a valid set of files, allowing it to accurately determine which file you're referring to in a natural language command.

**Workflow demonstrated in this lesson:**

* Install and import the `globby` library to find files based on a pattern (e.g., all `.json` files).
* Add a `filePath` field to your Zod schema.
* Define the `filePath` as `z.enum(files)`, where `files` is the array of file paths returned by `globby`.
* Pass a natural language prompt like "Summarize our typescript config" to the script.
* Observe how the AI correctly identifies `tsconfig.json` from the list of available JSON files and returns it as structured data.

This lesson is a crucial step toward building powerful, context-aware tools that can intelligently interact with your project's files based on simple, human-readable commands.

Providing File System Context to Local AI Scripts with Globby and Zod Enums

Hardcoded prompts are great for testing, but a truly useful script needs to respond to dynamic user input. This lesson shows you how to make your local AI script interactive by capturing command-line arguments.

You'll learn how to use Node.js's built-in `parseArgs` utility to take a user's natural language command directly from the terminal and pass it to your local language model for processing.

**Workflow demonstrated in this lesson:**

* Import the `parseArgs` function from the `node:util` module.
* Configure `parseArgs` to accept positional arguments from the command line.
* Replace the static, hardcoded prompt with the dynamic input provided by the user.
* Run the script with different sentences (e.g., `"Download a file"` or `"I want to download a file"`) to demonstrate that the AI can correctly infer the core command regardless of the phrasing.

By the end of this short lesson, your script will be able to accept and process commands directly from the user, making it a flexible and reusable command-line tool.

Processing Dynamic User Input in Local AI Scripts

While command-line chats with local AI models are useful, the real power comes from scripting. This lesson walks you through the initial setup for creating powerful, local AI-driven scripts by combining Ollama, the Vercel AI SDK, and Zod.

You'll learn how to take a natural language command, like "Concatenate all of the markdown files in my Downloads folder into a single file," and use the `generateObject` function to intelligently extract structured, actionable data from it.

**Workflow demonstrated in this lesson:**

* Install and set up the `ollama-ai-provider`, `ai`, and `zod` packages.
* Create a local AI model instance using Ollama and the Gemma 3 model.
* Define a Zod schema to specify the exact data structure you want to extract (e.g., `{ command: string }`).
* Use `generateObject` to pass a natural language prompt to the local model and receive a structured JSON object that matches your schema.
* Run the script using `bun` to see the local model successfully infer the "concatenate" command from a full sentence.

This foundational lesson unlocks the ability to build sophisticated, automated workflows by translating human language into machine-readable commands, all running entirely on your local machine.

Scripting Local Language Models with Ollama and the Vercel AI SDK

### **Build Powerful, Local AI-Driven Command-Line Scripts**

Go beyond simple AI chats and unlock the true potential of local language models by learning to script them. This course will guide you step-by-step through the process of building a flexible and intelligent command-line tool that can understand natural language, interact with your file system, and automate complex tasks—all running entirely on your local machine.

Starting with a blank TypeScript file, you'll learn how to combine the power of **Ollama**, the **Vercel AI SDK**, and **Zod** to create a script that's not just functional, but smart. We'll begin by teaching the AI to parse a simple command from a sentence. From there, we'll progressively add layers of sophistication: processing dynamic user input, making the script aware of your project's files, and generating meaningful text-based output.

You'll also learn critical patterns for building robust and extensible tools. We'll cover how to handle invalid commands and unexpected AI responses gracefully. Then, you'll refactor the script to use a modular, file-based system for its commands, allowing you to easily add new functionality without touching the core logic. Finally, we'll add a layer of intelligence that allows your script to infer exactly what you mean, right down to the type of file you want it to process.

By the end of this course, you will have built a powerful, personalized AI assistant and gained the foundational skills to create sophisticated local automations for any task you can imagine.

**What You'll Learn:**

* How to script local language models using Ollama and the Vercel AI SDK.
* To parse and process dynamic user input from the command line.
* To use Zod schemas, including `enums`, to validate and structure AI model output.
* Techniques for providing file-system context to your scripts with `globby`.
* How to generate context-aware text based on file content.
* Robust error handling for invalid user commands and unexpected AI responses.
* To build a modular and extensible tool by defining commands in separate files.
* To intelligently infer user intent, such as dynamically determining file types from a prompt.

Build a local AI-powered command-line assistant from scratch. Learn to use Ollama, Vercel AI SDK & Zod to parse input, automate tasks, and run scripts offline.

This lesson demonstrates how to give Cursor direct access to your terminal output using the [Nx MCP extension](https://nx.dev/getting-started/ai-integration?utm_source=youtube&utm_medium=videos&utm_campaign=Nx-AI&utm_id=Nx-AI), enabling automatic error detection and fixing in monorepo projects.

**Workflow demonstrated in this lesson:**

- Encounter a runtime error when serving a Vite application in an Nx monorepo
- Install and configure the Nx Console extension with MCP integration
- Ask Cursor to check your terminal for errors and fix the underlying issues
- Watch as the AI accesses running task details and terminal output through Nx MCP tools
- Observe how it analyzes workspace relationships to identify incorrect imports and missing dependencies

**Key benefits:**

- Seamless Error Handling: AI gains direct access to terminal output and running task information
- Automatic Diagnosis: Accurately identify the root cause of errors without manual copying
- Context-Aware Fixes: Leverage workspace knowledge to implement proper solutions
- Reduced Context Switching: Eliminate the need to manually relay information between terminal and AI



Juri Strumpflohner

Enable Cursor to directly access the terminal to read the output and fix issues

See how Nx MCP gives AI coding assistants direct access to terminal output and running tasks, enabling automatic error detection and fixing.

This lesson demonstrates how to solve the predictability problem when using AI to generate code in monorepos by combining Cursor with Nx generators.

**Workflow demonstrated in this lesson:**

- Explore how Nx generators provide predictable scaffolding for new libraries
- Set up [Nx Console with MCP](https://nx.dev/features/enhance-AI?utm_source=egghead&utm_medium=videos&utm_campaign=Nx-AI&utm_id=Nx-AI) integration to expose generator capabilities to Cursor
- Use the AI assistant to brainstorm a new feature with the help of the Nx MCP tools
- Have Cursor leverage the Nx MCP tools to trigger the appropriate Nx generator for creating a React-based feature library
- Observe how the AI assistant takes the generated library and further integrates it into the codebase

**Key benefits:**

- **Predictable Code Structure**: Ensure generated libraries follow your project's and organization's conventions
- **Consistent Setup**: Maintain uniform configuration across TypeScript projects and package.json files
- **Guided Implementation**: Combine AI instructions with structured, predictable code generation

Enable Cursor to generate predictable project setups

Learn how to combine Cursor AI with Nx generators for predictable library creation in monorepos, ensuring consistent structure across your codebase.

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