Learn Aria, Egghead and Vue

Mock Service Worker is an API mocking library that allows you to write client-agnostic mocks and reuse them across any frameworks, tools, and environments.



Mocking WebSocket APIs with Mock Service Worker

**Note:** This course is being actively developed and more lessons will be added in the future.

Cursor is an AI-enhnaced code editor that allows you to call upon the robots to refactor or stub out functions for you with natural language. 

In this course, John will show you workflows that will save you time and effort by utilizing the features such as its mult-line editing and context.

In particular, by giving Cursor's AI the proper context, whether that's lines of code or multiple files, you'll get better output when trying to extend or refactor logic that's present.


Bite-size video tutorials for badass web developers.

egghead

Productive Developer Workflows with AI Enhanced Cursor IDE

Vue (pronounced /vjuː/, like view) is a progressive framework for building user interfaces. Unlike other monolithic frameworks, Vue is designed from the ground up to be incrementally adoptable. The core library is focused on the view layer only, and is very easy to pick up and integrate with other libraries or existing projects. On the other hand, Vue is also perfectly capable of powering sophisticated Single-Page Applications when used in combination with modern tooling and supporting libraries.

There are several components from the `@tresjs/cientos` that you can use to enhance your **TresJS 3D scenes**.

- `ContactShadows` for performant fake shadows
- Wrap models with `Levioso` for subtle floating animations
- Add real HTML markup to your animations with `HTML`

Using these abstractions from `@tresjs/cientos`, you can create polished **3D experiences**.

Enhance Your TresJS Scenes with the @tresjs/cientos Package

In this **TresJS** lesson you will see how to connect a 3D model to your application state. You'll learn to:

- Access your model's material colors using the `materials` property from `useGLTF`
- Use Vue's `watch` function to update the model's colors when the user changes the selection in the UI

This allows you to create an interactive 3D experience that responds to user input.

Connect a TresJS Model with Vue State

In this lesson, you'll learn how to create a reactive color selector in Vue using the `useState` composable function. We'll cover:

- Creating a shared state that contains base colors and a selected base color
- Using the `toRefs` utility to convert reactive properties to refs
- Importing and using the `useState` composable in components
- Iterating through colors to create buttons
- Assigning click event handlers to update the selected color reactively

Create a Reactive Color Selector in Vue with useState

In this **TresJS** tutorial, we'll explore how to find and use free 3D models in GLTF format from sites like:

- [poly.pizza](https://poly.pizza/)
- [Sketchfab](https://sketchfab.com/)
- [pmndrs Market](https://market.pmnd.rs/)

We'll then cover using the `useGLTF` composable from `drei-sfc` to load models asynchronously, handling it gracefully with Vue's `<Suspense>` component. 

Once loaded, you will see how to explore the object and get the model node you're looking for. Then, you'll pass that node to the Vue `primitive` component's `:object` prop to render the model. 

By the end of the lesson, you'll be ready to start adding awesome 3D models to your own TresJS projects!

Load Models with useGLTF and the TresJS primitive Component

This is an overview of the [TresJS project template](https://github.com/alvarosabu/egghead-tres-3D-product-configurator/tree/main/lessons/starter).

The template is styled with the atomic CSS framework, [UnoCSS](https://unocss.dev), and comes with a default TresJS scene setup, including:

- a perspective camera
- ambient light
- directional light

There is also a color selector initially implemented using an array of colors and a `ref` variable for the selected color. Each color is rendered as a circle. 

Project Overview of the TresJS Product Template

In this **TresJS** lesson you will learn how to use `OrbitControls` from the [@tresjs/cientos](https://cientos.tresjs.org/) package to add interactivity to your scenes by:

- Importing `OrbitControls`
- Adding it below your `TresPerspectiveCamera` component

The `@tresjs/cientos` package is part of the TresJS ecosystem and provides useful abstractions and composables to extend the core framework. 


Use OrbitControls to Interact with your TresJS Scene

In this **TresJS** lesson, we explore how to animate 3D objects in a scene using the render loop. 

We'll cover:

- Using the `useRenderLoop` composable and `onLoop` callback to run code every frame
- Creating refs with `useRef` to reference and modify 3D object properties like position and rotation
- Handling varying device frame rates with `delta` or `elapsed` time for consistent animation speed

By the end of the video, you'll be ready to create smooth animations in your own TresJS scenes.

Animate TresJS Objects with the useRenderLoop Composable

In this **TresJS** lesson, you'll learn how to **enable shadows** in your 3D scenes. 

We'll cover: 
- the `shadows` prop to turn on shadows
- the `cast-shadow` property for lights
- and the `cast-shadow` and `receive-shadow` properties for meshes. 

You will see how to adjust light intensity, position, and object placement to **control the appearance of shadows**. By the end, you'll understand the key aspects of configuring shadows in TresJS scenes.

Enable Shadows in Your TresJS Scene

This TresJS lesson teaches you the basics of lighting 3D scenes. 

You'll learn how to:

- add ambient lights for overall lighting `<TresAmbientLight />`
- add directional lights for light from a specific direction `<TresDirectionalLight />`
- change light color and intensity using theprops. 

You will also see how materials, such as the `<TresMeshStandardMaterial>`, work with the lights in your scene.

Add Ambient and Directional Lighting to Your TresJS Scene

This TresJS lesson shows you how to transform 3D objects in your scene. You'll learn how to:

*   Adjust an object's position with `TresMesh`'s `position`, `scale`, and `rotation` props   
*   Use the `TresAxesHelper` component to guide object transformations in 3D space
    

Transform TresJS Objects Using Props

Learn how to add a 3D cube to your TresJS scene using the `TresMeshComponent`, `TresBoxGeometry`, and `TresMeshNormalMaterial` components. 

This lesson shows TresJS's declarative approach that makes 3D rendering simpler than using plain Three.js.

Add a Cube to Your TresJS 3D Scene

[TresCanvas](https://docs.tresjs.org/api/tres-canvas) is a Vue.js component that wraps the Three.js renderer. 

It can be imported, added to templates, and customized with props like background color and window size.

Add a TresCanvas to Your Vue App

[Link to the course repo](https://github.com/alvarosabu/egghead-tres-3D-product-configurator)

Our goal is to create an application that allows the user to customize a 3D product with different color materials visually. The first step is setting up the starter to follow up during the course

In this first lesson, we will download the course repo ‣. The repo is a monorepo containing all the lessons available for this course. When you clone it you are going to find

Getting Started with the TresJS Product Course Repo

Create Interactive 3D Experiences with TresJS

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

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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