Learn Cypress, Elm and Tweenlite

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.

Some components render differently based on the route they are rendered on. To use router within component tests, it is best to define its default values in `cy.mount()` function. Since we can customize this in any way we’d like, we can create a custom `path` attribute where we can pass our own routes and test our component behavior against them.

Test a Component Rendering Within Different Router Locations in Cypress Component Test

Every component test that uses Pinia needs to have Pinia imported and initialized. Instead of doing this separately for each test you can customize your `cy.mount()` command to automatically use a default store. You can further then customize it to use your own store in case you want to change its state.

Rewrite Cypress mount() Command to Provide a Default Store to all Components

Instead of calling an action function to change the state, we can choose to change it directly by importing the attributes from our store. In order to not loose reactivity while destructuring properties we can use `storeToRefs()` function.

Mutate Application State Properties Directly with Pinia’s storeToRefs()

Fast, easy and reliable testing for anything that runs in a browser.

cypress

When component uses a state management tool like Pinia, it needs to be defined within the test. To use Pinia inside the Cypress component test, we need to create a Pinia instance and pass it into our store. For changing the state we can call our actions directly from within our test and assert different attributes of the component with Cypress commands

Call Pinia Store Actions From Within a Cypress Component Test

`emit()` function enables components to communicate with one another. While this communication is a good candidate for end to end test, we can also test this in isolation within component. To do this, we’ll use `cy.spy()` function that will watch for the defined function call. Using this, we can confirm that our emitted event was actually invoked.

Use Cypress cy.spy() Function to Test a Vue 3 Component’s Emitted Events

By passing a `slots` option to our `cy.mount()` function, we can define content to be rendered inside our current component. We can use default or named components and eve use Vue’s `h()` function to create element and pass components with desired properties.

Render a Vue 3 Component with <slot /> and Test its Content Properties

Some components can have properties passed into them, For these, we can use an options parameter in our `cy.mount()` function and pass `props` attribute which will define the properties we want to pass into our component. This way we can test whether different properties correctly render out in our component.

Pass Properties Into a Vue 3 Component Within a Cypress Test

Importing styles is going to be essential for all of our components to render properly. We will do this similarly as we do in our application - at top level. `component.ts` file is the proper file for this, as this is the one that gets loaded before running our specs. Importing our `index.css` file here will make all of our components render with the correct styles.

Setup Global CSS Properties for Cypress Component Tests

To create our first test, you’ll create a spec file that pairs with the component. Usual naming convention is using `filename.cy.ts` (or .js), but this can be easily set up in `cypres.config.ts` file. After you create the spec file, you can mount the component by importing it and using cy.mount() command inside an `it()` block. This will render the component inside Cypress app, but will not load all the dependencies that the component needs, like css styles. These need to be imported into the spec file manually. Since we are running our component in a browser and inside a Cypress app, we can interact with it using common Cypress commands.

Mount and Test a Vue 3 Component in Cypress

To start with component testing in Cypress you need to open Cypress and go through setup wizard that will help you out with your first steps. It will take you through a couple of steps, where you’ll install dependencies and create important configuration. Most of this is done for you, but you can customize anything. In our application, we will be working with Vue 3 framework with Vite bundler.

Setup Vue 3 Component Testing in Cypress

[Storybook](https://storybook.js.org/) is already powerful for being able to develop components in isolation as well as using those stories as living documentation for others. [Nx](https://nx.dev) goes a step further. It leverages the fact that Storybook exposes an `iframe.html` that allows to access the rendering of the story without the actual Storybook frame. On top of that, Nx generates a Cypress configuration to run tests against the Storybook stories.

Prefer to read along as well? Here's the [accompanying article](https://blog.nrwl.io/use-cypress-with-next-js-and-nx-to-battle-test-your-react-components-da273f664f81).


Write Cypress Tests for our React Storybook Components with Nx

[Nx](https://nx.dev) promotes best practices by automatically generating [Cypress](https://www.cypress.io/) based e2e tests for all applications inside an Nx monorepo workspace. Not only does Nx take away the burden of configuring Cypress, but it also allows to easily serve Cypress in "watch" or headless mode. The latter is particularly useful to also run in your CI pipeline. When you run Cypress test with Nx, it automatically makes sure to first launch the tested application, then run Cypress and point it to the running application. 

Prefer to read along as well? Here's the [accompanying article](https://blog.nrwl.io/use-cypress-with-next-js-and-nx-to-battle-test-your-react-components-da273f664f81).

Write e2e Tests for our Next.js Application with Nx and Cypress

The pluggable linting utility for JavaScript and JSX.

eslint

We can leverage GitHub Actions to automate the running of all of our accessibility auditing tools as part of a Continuous Integration (CI) workflow. 

Auditing tools will only catch a percentage of potential accessibility issues, it is no replacement for manual testing.  But being able to automate the auditing will add efficiencies to our testing and provide an early warning system that can be run much more frequently than it’s practical to perform manual testing.  Additionally, we can use our CI suite as a safety net to prevent code changes that break accessibility from being able to be merged.

## Resources
* https://app.egghead.io/lessons/react-set-up-eslint-to-audit-accessibility-issues-in-react
* https://docs.cypress.io/guides/continuous-integration/github-actions#Basic-Setup
* https://github.com/cypress-io/github-action

Automate Accessibility Audits with GitHub Actions

“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

Server may respond differently when providing a response to a logged in user. Usually, a user is identified when an authorization header is sent with the request. With .intercept(), we can dynamically add a header to a request and skip the login process. Server will provide the same response as it would when a user would go through login process.

Send Network Requests with Authorization Headers in an Intercepted Request with Cypress

When user waits too long, we might want to give them an option to reload page and try again. This is usually a hard case to reach and test effectively. But the .intercept() command provides us with a `throttleKbps` option that can limit the bandwidth, or we can use `delay` option that will delay our response for a given time.

Test Slow Network Conditions in Cypress by Throttling and Delaying Intercepted Requests

Servers sometimes use entity tags that identify a user and provide them with a cached response if needed. This can create ambiguity in your tests. To make your tests more stable, you can modify the request headers that take care of this caching. By deleting `if-none-match` header with `.intercept()` command, you will get a fresh response from server every time.

Prevent Response Caching in Cypress by Deleting 'if-none-match' Request Header

When a static response is not enough, we can take the real data from server and modify it to our liking. This saves a ton of time, because we don’t need to create the data ourselves. Instead, we can take e.g. response body and change only a desired attribute. This gives us one more advantage, because we can first make assertions on real data, and only then make changes. This is a great advantage compared to providing a static response, that essentially creates a blind spot for any API changes.

Dynamically Combine Real and Mocked Response Body Data in Cypress

When you don’t want to stub your response or create your data before the test, you can change the request your application makes altogether. If your server recognizes different queries, you can modify your request‘s URL and add a query that will specify what kind of data you want to get.

Dynamically Add Queries in Cypress Intercept Function Requests

When testing certain user stories, you may want to start with a clean state, but then return to the initial page and see the data you have created. This can be problematic if you are stubbing your requests. Luckily, you can limit how many times you a request should be intercepted.

Configure cy.intercept to Only Intercept a Network Request Once 

Instead of explicitly providing the exact body, you can choose to save the data you want to use in a separate file. When you create a file in the fixtures folder, you can reference it using `fixture` option in the .intercept() command. This will automatically look into the fixtures folder and load the data from the file.

Use a Fixture in Cypress to Provide Response Data to Network Requests

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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