Learn Jest, Netlify and Stripe

Next.js is a minimalistic framework for server-rendered React applications.

You will implement stripe checkout in your application so that customers can buy products.

To do this, you will first create a Next.js API route that starts a stripe session and passes in products that are in the cart. This will forward users to the Stripe checkout page where they can input their credit card information.

Upon success or cancel, stripe will forward the user back to urls that you set.

Accept Payments through Stripe Checkout using Next.js API Routes

snipcart

While we have a lot of options for the different payment gateways that we can use in order to take payments, we're going to use [Stripe](https://stripe.com/), which is a well-known secure payment processing platform, where it's going to be able to easily integrate into our Snipcart account.

Once we add our Stripe account, we will head over to the Snipcart dashboard where we will connect Stripe as our payment gateway, which simply allows us to go through an authentication process, where we will have Stripe configured.


Use Stripe as a Payment Gateway in Snipcart

jest

Jest is a fast and safe testing framework with a focus on simplicity. 
This lesson will walk you through how to add jest to your application by running `yarn add --dev jest` followed by updating your `package.json` to be able to run your tests on the console by using the `yarn test` script.
This lesson covers how to create your first test by creating your first block that groups several related tests with `describe` and then adding your first test method using either the `it` or `test` keyword.

Add Jest to Your Application and Create Your First Test

An all-in-one workflow that combines global deployment, continuous integration, and automatic HTTPS.

netlify

In this lesson, you'll learn how to set up a new Vite project and configure it to use React with TypeScript.

Set up a Vite project with React and TypeScript

In this lesson, you'll globally install the Netlify CLI and use its dev command to run your site locally. Netlify automatically detects if it's a Vite project!

Set up Vite local dev with the Netlify CLI

React is one of the web’s most popular frameworks for building JavaScript applications. 

If you know what you’re doing, React can drastically simplify how you build, use, and maintain code.

Whether you’re a React newbie or you’re ready for advanced techniques, you can level-up with egghead. Explore our in-depth courses, lessons, and community resources to build more powerful applications and crack open your career.

react

Netlify has the power to automatically detect serverless functions in your project and run a functions server with hot reloading for you. 

In this lesson, you'll learn how to create serverless functions in your Netlify app using TypeScript

Add TypeScript powered serverless functions to a Netlify project

In this lesson, you will learn how to create a Netlify serverless function, powered by TypeScript, to handle form submissions.

Create a serverless form submission handler with TypeScript

In this lesson, you'll learn how to set up a submit handler that posts your form data to a serverless function.

Submit form data to a Netlify serverless function

After all your hard work, it's time to deploy your app! 

In this lesson, you will learn how to get your project onto GitHub and deploy it using Netlify

Deploy a Vite project with the Netlify CLI

Supabase is an open source Firebase alternative. A service to:

- Listen to database changes.
- Query your tables, including filtering, pagination, and deeply nested relationships (like GraphQL).
- Create, update, and delete rows.
- Manage your users and their permissions.
- Interact with your database using a simple UI.

Supabase is not a 1-to-1 mapping of Firebase. Most notably, Supabase uses Postgres rather than a NoSQL store. 

supabase

Every transaction in Stripe is associated with a customer. Since we want our user to be able to see their transaction history in Stripe, we need to keep a track of their customer ID in our `profile` table.

In this video, we create a new Stripe account and configure our Next.js application to use its publishable and secret API keys. Additionally, we discuss by adding `NEXT_PUBLIC_` to our environment variable names, it exposes them to the client - running in the user's browser. This is entirely safe for our publishable key - which can be public - but not our secret key - which should always be kept private.

To create a Stripe customer, we need to use our private API key, meaninh this logic will need to be executed on the server. Next.js makes this simple with API routes - special serverless functions that will be created for any `.js` files in the `pages/api` directory.

We create a new API route to handle adding a stripe customer and updating the user's profile with their customer ID. 

Create a Stripe Customer with Next.js API Routes

Each of our subscription options will be a product in Stripe. In this video, we create a `/pricing` page to display our new products. We use the Stripe package to fetch these options at build time and add some simple styling to make it look nice!

Annoyingly, the `prices` list is what we need to request for pricing information, but does not contain product information - such as `name`. This means we need to make an additional request for the product information for each price. Good thing we are only doing this once at build time, as the request time could add up pretty quickly, if we needed to do this on every request!

Lastly, we sort the list of plans from Stripe, to ensure that the prices go up in ascending order. Since each plan has a price, we can use a simple native JS sorting technique.

Use Stripe.js to Query Product Data and Pre-Render with Next.js

Stripe makes taking payments easy. We can use the Stripe library to create a checkout session. This takes a configuration object, which describes how we want to charge our customer, the method we would like to use, and where we want to redirect the user based on a successful transaction or cancellation.

In this video, we create a new API route to initiate a Stripe session to charge for a particular subscription option. This API route returns the session's ID, which we can use to redirect the client. In order to do this, we install the `stripe-js` library - which is used client-side to interact with Stripe - and initialize it using our `publishable` key.

Lastly, we can use the test credit card for Stripe to confirm everything is working in test mode.

Charge Customer for Stripe Subscription in Next.js

Webhooks allow us to subscribe to particular events, and have Stripe let us know when they occur. From the Stripe dashboard we can specify an endpoint to call, and which events we care about. For subscriptions, we want to know when a new subscription occurs, is updated, or deleted.

In this video, we create a new API route to handle webhook events from Stripe. We can call the `stripe.webhooks.constructEvent` function which returns the event object. It also takes the `req`, `signature` and `signingSecret` to verify the request is valid and came from Stripe.

The `signature` can be extracted from the request's headers, and the signing secret is available in the Stripe dashboard, however, the Next.js `req` object is a different structure to what `constructEvent` is expecting. Therefore, we need to install the `micro` package and use its `buffer` function to get the `reqBuffer`. We also need to export a config object from our page component, setting `bodyParser` to `false`.

Subscribe to Stripe Webhooks Using Next.js API Routes

We can use Stripe's Customer Portal to allow our users to manage their subscription and payment information. We will configure the Customer Portal in the Stripe Dashboard and create the required pages for `/terms` and `/privacy`.

Additionally, we create an API route to initiate our customer portal session. Similarly to our API route that charges for a subscription, we need to get the token from the cookie, add it to the Supabase session and fetch the associated `profile` data. 

Since the `updated` event gets triggered for creating a new subscription, as well as updating an existing one, and we want to perform the same changes in our Supabase DB, we can replace our case statement to handle `customer.subscription.updated`.

Lastly, we implement a case for `deleted`, setting `is_subscribed` to `false` and `interval` to `null`. Since this event doesn't actually trigger until the end of the customer's billing cycle, we can manually cancel the user's subscription from the Stripe dashboard.

Allow Customer to Manage Their Subscription with Stripe Customer Portal

There are a few steps to go through to push our application and all of our services to production!

1. In order to "activate" our Stripe account, we need to fill out some details about our business. 

2. Update everywhere `[http://localhost:3000](http://localhost:3000)` is referenced in our application to use the `CLIENT_URL` environment variable, which can be set to localhost in `.env.local`.

3. Create a GitHub repo, and commit and push all of our changes.

4. Create a Vercel account, import project from GitHub, configure all environment variables and deploy!

5. Use our Vercel domain to configure our Customer Portal and Webhooks in Stripe.

6. Update `STRIPE_SIGNING_SECRET` environment variable in Vercel and manually redeploy.

7. Update our Site URL in Supabase and remove our test `user` and `profile` records.

8. Create a function hook in Supabase to use our Vercel domain

We now have a SaaS application deployed to the Internet that we can start making that side hustle money from!! 🎉

Configure Stripe for Production and Deploy Next.js Application with Vercel

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

We can add accessibility testing to our `jest` unit tests with `jest-axe` which gives us:

* An easy way to automate accessibility auditing
* the flexibility to audit at any level of our code, as low as an element and as high as a component representing an entire page of our app
* A safety net for any accessibility regressions

Here we'll see how to use `jest-axe` with `react-testing-library` to render the components for `jest-axe` to audit for accessibility.

The React Testing Library is a very light-weight solution for testing React components. It provides light utility functions on top of react-dom and react-dom/test-utils, in a way that encourages better testing practices.

## Resources
* jest-axe: https://github.com/nickcolley/jest-axe
* react-testing-library: https://testing-library.com/docs/react-testing-library/intro/

Write Better Unit Tests with react-testing-library and jest-axe

We can add accessibility testing to our `jest` unit tests with `jest-axe` which gives us:

* An easy way to automate accessibility auditing.
* The flexibility to audit at any level of our code, as low as an element and as high as a component representing an entire page of our app.
* A safety net for any accessibility regressions.

Here we'll see how to use `jest-axe` with `enzyme` to render the components for `jest-axe` to audit for accessibility.

Enzyme is a JavaScript Testing utility for React that makes it easier to test your React Components' output. You can also manipulate, traverse, and in some ways simulate runtime given the output.

## Resources
* jest-axe: https://github.com/nickcolley/jest-axe
* enzyme: https://enzymejs.github.io/enzyme

Write Unit Tests for Accessibility Using jest-axe and enzyme

We can add accessibility testing to our `jest` unit tests with `jest-axe` which gives us:

* An easy way to automate accessibility auditing.
* The flexibility to audit at any level of our code, as low as an element and as high as a component representing an entire page of our app.
* A safety net for any accessibility regressions.

Here we'll see how to use `jest-axe` with `ReactDOM` to render components for `jest-axe` to audit for accessibility.

## Resources
* https://github.com/nickcolley/jest-axe

Render Components with ReactDOM to Write Tests with Jest and jest-axe

Best practices for building & testing redux applications have changed dramatically over time. This course aims to be a comprehensive yet understandable resource for those seeking to improve the testing of their redux applications.

This course is broken up into three parts.

The intro is look at how to add jest to an existing react redux application that was built on top of [vite](https://vitejs.dev/). We cover basic functionality like [watch mode](https://jestjs.io/docs/cli#--watch) and generating and using [code coverage](https://jestjs.io/docs/cli#--coverageboolean) to inform our testing strategy.

The first section covers the specifics of unit testing the redux code in our application. We'll go over how to test reducers, selectors and thunks. As part of this we'll touch on mocking APIs and using [Redux Mock Store](https://github.com/reduxjs/redux-mock-store) as well as running actions through our applications' actual reducers to broaden our test coverage.

Finally we'll look at how we can test our redux connected react components using the various utilities that come with the [React Testing Library](https://testing-library.com/docs/react-testing-library/intro/). We'll cover simulating various kinds of user input with the [user-event module](https://testing-library.com/docs/ecosystem-user-event), populating our redux store with test data, using [jest spy](https://jestjs.io/docs/jest-object#jestspyonobject-methodname), how to wait for asynchronous tasks to complete, and several approaches for finding & asserting that visual elements exist on the page.

This course is built on top of content that was created for the [Modern Redux with RTK and TypeScript](https://app.egghead.io/playlists/modern-redux-with-redux-toolkit-rtk-and-typescript-64f243c8) course and I highly recommend you start there if you haven't completed it already. 

I'm confident you will level up your redux testing abilities if  watch these lessons and practice coding each of them as you go.

Intro to Confidently Testing Redux Applications with Jest & TypeScript

Intermission Pep Talk - Testing Redux Applications

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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