Learn Chai, Tweenlite and Xstream

Chai is a BDD / TDD assertion library for node and the browser that can be delightfully paired with any javascript testing framework.

chai

In this lesson, you will learn how to verify your API server is returning HTTP 400 responses when clients submit incorrect data. Returning HTTP 400 ensures that your clients are notified of the incorrect usage. Testing for them ensures your API returns errors instead of incorrect responses when supplied with incorrect data.

Test for HTTP 400 responses with Mocha and Chai

In this lesson, we will use Chai's request method to test our Node application's API responses.
By the end of this lesson, you will know how to:
- install the prerequisites to use mocha and chai in your application
- test for HTTP status response codes
- test for a string of text on a page
- test for a json response and validate the properties of the object
- write tests that not only verify the response of your application, but the behavior as well


Test Node RESTful API with Mocha and Chai

Mocha is a feature-rich JavaScript test framework running on Node.js and in the browser, making asynchronous testing simple and fun. Mocha tests run serially, allowing for flexible and accurate reporting, while mapping uncaught exceptions to the correct test cases.

mocha

Writing great ES6 style Promises for Node.js is only half the battle. Your great modules must include tests as well to ensure future iterations don't break them. In this lesson, I show you a simple ES6 Promise in Node.js, then walk you through creating tests in Mocha using chai and chai-as-promised to test both resolve and reject methods.

Testing ES6 Promises in Node.js using Mocha and Chai

Cycle.js is a framework where your app is described as a simple function taking an event stream as input and outputting an event stream.

cycle

This lesson is the first in an introduction to Cycle.js. We will see how to build from scratch a toy version of Cycle.js. See how to use RxJS to create simple web apps, and how to separate logic from effects.

Separate Logic from Effects in Cycle.js

Now that you've got everything set up with mocha and chai, it's time to actually write tests. In this lesson you'll learn how to use `describe` and `it` to outline your tests and make sure you don't break your library in the future.

Unit Testing with Mocha and Chai

Every dependable library needs to have tests to ensure that you don't push accidental bugs. In this lesson, see how you can set up mocha and chai to start testing your library.

Setting up Unit Testing with Mocha and Chai

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

AngularJS animations and TweenLite make it really easy to create cool 3d effects in your application.

3d Animations with Greensock TweenLite

You will learn how to create and manage complex, nested schemas using a real world example (room bookings with guest details) used throughout all lessons. All created schemas are tested against potential bugs and we verify error messages.

The course covers topics such as:

- Utilizing Zod's shape and unwrap features to perform partial validations without losing the overall schema structure.
- Managing optional, nullable and nullish properties.
- Implementing unions and intersections to handle different types of data.
- Ensuring type safety with sets and maps, including custom validation logic for primitive values.
- Creating custom error messages for improved user feedback.

Get a solid understanding of how to leverage Zod to enhance runtime data validation in JavaScript/TypeScript applications 🔥

Tomasz Ducin

Advanced Type Checks with Zod

Get a solid understanding of how to leverage Zod to enhance runtime data validation in JavaScript or TypeScript applications

Get started with MongoDB and Docker really quick 🔥 even if you have no backend/fullstack experience.

This lesson guides you through installing Docker Desktop and setting up a MongoDB image.

We explain Docker images and containers, and their roles in providing isolated software environments.

We run a MongoDB image in 2 ways: using the terminal and Docker Desktop

We demonstrates configuring port settings for a container and accessing the MongoDB shell (`mongosh`) to interact with the database.

We learn how to manage Docker containers, configure settings, and run commands inside the container.

Grab the CLI command: 🔥 `docker run --name some-mongo -d mongo:latest` 🔥

Install Docker to run local MongoDB Container

Learn to install Docker Desktop, set up a MongoDB image, and manage containers. Configure ports and access the Mongo shell

[https://github.com/johnlindquist/mcp-cursor-tool-starter/tree/main](https://github.com/johnlindquist/mcp-cursor-tool-starter/tree/main)

Cursor's AI is powerful, but it can't do everything. What if you could give it the ability to interact with your specific workflows, tools, and data? This lesson shows you how to do exactly that by building a custom MCP tool – and we'll do it in under two minutes!

We'll leverage the Model Context Protocol (MCP), an open standard that bridges the gap between AI agents (like Cursor's Composer) and your custom code. By creating an MCP tool, you're essentially giving Cursor a new skill.

Here's the breakdown:

Understanding MCP: We'll briefly cover what the Model Context Protocol (MCP) is and why it's the key to extending Cursor's functionality. In short, it enables communication between the AI and your tools in a standardized way.

Fast Track Setup: We'll use a ready-made starter project (a single TypeScript file and package.json) to skip the boilerplate and get straight to the core concepts.

Step-by-Step Integration: We'll walk through the simple process of:

Cloning the starter project.

Installing dependencies with pnpm install.

Running the starter server.

Adding the tool to Cursor's MCP settings (under Features > MCP).

Verifying that your tool is working

Real-World Example: We'll demonstrate the tool in action, using Cursor's Composer to create a new GitHub issue using the gh CLI, all driven by our custom MCP tool.

This lesson is about more than just building one tool; it's about understanding how to extend Cursor's capabilities to fit your unique development needs. You'll learn the foundational steps to create a whole ecosystem of AI-powered tools.

Build Your First MCP Tool in Cursor in Just 2 Minutes

Displaying data in tables has been notoriously hard to implement. Naive implementations of data tables will leave your app slow and janky and many table libraries out there are restricted to a few select UI frameworks they use or aren't very customizable.

Tanstack Table is a 'headless' option meaning you have complete control of the presentation while taking advantage of the data side of the equation.

You'll be able to fetch and display remote data, handle pagination, and implement row virtualization for better performance.

Once you've implemented basic functionality you can take your tables to the next level customizing cells with your own JSX components, adding features like toggling column visibility, and ensuring your table is mobile-friendly with CSS pseudo-elements.

By the end, you'll have the skills to create efficient, user-friendly tables that elevate your applications. You can take what you learn here (using React) to any of the popular modern UI frameworks that fit your project requirements.

Ian Jones

Headless, Mobile-friendly Data Tables with Tanstack Table and React

Learn to display, paginate, and virtualize data effectively in a table with Tanstack table and React 19.

Install `ghx` with `npm i -g @johnlindquist/ghx`

https://github.com/johnlindquist/ghx

You've copied and pasted code, set up your project, and... *error*.  "Stream is not readable."  Sound familiar?  Generic Google searches and outdated Stack Overflow answers often leave you more confused than when you started.  You need more than just documentation; you need *context*.  You need to see how real developers solve this *exact* problem.

This lesson shows you a better way to debug, using real-world GitHub examples and the power of Cursor's Composer.

Here's the workflow we'll cover:

1.  **Identify the Error:** You've already done this!  You have a specific error message ("Stream is not readable") and the code that's causing it.
2.  **Find Real-World Solutions with `ghx`:** We'll use `ghx`, a tool for searching GitHub, to find *actual projects* that have encountered (and solved!) this same issue.  No more generic advice – you'll see working code.
3.  **Consolidate and Analyze:**  `ghx` brings those relevant code snippets into a single, easy-to-read file.
4.  **Leverage Cursor's Composer:** We'll use Cursor's Composer to directly interact with the examples and your problematic code.  Paste in your error, reference the `ghx`-generated file, and ask targeted questions like:
    *   "Based on these examples, how should I modify my code to fix the 'stream is not readable' error?"
    *   "What's the common cause of this error in the context of an MCP server?"
    *   "Show me the specific lines in the examples that handle stream readability correctly."
5. **Fix the Error:** Implement the solution using this workflow.

Instead of guessing, you'll learn directly from proven solutions.  You'll gain a deeper understanding of your issue and how to troubleshoot it effectively.  This approach transforms debugging from a frustrating hunt into a targeted learning experience.

Fix Real-World Coding Errors with GitHub Context through ghx and Cursor Composer

Learn how to test custom ESLint rules with Vitest and RuleTester.

In the [previous video](https://egghead.io/set-up-a-custom-es-lint-rule~kq49h), we created a custom eslint rule that gives us a warning when some of the class names don’t start with a lowercase letter.

Here, I'll show you how to test this or any other custom ESLint rule using unit tests without manually restarting the ESLint server or modifying production code to check if the rules work correctly. We'll look at the structure in which correct and incorrect code samples should be specific to RuleTester.

We'll also learn how to make our rule fixable and verify that the applied fix works as expected.

Test Custom ESLint Rules with Vitest and RuleTester

Learn to test a Custom ESLint Rule using Vitest and RuleTester.

AI is amazing for generating code, but often leaves us staring at functions and libraries we've never seen before. This creates a huge problem: When things break (and they will break), we're lost. We don't understand the individual pieces, let alone how they interact. Knowing one part isn't enough; we need to see how unfamiliar components work together.

The solution? Real-world examples.

This lesson introduces ghx, a tool I built to do exactly that. ghx searches GitHub, finding actual code examples that use the specific combinations of functions and libraries that are tripping you up. Instead of generic documentation, you get concrete implementations. You can find the ghx repository at https://github.com/johnlindquist/ghx.

Here's the workflow we cover:

Identify the Unknown: Pinpoint the AI-generated code you don't understand – specific functions, libraries, or their interactions.

Search with ghx: Use ghx to search GitHub for examples using those terms together. ghx finds repos where those elements co-exist.

Consolidate Examples: ghx combines the relevant code snippets from those examples into a single, digestible file.

Ask the AI (Again, but Better!): Feed this consolidated code to an AI (like a coding-focused model in an AI studio). Now, instead of asking general questions, you can ask targeted questions based on real-world usage:

"Explain how these functions are used together in these examples."

"What are some common use cases for this combination of libraries?"

"Show me alternative code snippets that achieve the same result."

"Can you identify potential problems or areas for improvement, based on these examples?"

Iterate. Use your improved understanding to make changes, and then repeat the process to debug.

By leveraging ghx and targeted AI prompting, we transform from being lost in a sea of unfamiliar code to actively learning from how others have solved similar problems. We gain a deeper understanding of our AI-generated codebase, making debugging, modification, and future development far more manageable. This gives you the tools to understand, debug and improve your own code.

How to Work with AI-Generated Code You Don't Understand with ghx and Gemini

In this less we create a custom Zod schema using `z.custom`.

The example case covers room numbers validation: it must start with an uppercase letter followed by three digits. `z.custom` allows the creation of a brand new custom schema with a callback function that returns a boolean indicating validity.

We highlight a challenge with type inference in Zod's custom schemas: callback parameter types don't get aligned automatically - type-safety must be explicitly defined using type parameters.

We run tests against various room numbers and demonstrate how to effectively validate inputs and manage error messages.


Create Custom Zod Schemas and Validate project-specific data

Create custom validation logic in Zod using z.custom, for primitive values, ensuring type safety and proper error messages for room numbers.


In this lesson we make use of partial validation of large and nested schemas in Zod.

Instead of creating many small schemas, which can complicate maintenance, Zod allows for the extraction of specific parts of a schema using the concept of ".shape". It "unwraps" the object schema, enabling access to sub-schemas within specific properties.

Additionally, we can use the `.unwrap` to get into element schemas of altered schemas such as `.optional` or `.array`.

Simplify working with complex schemas without the need to break them into hundreds of small schemas.

Use Zod Schemas partially using shape and unwrap

Manage large and nested Zod Schemas by extracting partial validations using shape and unwrap utilities

In this lesson we cover implementing sets and maps in Zod.

Using `z.set`, a schema for string-based amenities is created. We demonstrate how validation works by intentionally introducing errors and customizing error messages for better clarity.

Next, a map is created for existing guestDetails schema using `z.map`, with string keys and an existing `z.object`-based schema. The validation process is shown again by deliberately breaking the schema, such as omitting the age property, which triggers appropriate error messages.


Validate maps and sets using Zod schemas

Use sets and maps in Zod for schema validation, ensuring type safety for both keys and values.

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