Learn Cursor, Electron and Tachyons

**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

Functional css for humans.

Quickly build and design new UI without writing css.

tachyons

Tachyons provides extensions (-ns, -m, and -l) to many of its classes to help you design for responsive layouts. This lesson walks you through designing for mobile first then adding a design for desktop.

Design for Mobile First with Tachyons

Building responsive css layouts is critical in any modern website. Tachyons makes this easy by designing for mobile first then enabling you to switch directions and positions of elements when the site scales up to desktop sizes. This lesson walks you through using Tachyons with flexbox and leveraging the `flex-column` and `flex-row` classes to respond to mobile and desktop sizes.

Build Responsive CSS Layouts with Tachyons

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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Productive Developer Workflows with AI Enhanced Cursor IDE

Design for Mobile First with Tachyons

Build Responsive CSS Layouts with Tachyons