 Courses from Vincent Orr

PureScript is a small strongly typed programming language that compiles to JavaScript.

purescript

PureScript has a REPL (a console you can type expressions into) called `pulp`. In this lesson, we will show how you can use `pulp` to test your modules and functions. We will use code as we would in a file, like `import`ing modules. We will also use REPL specific syntax like `:type` (or `:t` for short) to view type definitions and `:reset` to re-compile changes to our files.

Technologies talked about in this lesson:

[Pulp](https://github.com/bodil/pulp) by [@bodil](https://twitter.com/bodil)
[Psci](https://github.com/purescript/purescript/wiki/PSCi)
[Purescript](https://github.com/purescript/purescript)

Using PureScript's psci with pulp

In this lesson, we will learn how to run Purescript with a simple log statement. We use pulp to compile Purescript down to readable javascript.

Use Purescript and pulp to make hello world

Recreating web UIs manually can be time-consuming. This lesson demonstrates using Google Gemini's multimodal features to generate UI code directly from screenshots. See how to capture a webpage, provide the image to Gemini, and prompt it to generate a React/Next.js and Tailwind CSS representation. The lesson also covers refining the AI's output using annotated screenshots for corrections and integrating the generated code into a local development project, speeding up the initial UI scaffolding process.

**Workflow demonstrated in this lesson:**

*   Use browser dev tools to capture full-page screenshots of a target website.
*   Upload screenshots to Google Gemini and prompt it to generate UI code (React/Next.js/Tailwind).
*   Refine AI output using additional annotated screenshots for feedback and corrections.
*   Set up a basic Next.js project locally.
*   Use an AI-generated bash script to parse the generated code, split it into components, and integrate it into the local project structure.
*   Install necessary dependencies (like icon libraries) identified during the process.
*   Debug issues in the integrated code, potentially using in-editor AI assistance.
*   Run the local development server to view the AI-assisted UI clone.

**Key benefits:**

*   Reduces the time needed to create initial UI structures based on existing designs.
*   Utilizes AI's image understanding to translate visual layouts into code.
*   Shows a workflow for refinement using visual feedback (annotated screenshots).
*   Provides an example of integrating AI-generated code into a development project.

**Resources** 

[https://www.npmjs.com/package/@johnlindquist/file-forge](https://www.npmjs.com/package/@johnlindquist/file-forge)

**Want to Learn More? Sign-up for my Cursor Workshop!**

[https://egghead.io/workshop/cursor](https://egghead.io/workshop/cursor)

John Lindquist

Visually Clone Websites with Gemini 2.5 Canvas for Cursor

Cursor's default inline edit (`Cmd+K`) has scope limitations, and using the full chat (`Cmd+I`) for simple refactors can be overly conversational. This lesson demonstrates how to create a custom chat mode specifically configured for direct code editing. By setting up a dedicated mode with custom instructions and a keybinding (like `Cmd+E`), you can bypass unnecessary AI chatter and apply targeted, file-aware modifications more efficiently than standard methods.

**Workflow Demonstrated in this Lesson:**

*   Create a "Custom Mode" within Cursor's chat interface.
*   Assign a name (e.g., "Edit") and a preferred AI model.
*   Set a dedicated keybinding (e.g., `Cmd+E`).
*   Configure allowed tools (enable `Edit`, disable `Run`/`MCP`).
*   Use "Advanced options" to enable `auto-run`.
*   Add "Custom instructions" to prompt the AI for direct edits without explanation.
*   Trigger the mode with the keybinding on a code selection to perform edits beyond the selection's scope (e.g., hoisting variables).

**Key Benefits:**

*   Perform refactors and edits beyond the scope of the initial selection, overcoming `Cmd+K` limitations.
*   Achieve a more direct, less "chatty" AI editing experience compared to standard chat (`Cmd+I`).
*   Streamline common AI-assisted modifications with a dedicated keybinding.
*   Gain fine-grained control over the AI's editing behavior using custom instructions.

**Want to Learn More? Sign-up for my Cursor Workshop!**

[https://egghead.io/workshop/cursor](https://egghead.io/workshop/cursor)

Build a Better Edit Tool in Cursor with Custom Agent Modes

Stop waiting for a single AI agent! This lesson demonstrates a powerful workflow to massively parallelize your AI-driven development in Cursor. Learn how to use tools like `file-forge` to package your project context, prompt advanced AI models (like Gemini in AI Studio) to brainstorm features and generate setup scripts, and then leverage a custom `git worktree` CLI tool (`wt`) to automatically launch an "army" of isolated Cursor instances, each tackling a different task on its own branch.

**Workflow Demonstrated / Key Benefits:**

*   **Bundle Project Context:** Use `file-forge` to create an AI-friendly representation of your codebase.
*   **AI-Powered Brainstorming & Scripting:** Prompt a reasoning model (e.g., Gemini via AI Studio) to suggest features *and* generate a shell script (`zsh`/`bash`) to automate setup.
*   **Automated Worktree Creation:** Execute the generated script which uses a custom `git worktree` CLI (`wt`) to:
    *   Create new branches and worktrees for each suggested feature.
    *   Install dependencies automatically (e.g., `pnpm install`).
    *   Launch multiple, isolated Cursor instances.
*   **Parallel AI Development:** Assign different features or implementation approaches to each Cursor agent (Composer) simultaneously.
*   **Efficient Monitoring:** Easily switch between active Cursor projects/worktrees using `Ctrl+R` or OS spaces.
*   **Seamless Integration:** Merge completed features back into your main branch using `wt merge` or use agents to commit, push, and create Pull Requests via the GitHub CLI (`gh pr create`).
*   **Boost Productivity:** Dramatically speed up iteration and experimentation by running multiple AI agents concurrently without conflicts.

This approach transforms your workflow, allowing you to explore multiple development paths simultaneously and integrate the best results efficiently using standard Git practices.

**Resources** 

[https://www.npmjs.com/package/@johnlindquist/worktree](https://www.npmjs.com/package/@johnlindquist/worktree)
[https://www.npmjs.com/package/@johnlindquist/file-forge](https://www.npmjs.com/package/@johnlindquist/file-forge)

**Want to Learn More? Sign-up for my Cursor Workshop!**

[https://egghead.io/workshop/cursor](https://egghead.io/workshop/cursor)

Swarm Intelligence: Massively Parallelize Your AI Agents in Cursor

Creating powerful and context-aware tools for Cursor IDE becomes straightforward when leveraging information directly from your current project. In this lesson, you'll learn how to build a custom MCP (model context protocol) tool that intelligently requests essential details—such as the current project path and conversation summaries—from your Cursor project.

Workflow demonstrated in this lesson:

- Define MCP tool parameters to request specific project details.
- Pass project path and conversation summaries into your MCP tool.
- Automatically write or update files within your current project using these parameters.

Key benefits:

- Automate tasks with precision based on your active project's context.
- Simplify interactions by creating AI-driven CLI commands tailored to your needs.
- Open endless possibilities for integrating external APIs and advanced automation.

Through this practical demonstration, you'll gain a versatile technique for enhancing your development workflow in Cursor IDE.

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

Build MCP Tools Using Project Context for Custom Cursor IDE Automation

Setting up custom MCP (Machine Command Protocol) servers in Cursor can involve tedious steps, such as configuring node paths or referencing additional commands. This lesson shows how to streamline this process by bundling your MCP server directly into a standalone executable.

Workflow demonstrated in this lesson:

- Use a simple MCP server project starter.
- Run a single bundling command to create an executable.
- Right-click the generated executable, copy its full path, and add that directly to your Cursor settings.

Key benefits:

- Eliminates the need for complex node path configurations.
- Simplifies the setup and integration of custom MCP servers into Cursor.
- Makes the server executable instantly accessible within your Cursor environment.

By following this streamlined approach, you can quickly and easily integrate custom MCP servers into Cursor, making your development workflow cleaner and more efficient.

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

Easy MCP Server Setup in Cursor IDE with Bundled Executables

TanStack Router is the foundation for TanStack Start, which is gaining significant attention due to its innovative approach to application development.

This course focuses on getting effective TanStack Router, a modern and scalable routing solution for React applications. You will learn how to:
- set up and scaffold new applications,
- use file-based routing
- create nested routes
- manage dynamic route parameters and more.

The course covers advanced features like route loaders, caching strategies, and lifecycle hooks, which are crucial for enhancing application performance and user experience.

By understanding TanStack Router, you can build more efficient, type-safe, and maintainable applications, leveraging its integration with data fetching libraries like TanStack Query.

This knowledge is essential for developers looking to improve their routing skills and stay ahead in the field.

Tomasz Ducin

Up and Running with Tanstack Router and React

Learn TanStack Router for React: set up apps, use file-based routing, manage routes, and boost app performance.


In this lesson, we explore how TanStack Router integrates with the [native browser history API](https://developer.mozilla.org/en-US/docs/Web/API/History) to manage navigation.

As we navigate through different routes, the URL changes are stored in the browser's history, allowing users to use the forward and backward buttons to traverse their navigation history.

This functionality also **enables permalinks**, meaning users can copy and paste URLs to access specific routes directly.

Additionally, we learn how to programmatically interact with the history object to move forward or backward through the navigation stack, enhancing the user experience by providing full control over navigation actions.

This compatibility with the native history API ensures that TanStack Router applications behave similarly to traditional websites, offering a seamless and intuitive user experience.

Browse History in Tanstack Router

Browse history in TanStack React Router using the native history API for seamless navigation

In this lesson, we explore how to hook into TanStack Router's lifecycle hooks to execute custom logic during route changes.

The hooks we'll look into are: `onEnter`, `onStay`, and `onLeave`, which are triggered when entering, staying within, or leaving a route, respectively.

All hooks receive a detailed match parameter, providing information about the route's path, ID, and tons of other details.

This allows for the implementation of features like animations or logging when navigating between routes.

We also learn how to use these hooks to track and manage state changes as users interact with the application, making it easier to implement complex behaviors based on route transitions.

Hook into Route Lifecycle Hooks in Tanstack Router

Hook into TanStack React Router's lifecycle hooks for custom route logic and animations.

In this lesson, we learn how to handle not found errors in TanStack Router by using the `notFoundComponent` at both nested and global levels.

To trigger the display of a `notFoundComponent`, we must throw a specific error using the `notFound` function within a loader or other lifecycle method of a route. This approach allows us to provide custom error messages for specific routes or globally across the application.

We explore how to define a notFoundComponent within a specific route's configuration and how to set a `defaultNotFoundComponent` at the router global level, which applies to all routes unless overridden locally. This flexibility ensures that users receive meaningful feedback when encountering non-existent routes.

Provide NotFoundComponent at Nested Route or Global Level in Tanstack Router

Handle notFound errors in TanStack Router with notFoundComponent at nested or global levels.



In this lesson, we learn how to access dynamic route parameters using the `useParams` hook in TanStack Router.

When used with a specific `Route` object, `useParams` provides type-safe access to route parameters, ensuring that TypeScript knows the exact types of these parameters.

This approach eliminates the possibility of undefined/optional values, making it safer for accessing parameters like `employeeId`.

However, when using `useParams` globally without specifying a route, it requires an options object with `{ strict: false }`, which reduces type safety.

In this case, the hook can access parameters from any route in the application, but it does not guarantee their existence or type. This flexibility allows components to be reused across different routes, but it requires careful handling of potential errors.



Access Dynamic Route Parameters with useParams in Tanstack Router

Access dynamic route parameters with useParams in TanStack React Router for type-safe routing.

Vincent Orr

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Using PureScript's psci with pulp

Use Purescript and pulp to make hello world