 Courses from Paul Frend

JavaScript® (often shortened to JS) is a lightweight, interpreted, object-oriented language with first-class functions, most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic, and supports object-oriented, imperative, and functional programming styles.

javascript

Our helpers are now powerful enough to work with any data type as the source, but they can only build up arrays or objects as the target. For an arbitrary collection type, we thus have to fall back to our `transduce` function and manually specify the inner reducer behavior based on the API of our target collection. 

In this lesson, we'll add support for the transducer protocol, and see how it enables us to transduce into any collection type without having to fall back to using `transduce`. We'll use the `List` type from [Immutable.JS](https://facebook.github.io/immutable-js/) as our example.

Add the Transducer Protocol to Support Arbitrary Data Types as our Target Collections

Our `transduce` function is powerful but requires a lot of boilerplate.
It would be nice if we had a way to transduce into arrays and objects without having to specify the inner reducer behaviour, i.e. how to build up values, since a given collection type will almost always have the same inner reducer.

In this lesson we'll being doing just that with an `into()` helper function that will know if an array or object collection is passed in and handle each case accordingly. 

Make an Into Helper to Remove Boilerplate and Simplify our Transduce API

So far we've been transducing by manually calling `.reduce()` on arrays, but we want to be able to transduce over other collection types as well.

In this lesson we'll create a `transduce` function to support transducing over any data structure that implements the [es2015 iterable protocol](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Iteration_protocols). We’ll put it to the test by transducing over strings and maps, as well as going from one collection type as input to another as output.

Transduce over any Iteratable Collection

To make our composition more readable and easier to name we are going to ceate a `compose` function we can use to avoid having to manually nest our transducer calls. 

We'll also go over semantically naming your `compose` functions for extra readability. 


Improve Composition with the Compose Combinator

A frequent use case when transducing is to apply a transformation to items without changing the type of the collection.

In this lesson, we'll create a helper to do just that. `seq` will be similar to `into`, except the target type will be inferred from the source collection. For example, if transducing from an array, `seq` will create an array as the output collection as well.

`seq` is thus more restrictive and easier to consume than `into`.

Create a Sequence Helper to Transduce Without Changing Collection Types

We've seen how we can `transduce` from arrays or other iterables, but plain objects aren't iterable in Javascript.
In this lesson we'll modify our `transduce()` function so that it supports iterating from plain objects as well, treating each key value pair as an entry in the collection.

To do this we'll be using a **lodash** function called [entries](https://lodash.com/docs/4.17.4#toPairs). 

Transduce When the Collection Type is an Object

While it’s possible to use our transducers in production (after writing some tests!), I would recommend using a proven transducer library instead.

In this lesson, we’ll go through how to use [transducers.js](https://goo.gl/QpWAwT) from [James Long](https://goo.gl/e4Jtsr) instead of our functions. We’ll also cover some more advanced features which we didn't add support for ourselves, like short-circuiting iterations with the `take` transform as well as transducing lazily over iterables.

The aim is that you’ll be able to use these libraries with more confidence and better understanding since we’ve been through how things are working under the hood.

You can also check out [transducers-js](https://github.com/cognitect-labs/transducers-js) by [cognitect labs](https://github.com/cognitect-labs), which is very similar in its feature set to transducers.js.
And remember, you don’t have to rely solely on the transforms in the library, you can write your own as well, as long as you support the transducer protocol.

Use Transducers in Production

In our previous lesson, we created `map()` and `filter()` functions based on reduce, but we still couldn’t compose them together.

In this lesson, we’ll solve this by making these functions into transducers. This will let us compose the reducing behavior of `map()` and `filter()`, without coupling this composed transform to a data type.

Create Map and Filter Transducers

Two of the most useful operators when working with collections are `.map()` and `.filter()`. We want our **transducers** to be able to combine these operations in a single pass when iterating through our collections. To do this we need to find a commonality between the two, so we can treat them the same. To achieve this common ground, we'll create `map()` and `filter()` functions which use the built in `.reduce()` function under the hood instead, and we’ll start to combine the concepts of transforming and reducing.

Rewrite Map and Filter as Reducers

Now that our transducer functions are functioning as expected, it’s time for another round of performance testing.
We’ll use the same `timeIt()` function from [Lesson One](https://egghead.io/lessons/javascript-understand-why-transducers-can-improve-performance) to see how our transducers compare to the Array built-in methods, an imperative solution, as well as the external transducer library [transducers.js](https://github.com/jlongster/transducers.js?files=1).


Measure the Performance of our Transducers

Now that we understand the problem transducers can help us with, it's time to define what they actually are.
A Transducer is a combination of two concepts, a *transform*, and a *reducer*. **Trans-Ducer**.
In this lesson we'll give a general overview of the *reducer* part of the puzzle. 


Write Reducers for Different Data Types


The next transducer building block we need to explore is a **transformer**. A transformer is a function that takes a value and returns a transformed version of it.

In this lesson, we cover some examples of transformer functions and use them with some basic composition. We'll follow the functional style of creating new values, avoiding mutations. Even though they're trivial functions, it's important to understand how they work, as soon we’ll be combining them with reducers to create transducers. 

Understand Transformer Functions

Chaining operations like `map` and `filter` on an array can have performance issues as we have to iterate through the whole array for every operation.

In this lesson we explore this problem by doing some basic array transformations and measuring their performance.

 We’ve got two utility function to help us with this:
* `arrayOfRandoms` is a higher order function which helps us create a range of random numbers
* `timeIt` will help us measure how long functions take to execute

It's important to understand this problem and why it occurs before we move on to how transducers handle it differently.

Understand why Transducers can Improve Performance

In this course, we will go through the concept of transducers and build them up from basic principles. We will go over the problems with regular composed transformations, and show how we end up with unnecessary data and intermediate collections. We go on to learn how we can use transducers for performance gains when operating on large data sets, as well as how they’re an excellent fit for processing other iterable data, like generators. Not just arrays.

Quickly Transform Data with Transducers

A modern JavaScript utility library delivering modularity, performance & extras.

lodash

Imagine if looking at your business logic read as simple as a story from a children's book. That's exactly what we'll cover by learning how to compose small pieces of business logic and also set them up for further composition in the future.

We're showing a list of fictional tweets in a trivial React component created with create-react-app, however the example is not React specific and can be applied to any framework/environment.

Our example will refactor out business logic from a non-composable function to small composable pieces. To accomplish this we'll be using flow, filter and map from lodash/fp, which is included in the regular lodash npm package.

This approach works well in any environment where you're not relying on object references to your original data, as our functional approach creates new collection objects instead of mutating them.

Guide to lodash/fp: https://github.com/lodash/lodash/wiki/FP-Guide


Refactor Business Logic to Composable Functions with Lodash

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/file-forge

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

https://egghead.io/workshop/cursor

John Lindquist

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.

In this lesson, we learn how to reorganize file-based routes in TanStack Router to accommodate nested routing structures.

We explore how to create additional nested routes by converting files into directories, allowing for further nesting.

For example, to create a nested route like `/employees/$employeeId/edit`, we can create a **directory** (!) named `$employeeId` and place an `index.tsx` file inside it to define the route component for this nested route.

This approach provides flexibility in organizing files while maintaining the convention that file names and locations align with route paths. We also see how to adapt existing components and loaders to work with these reorganized routes, enabling features like edit forms or delete actions (or whatever) within nested routes.


Reorganize File-Based Routes in Tanstack Router

Reorganize file-based routes in TanStack Router for flexible nested routing.



In this lesson, we learn how to parametrize the loader for a nested route in TanStack Router.

We focus on a nested route like `/employees/$employeeId`, where the `$employeeId` is a **dynamic parameter**.

To fetch data for this route, we use a `loader` function that accesses the `$employeeId` parameter. This is achieved by using the params object available in the `loader` function, which contains the dynamic segments of the URL. We then use this parameter to call a function like `getEmployeeById`, which fetches specific employee details from an API.

We highlights that TanStack Router **automatically infers types** for these parameters, ensuring type safety for nested routes.

Parametrize Route Loader for Nested Route in Tanstack Router

Parametrize nested route loaders in TanStack Router using path parameters

Paul Frend

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