Learn Clojure, Monorepo and Reactfire

In a monorepo, all of your apps and packages reside in a single repo. Structuring your code this way has many organizational benefits. You can more easily manage your dependencies, and you can maintain more consistency across your various apps. 

But, if you attempted to do this with just yarn or pnpm alone, you'll quickly start running into productivity issues. Your build times will be very long, and you'll have to make sure to run builds for any internal packages an app is depending on.

That's where Nx comes in.

Combining the speed of pnpm with the efficiency of Nx gives you an amazingly performant monorepo setup.

Nx builds a graph of your workspace and all of the connected dependencies. If there is a change anywhere, Nx will know about it and everything that is affected by it. 

Which means that Nx is able to use caching to prevent unnecessary builds when nothing has changed. Nx's caching features enable blazing fast <10ms build times 🔥

You get to have all the advantages of the monorepo with none of the performance issues! 

So start watching this course and learn how to achieve these speeds today. 

A Monorepo is where you have multiple projects inside a singular repository.

monorepo

Build High-Speed Monorepos with Nx and pnpm Workspaces

Introduction to Monorepos with NPM Workspaces

On the surface, starting a project sounds easy. First you make some directories, install some dependencies, then you write some code.

But there's a bit more to it than just those three steps.

The type of project you're working on impacts the decisions you make. It gets more complicated when you're working on multiple projects that are related. For example, maybe you have a customer-facing frontend app, an admin dashboard app, and a single backend used for both.

How does this change your directory structures? How should you share code between projects? What happens when you add a Design System into the mix?

It can get hard to keep things straight in your head.

Nx is a tool that aims to solve these problems for you, whether you're working solo on a small full-stack app or on a dev team of thousands.

It's not just an opinionated folder structure. When you create an app with Nx, it generates unit and e2e test setups as well as preparing bundling and code-splitting with Webpack, and stubbing out common features like routing.

One of the coolest features of Nx is its Dependency Graph. The graph can visually show you the relationship between the parts of your application, and is optimized to ensure that compiling, testing, and linting only happens in the affected areas of your project.

In this course, Juri Strumpflohner demonstrates these features and more through the iterative creation of a React + Storybook + Express app. Don't worry if this isn't your preferred stack! Nx supports the most popular frameworks & libraries on the web.

More importantly, the focus of this course is more on the workflow than the implementation.

Watch Juri's course, and see if the Nx tooling & workflow is right for you and your team!

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

Scale React Development with Nx

Clojure is a dynamic, general-purpose programming language, combining the approachability and interactive development of a scripting language with an efficient and robust infrastructure for multithreaded programming. Clojure is a compiled language, yet remains completely dynamic – every feature supported by Clojure is supported at runtime. Clojure provides easy access to the Java frameworks, with optional type hints and type inference, to ensure that calls to Java can avoid reflection.

clojure

Para iniciar con Clojure es necesario realizar algunas configuraciones e instalar algunas herramientas.
Lo primero es instalar [leiningen](https://leiningen.org) y configurar tu editor de código.
En este caso configuraremos VIM. Para esto instalaremos algunos ]plugins 

* [vim-fireplace](https://github.com/tpope/vim-fireplace)
* [vim-salve](https://github.com/tpope/vim-salve)
* [coc-conjure](https://github.com/jlesquembre/coc-conjure)

Configurar ambiente de desarrollo y VIM para Clojure

In this lesson I show what a Clojure keyword is and why we prefer them as keys in maps; and the three ways we can look up a value in a map.

Keywords and Map Lookup in Clojure and ClojureScript

We'll use Lumo, a cross-platform standalone ClojureScript compiler and Node.js environment, to create Hello as a command-line application. 

In this lesson, I’ll show you how to install Lumo through NPM, invoke a function in ClojureScript, and use the println function from the core library.

Compile and Run a ClojureScript Command-line App on Node.js with Lumo

Monorepos are an amazing way to collaborate on a multitude of projects with other team members. The ability to share code seamlessly between projects and have a single source of truth is paramount when working on projects with multiple moving wheels. 

One of the tradeoffs of a Monorepo is all of the management you have to do to make your projects run flawlessly and simultaneously. Each project has it’s own linting, building, testing, and deploying that it runs whenever code in that project changes.

This becomes a real problem when each of your projects lints, builds, tests, and deploys on every PR into your monorepo. You might be fine with a handful of projects but as your applications scale and you accrue more projects, you will need a way to scale. 

Turborepo is the tool that solves your monorepo’s needs.

Turborepo is a task runner for Monorepos. Turborepo caches the results of your CI tasks, schedules those tasks to run at maximum speed, and only runs tasks on the affected code in your PRs.  And because of remote caching, your coworkers, wherever they are, will have the most up-to-date code possible. 

One of the greatest things about it is that it's easy to use and can be incrementally adopted. Turborepo is a thin layer on top of your Monorepo. 

Turborepo doesn't get in the way as it is only a task runner. You don't have to modify your application code or anything to make it work. 

You’ll learn about

- How to set up caching for both local and remote
- CLI-commands and how they enhance your Monorepo
- Parallel execution of your tasks

So hop in and learn about Turborepo today!

Prerequisite: This course is a continuation of Introduction to [Monorepos with NPM Workspaces](https://egghead.io/courses/introduction-to-monorepos-with-npm-workspaces-c03f500b). To get the most out of this course, watch [Monorepos with NPM Workspaces](https://egghead.io/courses/introduction-to-monorepos-with-npm-workspaces-c03f500b) first. 

Migrate a Monorepo from NPM Workspaces to Turborepo

Tailwind is a utility-first CSS framework for rapidly building custom user interfaces.

tailwind

Architect an Extensible Digital Garden with Next.js, Tailwind, and Nx

In this lesson, you'll learn how to enhance the accuracy of components generated by Cursor Composer by aligning your project's package.json with Cursor's rules. We'll guide you through extracting the list of installed packages from your package.json and incorporating them into your .cursorrules file, along with brief descriptions of their functionalities. By providing Cursor with this contextual information, you'll enable it to generate components that are more precise and functional on the first attempt. The lesson includes demonstrations of component generation both with and without these enhancements, highlighting the improvements in accuracy and efficiency.

Boost Cursor Composer Accuracy: Integrating package.json with Cursor Rules

Speed up your bug-fixing process dramatically with Cursors Composer. Take a warning from your logs, search for relevant terms, then seamlessly import both the warning and search results into Composer. Add documentation and apply fixes to all search results in one go. This streamlined workflow resolves multiple issues rapidly, saving valuable development time.

Batch Fix Every File in Search Results with Cursor's Composer

 We map over the initial set of results then use `Promise.all` to efficiently fetch the details of each pokemon rendered in the table. This allows us to add more columns to our pokemon table.

Fetch and display row details with Promise.all in Tanstack Table

map over the initial set of results then use `Promise.all` to efficiently fetch the details of each pokemon rendered in the tanstack table

Create a custom hook to fetch remote data by passing a promise to the React 19 `use` hook. Then render this data in Tanstack Table by memoizing the results and passing them to `useReactTable`.

Render remote data in a Tanstack Table with React 19's use hook and a promise

Create a custom hook using React 19’s use to fetch data with a promise. Render it in a Tanstack Table by memoizing and using useReactTable.

You know when you're deep in the code trenches and need someone to challenge your assumptions? That's exactly what this is about.

Instead of waiting for someone to poke holes in your master plan, you can paste that strategy straight into AI Studio and let it pressure test your ideas. We're talking about using this tool to validate everything from your feature proposals to your ambitious refactoring strategies.

The real magic happens when you hit that microphone button. Now you're in a real-time verbal sparring match. It's like rubber duck debugging but the duck actually pushes back and makes you defend every architectural decision you've made.

Here's the workflow:
1. Drop your master plan into the system instructions
2. Tell the AI to challenge every assumption
3. Hit that mic and defend your choices like you mean it
4. Watch your plan either emerge stronger or get rightfully dismantled

This approach is perfect when you're:
- Stuck in your own head with a complex problem
- Need to validate if your approach holds water
- Want to catch edge cases before they catch you
- Looking for opportunities you might have missed because you're too close to the code

It's like having a senior dev on call 24/7, ready to challenge your ideas and push you to think deeper about your solutions.

Now go forth and get your ideas challenged. Your projects will thank you for it.


An Uncomfortable and Challenging Conversation with AI Studio

In this lesson, you'll learn how to effectively brainstorm your project ideas before working with AI agents. We'll cover:

1. The importance of having a clear plan before starting work with AI agents
2. Using traditional brainstorming techniques to gather your thoughts
3. Leveraging video recording to capture your brainstorming session
4. How to review and analyze websites for inspiration
5. Documenting likes, dislikes, and potential features for your project
6. Using AI tools (like AI Studio) to organize and process your recorded brainstorming session

The lesson emphasizes a modern approach to brainstorming that combines traditional techniques with video recording, making it easier to later process your ideas through AI tools. This method helps ensure that no ideas are lost and that your thoughts can be effectively organized by AI for future reference.

How to Brainstorm Your Project Features to Prepare for AI Agents

fast-check allows us to create composite generators that suit our testing needs. We'll generate two numbers to create a point simulating an X and Y and assert that the distance should always be greater than or equal to 0.

Generate composite objects with fast-check fc.record

Just using fast-check by itself can make you tests verbose and tough to read. Lucky for us there are many integrations with fast-check such as the `@fast-check/jest` package that will help remove some boilerplate from our test.

Tidy up your test with @fast-check/jest

Many people share .cursorrules files, but trying to craft your own from scratch is a huge pain. Instead, this lesson shows you how to grab ALL community-created .cursorrules files to build a custom rule set for your projects. We'll use Gitingest and Google AI Studio to gather these examples and generate an awesome starting point customized to your specific project.

Generate Custom .cursorrules for Your Project Based on Community Examples

The GitHub CLI allows you to search issues and filter on different criteria like amount of comments. This allows you to search for real-world situations and pains/solutions people have using different technology. 

With the GitHub CLI in hand we'll drop 100s of issues as context in Googles AI Studio and have Gemini determine the 10 most frequently asked questions. This will give us a sense of how people use a library and where people get hung up.