Learn Elasticsearch, 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

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

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

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

In this lesson, you'll learn how to leverage the GitHub CLI to efficiently search through issues across multiple repositories and use that data with AI Studio for deeper insights.

The lesson covers:

- Using GitHub CLI to search for specific issues across repositories
- Filtering issues based on criteria like comment count
- Extracting issue content and comments
- Using the collected data as context in AI Studio
- Creating practical applications like FAQs from issue discussions

The example demonstrates creating a Framer Motion FAQ by searching GitHub for issues with multiple comments, but the technique can be applied to any framework or library.

This workflow helps developers:
- Research common problems and solutions
- Understand recurring issues in specific technologies
- Generate useful documentation from real-world problems
- Make better use of existing GitHub discussions

This approach combines the power of GitHub's vast knowledge base with AI tools to create valuable insights for development work.

Search 100s of Issues with GitHub CLI as Context for AI Studio

I often find myself wondering how to use certain APIs in certain situations, and I have a couple of keywords that I try and search up on Google. And what you can really do in this situation is turn to GitHub and see how other people have actually used this code on public projects. The GitHub CLI enables us to run searches like this, and we can take the results and work on them programmatically. And inside of Cursor with their Composer AI agent, we can search for the files we want, and we can tell the agent to download them and then work on them and inspect them and teach us how those things work together.

Search All of GitHub's Code and Pass the Results to AI Agents in Cursor

Implement virtual scrolling to handle large datasets in an HTML table. Start with a basic table that renders 10,000 rows of data. The browser can struggle with this amount of DOM nodes to render at one time, especially on low end devices. 

We implement tanstack react virtual to render only the necessary amount of rows the user is viewing. The implementation includes proper column sizing, accessibility considerations, and convenient scroll controls, resulting in a smooth and performant table experience regardless of the dataset size.

Create a row virtualized HTML table with Tanstack Virtual

Learn how to implement virtual scrolling in React tables using TanStack Table and Tanstack Virtual with the useWindowVirtualizer React hook.

The most difficult tasks will require the strongest AI models. And those models simply aren't available as AI agents inside of your tooling. So the best workflows are to drag and drop the files you need to be changed and analyzed inside of a reasoning model such as OpenAI's O1 Pro, then letting it create a giant plan for your dumber AI agents to work on. The key here is that instead of just copying and pasting the entire plan into Cursors Composer or your AI agent, it's to create a file inside of your project and ask your agent to tackle the plan one step at a time. This allows you ample opportunity between each step to test and verify the micro steps along the way. Otherwise, as I've experienced many times, a session can go way off track and you burn a lot of time and burn a lot of AI requests Cursors Composer and it starts churning through a plan Cursors Composer and making lots of unwanted changes. Cursors Composer and it's to guide an AI agent to hold its hand along the way, especially through the most complex tasks. Cursors Composer and the most complex tasks Cursors Composer and the most complex tasks require a step-by-step plan.

Planning AI Agent Sessions with Reasoning Models

AI agents will inevitably make mistakes—it's just a matter of when. A key tool in your toolbox, once they do make mistakes after working on a problem for a while, is to use **git diff** to capture all of the changes they've made. 

Once you have those changes in a text file, you have a portable document that you can test against other AI models. This allows you to move the problem into smarter reasoning models with smaller context windows, helping you isolate and focus on the issue at hand.

**git diff** has been invaluable when working with AI agents because they'll often make many good changes but also slip in one tiny error. Finding that one small mistake is **git diff**’s superpower.

Leverage Git Diff to Fix AI Agents' Mistakes

AI agents are notorious for getting tasks done, but also taking some shortcuts in TypeScript, and falling back to using things like Any, which you might not want in your codebase. This creates a scenario when it's done all of the work, you've tested and verified everything, but it's left you with many things untyped. So in this situation, when you run git commit, you can set up a commit hook to expose all of the linting, expose all of the TypeScript errors. And then you just let the AI agent run that commit hook and it can find all of the mistakes that it made. And go through And fix everything without you ever having to touch the code. This lesson covers a real world scenario of a commit hook running based on a refactor that was made when extracting some types. And it shows how running commit with an agent, if you allow the commit to give exhaustive information on what went wrong, the agent can continue on and make all the changes for you.

Commit Hooks are Critical with AI Agents in Cursor

If you ever want to see data represented in a certain way, such as a chart or a graph, to better understand what's going on in the world, you can use AI's to grab the most recent data and then convert it into any visual format you want. This is enabled in AI studio with Gemini through what's called grounding from web search, structured output formatting data, and code execution, which can run Python and generate charts. This lesson is going to walk you through the challenges of using grounding, especially when working with numbers which need to be accurate. How you can use image recognition and convert that to structured output. And then the challenges of using code execution. Especially when AI's are confident that the code ran even though the code failed. At the end, we still get to generating a chart through the online editor and then we take it also to set up in Cursor in a local environment where we can run all the way through YOLO mode and just click around some buttons and get our chart.

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Build High-Speed Monorepos with Nx and pnpm Workspaces

Introduction to Monorepos with NPM Workspaces

Scale React Development with Nx

Migrate a Monorepo from NPM Workspaces to Turborepo

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