Learn Elixir, Reactfire and Yarn

Elixir is a dynamic, functional language designed for building scalable and maintainable applications.

Elixir leverages the Erlang VM, known for running low-latency, distributed and fault-tolerant systems, while also being successfully used in web development and the embedded software domain.

elixir

Some common uses for pattern matching in Elixir include checking conditions and passing parameters into functions. It's useful for pulling out only specific variables from collections of data.

Additional resources:
- [Elixir docs on pattern matching](https://elixir-lang.org/getting-started/pattern-matching.html)

Pattern match in conditions and function parameters in Elixir

Elixir uses `=` as an operator, but rather than assigning values to variables with it, it *matches* values on the right hand side of the `=` with the left side. This can be used in simple values, more complex data structures, and powerful condition checking.

Put into simpler terms, pattern matching is Elixir's way of assigning variables.

Additional resources:
- [Elixir docs on pattern matching](https://elixir-lang.org/getting-started/pattern-matching.html)

Pattern match values in Elixir

These videos include the introductory steps to writing your first Elixir function and beginning to understand some key concepts in Elixir that make it unique. 

Some of those concepts include:
- the Mix build tool
- project structure
- collections of functions written in modules
- function *arity*
- pattern matching and common uses for it
- the pipe operator

Getting Started with Elixir

The syntax to pass arguments into functions is akin to many other languages. Elixir functions are uniquely recognized when running based off of the number of arguments passed in (a term called *arity*). This means you can define functions with the same name, so long as a different number of arguments is passed in.

Arity is a key concept to be aware of when reading documentation and writing your own code.

Additional resources:
- [Information about arity](https://elixir-lang.org/getting-started/modules-and-functions.html#function-capturing)
- [Default arguments in functions](https://elixir-lang.org/getting-started/modules-and-functions.html#default-arguments)

Pass arguments into Elixir functions and understand arity

Elixir code is organized into groups of functions called modules. You can create your own modules and built on top of predefined modules that are a part of Elixir by default.

Additional resources: 
- [Getting started with Modules and function from Elixir docs](https://elixir-lang.org/getting-started/modules-and-functions.html)

Write a New Function Inside of an Elixir Module

Bootstrap a new Elixir project using Mix (a build tool included when you install Elixir), with its built in project generation.

Additional resources:
- [Official introduction to Mix](https://elixir-lang.org/getting-started/mix-otp/introduction-to-mix.html#our-first-project)

Generate a new Elixir project using Mix

Fast, reliable, and secure dependency management.

yarn

Yarn 2 is workspace aware. When you need to upgrade a package in one workspace, yarn will ask you if you want to resolve the version of the package to a version you use in a different package.

In this lesson, we go over `yarn up -i` to upgrade a package with an old version.

Use yarn up to Update Dependencies In A Yarn Workspace

`yarn dlx` can be used to run packages without installing them. This is useful for packages like `create-react-app` and `gatsby` that scaffold out apps for you, so you don't have to keep updating your global versions.



Use Yarn dlx to Execute Packages without Installing Them

This lesson goes over how to install yarn 2 through npm.

To install yarn 2 globally, we use `npm install -g yarn@berry`. 

To install yarn 2 to a specific project, we use `yarn policies set-version berry`. This creates a `.yarnrc` file and a `.yarn/` directory. `.yarnrc` that sets the path to our yarn instance in the created `.yarn/` directory that holds our new yarn version.

Install Yarn 2

Yarn PnP is a new feature set on **by default** with yarn 2. PnP gets rid of your projects `node_modules` in favor of a `.pnp.js` file.

This `.pnp.js` file maps all of the packages installed in your project to where Yarn has placed them on your disk. This enables faster, more reliable installs because you don't have to write to disk as often.

WTF is Yarn Plug'n'Play (PnP)?

Yarn 2 (Berry) has finally [released a stable version](https://dev.to/arcanis/introducing-yarn-2-4eh1)! 

The yarn team has been working on this release for over 2 years. 

This collection goes over: 

- What yarn pnp is and how it tries to solve the `node_modules` problem
- How to install yarn globally and locally inside of a single project
- How to use `yarn dlx`
- How to use `yarn up` to manage yarn workspace dependencies



Yarn 2 AKA Berry

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

The fastest way to create a robust, cross-platform compatible Node.js CLI (optionally typed with TypeScript) is by running `npx oclif single mycli`. Here we explain what this means and how and why to integrate this with Yarn Workspaces for an ideal developer experience.

Create a Single-Command Node.js CLI with Oclif, TypeScript and Yarn Workspaces

The pipe operator is an easy to use and powerful feature of Elixir. It takes the output from the expression on the left, and automatically passes it as the first argument to the function on the right. This is especially useful when chaining together multiple function calls or data transformations. Using the pipe operator results in code that's easier to read, write and refactor.

Chain Function Calls with the Pipe Operator in Elixir

React Native lets you build mobile apps using only JavaScript. It uses the same design as React, letting you compose a rich mobile UI from declarative components.

react-native

In this lesson we'll use yarn and yarn workspaces to create a monorepo that includes a web creat-react-app, a react-native app, and shared code. We'll use `react-app-rewired` and `react-app-rewire-babel-loader` to rewire the create-react-app configuration to allow babel to process the shared code from workspace. We'll also use `crna-make-symlinks-for-yarn-workspaces` and `metro-bundler-config-yarn-workspaces` to configure the react-native app to be able to find the shared code in the workspaces.

Use Yarn Workspaces to Share Code with CRA and create-react-native-app in a Monorepo

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

In this lesson we’ll bootstrap and React application using Yarn and Create React App through the `yarn create` command.

Bootstrap a React Application with Yarn & Create React App

Yarn is a new JavaScript package manager that aims to be speedy, deterministic, and secure. See how easy it is to drop yarn in where you were using npm before, and get faster, more reliable installs.

Yarn: A JavaScript Package Manager

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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