Learn Aphrodite, Php and Reactfire

PHP is a popular general-purpose scripting language that is especially suited to web development.

Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world.

Using "require" for classes isn't a modern practice for PHP development. A much better option is to use Composer's autoloader to automatically import classes into your app.

Replace PHP require statements with the Composer class autoloader

Once you start creating PHP classes, you'll want to organize them into groups of classes. In this lesson, you'll learn how to do just that with namespaces.

Defining & using PHP namespaces

Creating functions around your app is just fine, but when your app grows in complexity, you will want to create those functions as being part of PHP classes. This will help your app scale & expand with time.

Refactoring functions into PHP classes

Class properties and methods can have different visibility levels, which control how private or protected those properties are.

Understanding scope in PHP classes

Creating class properties is useless unless you can read back that property! There's an easy way to do this, with PHP's shorthand arrow syntax.

Retrieve public class properties with PHP’s arrow syntax

There's a new way to create class properties in PHP 8. In this lesson, you'll learn about "promoted properties", also referred to as "constructor property promotion".

Create promoted properties in PHP 8 classes

Attaching properties to a PHP class is important, so you can track specific values of data entities or functionality.

Create class properties in previous versions of PHP

It's very common to execute a piece of code when an object is created. In this lesson, you'll learn how to do just this with a PHP class constructor.

Run code on PHP class creation with a constructor

Creating a class in PHP is quite easy with the "new" keyword, and can represent a specific data entity or functionality.

Create & instantiate a class in PHP

Files can get hard to read & debug if they get too long. In this lesson, we'll learn how to include external files in your PHP scripts.

Including or requiring files in PHP

PHP "juggles" data types with something called "type coercion". Let's learn all about that!

Type coercion & strict data types in PHP

Types are important in any programming language. In PHP, you can define argument & return types to build more reliable applications.

Defining function return & argument types in PHP

You can't get data into a function without arguments. In this lesson, we'll learn how to pass variables as arguments to PHP functions.

Passing arguments to PHP functions

A PHP function can encapsulate a block of code, but it can also return values. In this lesson, we will learn how to return values created within a function.

Calling functions with return values in PHP

Scope in PHP is how variables are encapsulated in pieces of code. Let's learn about function & global scope in PHP.

Function & global scope in PHP

In this lesson, we will go over how to create a function with proper syntax in PHP with the function keyword.

Create a function in PHP

Sometimes you need to group together pieces of code. That's what functions are for! In this lesson, we'll learn all about them.

Purpose of a function in PHP

Sometimes you need to escape out of a PHP loop. You can easily do this by using either the continue or break keywords.

Continue or break out of a PHP loop

Different from a foreach loop, a for loop in PHP allows you to iterate through an array based on an index or another conditional value.

Display the contents of an array with a for loop

There are many functions available relating to PHP arrays, and one of them is count, to get the total number of elements within an array.

Get the count of a PHP array

Storing an array within an array is possible with PHP, and are called "multi-dimensional" arrays.

Create a nested or multi-dimensional array in PHP

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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