Learn Angularfire, Linux and Reactfire

firebase

There are 2 different ways to get a document's ID when using `@angular/fire`.

You can add the parameter `{ idField: 'id' }` to your collection when using the `collectionData()` observable.

You can also use the observable `collectionChanges()` and access the internal metadata of the document to manually get the ID and add it to the resulting object.

Code sample: https://github.com/javebratt/angularfire-idField

Access a Firebase Document's ID Even when It's Not Stored in the Document with angularfire

AngularFire is the officially supported AngularJS binding for Firebase. Firebase is a backend service that provides data storage, file storage, authentication, and static website hosting for your Angular app.

angularfire

You'll learn the 2 Observables that Firebase Authentication provides to subscribe to the authentication state of your application, `authState` and `user`.

You'll see how they both return the same `User` object from Firebase Authentication.

And you'll learn that the difference is that `authState` triggers when the user logs in, or logs out of the application, while `user` is called both of those times, and also is called when Firebase refreshes the user's authentication token.

As a personal guideline, if I need to do an operation when the user's token is refreshed (_for example, update a 3rd party service_) I'll use `user`, if not, I'll default to `authState`.

Choose Between authState or user to Subscribe to Firebase Authentication State Changes

Amazon Web Services (AWS) is a powerful expansive cloud infrastructure platform for building modern web applications.

AWS provides services for data, presentation, authentication, security, video encoding, and much more. Offering over 175 fully-featured services from data centers globally, AWS powers a large percentage of the internet.

Now that we are SSH'ed into our EC2 instance we can start modifying the server to actually run the application that we want to to run. Before we do that we want to switch users because you initially SSH you are the root user.

We will start by adding a `ghost-admin` user that we will give sudo permissions to. Once we are logged into that user we will update the server to ensure we are on the latest and greatest versions before installing additional software.


Add and Log In to an AWS EC2 Instance with a sudo User

Node.js is a platform built on Chrome's JavaScript runtime for easily building fast, scalable network applications. Node.js uses an event-driven, non-blocking I/O model that makes it lightweight and efficient, perfect for data-intensive real-time applications that run across distributed devices.

node

Ghost has two hard dependencies which need to be installed before it can run. Those are Nginx and Node.js.

We will start by installing Nginx and set Nginx up to control the traffic coming into the EC2 server. After this, we'll install NVM using the raw install script that they provide on GitHub. With NVM installed we can then install the latest version of Node.js


Install Nginx and Node.js on a AWS EC2 Server

To run Ghost, we first need to install the ghost cli. This can be done with npm now that Node.js is installed on the server.

After installing the CLI we will create a directory `/var/www/ghost` that the actual installation of Ghost will occur in. We'll update the permissions of this file to include the `ghost-admin` user that we are logged in as.


Install Ghost CLI and Create Directory

Vim is a highly configurable text editor built to make creating and changing any kind of text very efficient. It is included as "vi" with most UNIX systems and with Apple OS X.

Every programmer starting to work in a *nix environment eventually finds themselves in VIM, the default text editor. Often by mistake! Because VIM lacks the typical polished GUI of normal programs, it can be hard to discover the correct sequence of commands to even exit the program. Once you learn how to exit VIM, then the next task is to also learn how to make VIM do what you want -while- exiting!

In this lesson we cover 7 different ways of exiting VIM, whether you are in EX mode or Command (also known as Escape) mode, whether you are editing a single file or several at once.

Exit Vim

Installing the AWS CLI on your local workstation may be one of the biggest boosts to productivity you can make if you use AWS often. Almost all AWS features are available via the CLI allowing you to script, schedule and automate many AWS related tasks.
In this lesson, I’ll show you how to setup and verify the AWS CLI.

Installing the AWS CLI on OS X and Linux

AngularJS is an open-source JavaScript framework, maintained by Google, that assists with running single-page applications. Its goal is to augment web-based applications with model–view–controller (MVC) capability, in an effort to make both development and testing easier.

angularjs

Firebase provides a real-time syncing backend for your AngularJS applications in very few lines of code. In this lesson, you'll learn how to get started integrating it into your app.

Get Started with Firebase and AngularFire

 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

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.

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. 

What you can 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.

Inside of Cursor with their Composer AI agent, we can search for the files we want, tell the agent to download them, and inspect them for better understanding of 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.

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