Learn Canvas, Ember and Reactfire

A <canvas> element provides scripts with a resolution-dependent bitmap canvas, which can be used for rendering graphs, game graphics, or other visual images on the fly.

canvas

The `canvas` element exposes a 2d context which allows you to draw lines using the `moveTo`, `lineTo`, and `stroke` api. You can combine this with Mouse events to make a simple drawing app.

Draw with Your Mouse on an HTML Canvas

A JavaScript framework for creating ambitious web applications.

ember

In this lesson we will create an `updateContent` method that sets the value of our `content` property to the current epoch timestamp. We need to call `toString()` on the `Date.now()` method in order to make TypeScript happy.

```
updateContent() {
  this.content = Date.now().toString()
}
```

We create a constructor that takes the options parameter, call `super(options)` and call into our `this.updateContent()` method.

```
constructor(options) {
  super(options)
  this.updateContent()
}
```

When we refresh our page we can see that the value of `content` is set to the epoch timestamp. 

To make the content really dynamic we use `setInterval()` to call into `updateContent` each second: `setInterval(() => this.updateContent(), 1000)`.

When we save our file we see that our content does not get updated anymore and when we check the console we see that we get an error telling us that: The 'content' property on the my-component component was changed after it had been rendered. The error also informs us that we should Use the @tracked decorator to mark this as a tracked property.

We add the @tracked decorator to our property, we import tracked from @glimmer/component and we refresh the page. We verify that `content` gets updated each second.

We change our `updateContent` to add our the timestamp to the array. When we use `this.items.push(this.content)` to achieve this we see that the list does not get updated. This is because the `@tracked` decorator only tracks the re-assignment of properties.

The way to get this working is by re-assigning our `items` array inside `updateContent`. We assign a new array, de-structure the current items array and add the new content.

```
this.items = [...this.items, this.content]
```



Use dynamic data in a GlimmerJS component

In this lesson we will add a variable called `content` to our component class, initialize it as a string and display the variable in our class using the double curly braces syntax, which is the handlebars expression for expressions, `{ {content }}`. 

After this we create a new class variable called `items`, initialize it as an array with strings. To display this list in our template we add a `<ul></ul>` and inside this unordered list we use the handlebars `each` helper to display the list items.

And inside our each statement we will print a `<li>` tag and render `{{ item}}` inside it.

```
<ul>
  {{#each items key="@index" as |item| }}
    <li>{{item}}</li> 
  {{/each}}
</ul>
```



Use template variables in a GlimmerJS component

In this lesson we will use the ember cli to generate a new component. Running `ember g glimmer-component my-component` generates the template and component class. We change the content of our component and add it to the main template in order to show it on the page.

Create a new GlimmerJS component

[GlimmerJS](https://glimmerjs.com/) is a UI component library built by the Ember.js team. It is fast and lightweight and uses a combination of TypeScript and Handlebars to build the components.   

In this lesson we will use the Ember CLI to initialize a new GlimmerJS project.

We will start by installing Ember CLI globally by running `npm install -g ember-cli/ember-cli`.

Next we will use the `ember` command to create our new project by running `ember new egghead-glimmer -b @glimmer/blueprint` . 

When our project is created we `cd` into the project directory and start the development server. We will view the page in the browser and change the base template.

Initialize a project using Glimmer.js

In this lesson, we cover several different ways to animate graphics drawn to Canvas.

Animating with Canvas

In this lesson, listening for and handling mouse and keyboard events in Canvas is covered.

Listening for Mouse and Keyboard Events with HTML Canvas

In this lesson, you'll learn how to read and write individual pixel color values from a canvas.

Canvas and Pixels

Transformations can change the default canvas coordinate system and create complex drawings with much less code.

Canvas Transformations

Learn some of the different ways you can affect the graphical content that you are drawing to a canvas. We'll cover global alpha, global composite operations and shadows.

Canvas Effects

In addition to vector drawing, you can draw bitmap images into a canvas, and also retrieve a bitmap based on the content in a canvas.

HTML Canvas and Images

With the HTML `canvas` you can easily draw text as well as graphics. Let's take a look at basic text manipulation on the canvas.

Drawing Text on Canvas

When drawing on the HTML5 canvas element, your fills aren't limited to flat colors. You can use gradients to get smoothly blended color fills on your drawn elements.

Gradient Fills on the HTML5 Canvas

You aren't limited to boring black lines when drawing on the HTML5 canvas element. Using styles, you can program interesting colorful images with JavaScript.

Drawing Styles on HTML5 Canvas

When drawing on your HTML canvas in JavaScript, you aren't restricted to straight lines. Using curves and arcs you are able to create smooth curved lines.

Drawing Paths - Curves and Arcs

Now that we have an HTML5 canvas to draw on, let's take a look at drawing simple lines and rectangles with JavaScript

Drawing Paths - Lines and Rectangles

The HTML Canvas element allows us to access powerful drawing APIs from JavaScript. In this lesson, the first in a series, we will learn about `<canvas>`, and get ready to start drawing.

Introduction to the HTML Canvas element

<p>Today Joel catches up with Iheanyi Ekechukwu. Iheanyi is a Product Engineer currently working at DigitalOcean. He previously worked at IBM on Watson. They also talk about Iheanyi's education, stack, and side projects</p><p>Iheanyi started out majoring in Computer Engineering, but switched to Computer Science after he figured out hardware just wasn't for him. He now lives and works in Brooklyn and spends most of his time coding (though he always brings his design skills to the table).</p><p>Iheanyi's design comes from a dual degree program at Notre Dame, the college where he graduated. He noticed a lot of subpar interfaces coming from pure programmers, and he was frustrated with that, so he took his school's opportunity to learn design and apply it to his work. Even if he isn't a designer, he uses his skills daily to communicate with designers and make whatever he works on that much better.</p><p>Iheanyi started using Ember back during college. He was frustrated by his school's class search interface, and he set out to improve it. Ember and Rails were like a match made in heaven for him, Ember having been authored by ex-Rails core team member, Yehuda Katz.</p><p>Joel discusses with Iheanyi what he's currently using in his work. They get into GO, and how Iheanyi has enjoyed working with a statically typed language when doing back-end work. Not having to worry about full test coverage when refactoring has been great!</p><p>Lastly, they talk about the various side projects Iheanyi has going. Such as Interface Lovers, a blog where top designers are interviewed and share their work music playlists. Also, Seeker, a job-board app that allows you to connect your strip account to it and have companies submit jobs.</p><hr /><h3>Transcript</h3><p><a href="https://egghead.simplecast.com/episodes/47aebd17/transcript">"Iheanyi Ekechukwu on education, programming, and managing side projects" Transcript</a></p><h3>Resources</h3><ul><li><a href="https://seeker.company">seeker</a></li><li><a href="https://interfacelovers.com/">Interface Lovers</a></li><li><a href="http://creative.black/">Creative Black</a></li><li><a href="https://www.ibmchefwatson.com/">Chef Watson</a></li></ul><h3>Iheanyi Ekechukwu</h3><ul><li><a href="https://iheanyi.com/">Website</a></li><li><a href="http://www.twoblacknerds.com/">Podcast</a></li><li><a href="https://twitter.com/kwuchu">Twitter</a></li><li><a href="https://github.com/iheanyi">Github</a></li><li><a href="https://egghead.io/instructors/iheanyi-ekechukwu">egghead.io</a></li></ul><h3>Joel Hooks:</h3><ul><li><a href="https://twitter.com/jhooks">Twitter</a></li><li><a href="https://joelhooks.com/">Website</a></li></ul>

Today Joel catches up with Iheanyi Ekechukwu. Iheanyi is a Product Engineer currently working at DigitalOcean. He previously worked at IBM on Watson. They also talk about Iheanyi's education, stack, and side projects

Iheanyi Ekechukwu on education, programming, and managing side projects

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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