Learn Ember, Lodash and Serverless

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

In this video you'll learn how to deploy a React app to AWS using AWS Amplify Hosting's drag and drop folder functionality. The app will also automatically be globally distributed on AWS's global CDN.

Deploy a React App to AWS in 24 seconds

Starting from a prebuilt frontend, Jason Lengstorf guides you through the creation of a "Corgi Up-boop App" that uses Netlify Functions and Hasura. You can view [the deployed final product on Netlify](https://egghead-serverless-functions.netlify.app/).

You'll set up the Netlify CLI, and configure a local development environment before writing and deploying functions with a few different use cases.

The first example function bypasses CORS restrictions to retrieve corgi data from an example third-party API.

Next, you'll configure environmental variables to securely communicate with the Unsplash API without exposing private keys to the client.

Serverless functions don't persist data by themselves, so we will use  Hasura to create a GraphQL API for tracking the number of "boops" each corgi receives.

This course is an excellent illustration of the speed of Serverless functions, and how fast you can implement functionality in your web applications.

An all-in-one workflow that combines global deployment, continuous integration, and automatic HTTPS.

netlify

Build a Corgi Up-boop Web App with Netlify Serverless Functions and Hasura

Environment variables set in a `.env` file can be imported using the Netlify CLI with the following command: `netlify env:import`.

This allows you to manage environment variables directly on Netlify, providing the benefit of security and flexibility of sharing environment variables with your team. 

From inside Netlify, you can access the environment variables in:

`Site settings` > `Build & deploy` > `Environment` > `Environment variables`

Import and Set Environment Variables from a .env file using Netlify CLI

CORS limits websites from communicating with other domains without the full consent of both sites. Consuming data from a 3rd Party REST API makes it difficult since we can't properly configure the appropriate CORS headers. To solve this, we'll set up a proxy server to request the data using a Netlify function that avoids CORS altogether.

The Third-Party API that we'll be working with will provide the following data: `id`, `name`, `favoriteSong` for each corgi. 

[http://no-cors-api.netlify.app/api/corgis](http://no-cors-api.netlify.app/api/corgis)

We will also use `node-fetch` a light-weight module that brings the `fetch` API to fetch the data into our application.

Circumvent CORS when Accessing a Third-Party API using Netlify Functions 

There are a few ways you can run Netlify serverless functions while you are developing them. 

You can: 

- Navigate to the directory under the `/.netlify/functions/` path
- Curl them using the full URL
- Use an app like Postman

Manually Trigger Netlify Serverless Functions Locally

Netlify makes deploying to production a breeze by configuring your Netlify CLI to your Netlify account and integrating with GitHub.

We'll log in to Netlify through the CLI and initialize a site by completing all the options that the CLI runs us through. Once this is done, Netlify will trigger new builds for your site every time you push to GitHub.

Deploy Serverless Functions to Production on Netlify using the Netlify CLI

Netlify makes developing serverless functions easy with the netlify-cli (`ntl` for short). You'll be able to build and test functions locally as well as publish your functions from the CLI. 

We will install netlify-cli globally and create a `netlify.toml` file that will configure where the CLI should look to run functions that we define, in our case `functions/`. When the application is served up, Netlify runs functions under `/.netlify/functions/`.

Set up a Local Development Environment for Serverless Functions Using Netlify

A serverless function is something that is going to allow us to do server-like things without having to manage servers, but an important distinction here is that serverless does not mean that there are **no** servers. 

The environment is actually running on top of an operating system and uses physical servers, but the responsibility for provisioning and managing the infrastructure belongs entirely to the service provider.

Some of the benefits of using serverless functions: 

- **Enhanced scalability.** No need for developers to implement code to scale. It scales automatically.
- **Reduced time to market.** Serverless is an optimized set of tools for building quickly.
- **No boiler code.** A serverless function is just a tiny piece of logic. Reducing the complexity of software.
- **More time for user experience.** Serverless functions allow teams to focus resources on the user experience and not on managing servers.
- **Security.** We can call third-party APIs securely without exposing important data on the client-side.

What Are Serverless Functions?

`_.omitBy` and its sibling `_.pickBy` are popular utilities which apply filter-like functionality to objects. Both of them accept a function to determine if a given entry in the object should be included in the output object. This lesson will demonstrate an approach to build both of these utilities from a single function using the combo of `Object.entries` and `Object.fromEntries`.

`Object.entries` converts an object into an array of key, value pairs. We couple that with the native Array filter method, to create a generate `filter()` method that works on objects. Once we've filtered our object we use `Object.fromEntries` to convert the array back into an object. This works great for both `omitBy` and `pickBy`.

**Note**

`Object.fromEntries` is part of the ES2019 spec and may need [a polyfill](https://github.com/ungap/from-entries) to work depending on your environment.

---

- [MDN Guide: Object.entries](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Object/entries)
- [MDN Guide: Object.fromEntries](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Object/fromEntries)



Build lodash.omitBy and lodash.pickBy with Object.fromEntries

`_.pick` and its sibling `_.omit` are popular utilities which apply filter-like functionality to objects. This lesson will demonstrate two different techniques for building the functionality found in [_.pick](https://lodash.com/docs/4.17.15#pick).

1. Using `for...of` to loop through the `keys` array
2. Using `for..in` to loop through the target object

This lesson will also cover how to emulate `_.pick`'s support for either an array or multiple arguments for the picked keys using [Rest params](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Functions/rest_parameters) and [Array.prototype.flat()](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/flat).


Build lodash.pick from Scratch

This lessons builds on [Build lodash.debounce from scratch](https://egghead.io/lessons/javascript-build-lodash-debounce-from-scratch) to add support for one of its more interesting options: `maxWait`. The `maxWait` option ensures that a debounced method is deferred no longer than the time specified. This is helpful if we need to respond to a long running interaction while it's still in progress. 

In this lesson we'll demonstrate how continuously typing into a debounced input field prevents us from logging any input at all. We'll add a `maxWait` option as a third parameter to our `debounce` function and set it up using an additional `setTimeout` call.

Essentially `maxWait` works by adding a second timer which will fire in the case that the normal debounce timer does not get called. We avoid unnecessarily triggering our debounced function by calling `clearTimeout` in both of our `setTimeout` functions to clear out whichever timer didn't just fire. We also need to set the `maxTimer` variable to `null` to tell our function it's safe to start a new maxWait timer each time either timer fires.


**Notes**

- `setTimeout` returns a timer ID not the actual timer object itself, which is why we have to `null` it out *after* we already call `clearTimeout`
- If you don't `null` out `maxTimer` in your original timer, the maxWait timer will stop triggering after the debounce timer is called the first time. I learned that one the hard way when I first recorded this video.


Extending debounce with a maxWait Option

This lesson will demonstrate how to replace common uses of [lodash.get](https://lodash.com/docs/4.17.15#get) with [optional chaining](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Optional_chaining) syntax.

`_.get` is by far my favorite lodash method. I've been on so many projects where the results of a network call was a large and often unreliable blob of data. After countless attempts to reign in errors caused by trying to access non-existent properties I've come to rely on _.get as a simple & safe way to retrieve data from those objects. However, as is always the case in web development, times are changing and the tools we need to safely access properties of complex objects are now being built into the platform. [Optional chaining syntax](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Optional_chaining) gives us the power of _.get without several of the tradeoffs. It doesn't require an extra library to use, it plays nicely with TypeScript and it can be used when calling methods as well as when accessing properties.

This lesson covers 4 common use cases of `lodash.get` and converts them to using optional chaining syntax.

1. Nested Object and Array Access
2. String and Variable Property Access
3. Calling a Method on an Object that may Not Exist
4. Providing a Default Value when a Property does Not Exist 

---

 ### Resources

- [Nullish coalescing operator](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Nullish_coalescing_operator)

Replace lodash.get with Optional Chaining Syntax

This lesson will demonstrate how to recreate a simplified version of the popular [lodash.debounce](https://lodash.com/docs/4.17.15#debounce) method from scratch. **I literally failed a job interview by messing up this question, so watch carefully!**

Debounce is an incredible tool most commonly used to prevent responding too quickly to user input that is in motion. For example, preventing excessive AJAX requests made while a user types into an autocomplete field or quickly responding to window resize events without bringing your browser to a halt.

Our approach is to build the simplest `debounce` method possible using a [setTimeout](https://developer.mozilla.org/en-US/docs/Web/API/WindowOrWorkerGlobalScope/setTimeout) which gets cleared each time the supplied function is called thereby delaying the execution of our function until input stops for a given amount of time.

The [Lodash implementation of debounce](https://lodash.com/docs/4.17.15#debounce) includes support for a number of options including `leading`, `maxWait`, and `trailing` which we have not covered in this video, but may in future lessons.

### Variations

Modern implementations of `setTimeout` include support for additional arguments which get passed into your function so an alternative to the approach used in this lesson would be `setTimeout(fn, wait, ...args)`

### ES5 Compatbility

If you are in an es5 environment skip the rest/spread syntax and use:

```
var args = Array.prototype.slice.call(arguments)
```

And later, in your setTimeout function:

```
fn.apply(null, args)
```

----

There are some really great resources out there to learn more about debounce, here are my two favorites:

- https://css-tricks.com/debouncing-throttling-explained-examples/
- http://benalman.com/projects/jquery-throttle-debounce-plugin/


Build lodash.debounce from Scratch

[Lodash](https://lodash.com/docs/) is a collection of utilities that have become a staple of modern web development. This collection will show you how to re-create its most common methods using simple JavaScript techniques available in any modern web browser. In addition to re-building tools like `_.merge`, `_.get` and `_.debounce` from scratch you will also learn how to replace them completely in some cases with modern syntax such as [optional chaining](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Optional_chaining), [nullish coalescing](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Nullish_coalescing_operator) and [Object.fromEntries](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Object/fromEntries).

This collection should interest the following groups:
- Those who want to better understand common JavaScript patterns
- Engineers who want to reduce their dependence on Lodash (for performance or other reasons)
- Candidates preparing for a technical job interview (where it is common to be asked how to implement some of these methods).

Recreating Popular JavaScript Utility Methods from Lodash

This lesson will demonstrate how to recreate a simplified version of the popular [lodash.merge](https://lodash.com/docs/4.17.15#merge) method from scratch.

First we'll create a test file with two objects that we want to merge together and demonstrate the output after running them through lodash.merge , then we'll go about creating our own version of the function.

Our initial version will support only one `source` object to merge in, and will support only the subset of JavaScript syntax supported by the JSON spec, including the following types:
- `boolean`, `number`, `string`
- object literal
- array

After getting basic types to merge we'll show off two methods for merging arrays, either using [concat](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/concat) or recursively going through the children and merging them.

Finally we'll build our own `isObject` function to check for object literals and recursively apply our `mergeJSON` function to any object literals in our `source` object.

And last we'll add support for multiple `source` objects to be merged in using a `for...of` loop.


**Note**

The `mergeJSON` method we create approximates the `lodash.merge` functionality but does not seek to replace it. It may be useful for some use cases, but primarily it's meant to be a tool for understanding how to merge objects with recursion.

Build lodash.merge from Scratch

This lesson will demonstrate how to recreate a simplified version of the popular [lodash.get](https://lodash.com/docs/4.17.15#get) method from scratch.

Our function takes 3 parameters `obj`, `string` and `defaultValue`.

The primary technique we discuss is splitting the `string` on any kind of separator we might see such as `[`, `]`, or `.` and then filtering out any empty strings we get in our array. Once we have that array of path parts, we can loop through each part with a `for..of` loop then keep reaching into object until we either a) run out of parts to try 🎉 or b) hit a dead end (`null` or `undefined`). The entire function is very simple to write, but can be incredibly powerful when you're safely trying to access large and complex blobs of data that might be coming from a database or server response. 

Build lodash.get from Scratch

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

A modern JavaScript utility library delivering modularity, performance & extras.

lodash

Imagine if looking at your business logic read as simple as a story from a children's book. That's exactly what we'll cover by learning how to compose small pieces of business logic and also set them up for further composition in the future.

We're showing a list of fictional tweets in a trivial React component created with create-react-app, however the example is not React specific and can be applied to any framework/environment.

Our example will refactor out business logic from a non-composable function to small composable pieces. To accomplish this we'll be using flow, filter and map from lodash/fp, which is included in the regular lodash npm package.

This approach works well in any environment where you're not relying on object references to your original data, as our functional approach creates new collection objects instead of mutating them.

Guide to lodash/fp: https://github.com/lodash/lodash/wiki/FP-Guide


Refactor Business Logic to Composable Functions with Lodash

[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

 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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