Javascript

Introduction to Testing in Node.js with Mocha and Chai

Published Apr 21, 2021

Updated May 30, 2023

5 min read

This article was written over 18 months ago and may contain information that is out of date. Some content may be relevant but please refer to the relevant official documentation or available resources for the latest information.

When writing software, it can be tedious to test features manually and ensure your entire program continues to work as it evolves over time. We can use automated testing to invoke our program automatically, and ensure it works as expected.

The Purpose of Testing

Simply put, writing good tests that verify your program works as expected will help you catch regressions, and improve the reliability of your software. Good tests will ensure the program meets the requirements the program needs to meet, and that it will continue to do so as the program evolves over time. Software testing is also integral to some software development processes like TDD and BDD, though these are outside the scope of this article.

The Node.js ecosystem has a large variety of testing frameworks to choose from, and many serve many different purposes. We'll be focusing on using Mocha and Chai to demonstrate the basics of testing in the context of backend development.

What are Mocha and Chai?

Mocha is a JavaScript testing framework that runs on Node.js, and in the browser. It is one of the most popular test frameworks in use in the Node.js community, and is very easy to learn and understand.

Chai is an assertion library that helps extend test frameworks like Mocha to make writing certain kinds of tests easier. Most test frameworks like Mocha also come with their own assertion functions as well, but we've opted to show off Chai since it makes it easier to write more complex tests.

Installing Mocha and Friends

Mocha and Chai can be installed into your project using the following:

npm install mocha chai --save-dev

Also, note that all following examples are available on GitHub for your viewing pleasure. I won't be showing all code being tested in this article as it would be a lot. You can then define a scripts section in your package.json file to run your tests so you can run your tests with npm test.

{
  "name": "mocha-basic",
  "version": "1.0.0",
  "description": "",
  "main": "index.js",
  "scripts": {
    "test": "mocha"
  },
  "author": "",
  "license": "ISC",
  "devDependencies": {
    "mocha": "^8.3.2"
  }
}

The Basics

We will start by showing off one of the most basic tests that you can make with Mocha, and bear with me as I'll show off some more complex tests later. This is a basic unit test that calls a function that returns the number 42, and tests that the result comes back as expected. The following is defined in a file called index.js.

exports.meaningOfLife = () => {
  return 42;
};

And the test for this function can be written as follows in a file located at test/index.test.js (note the naming convention).

const assert = require("assert");

const { meaningOfLife } = require("../index");

describe("Index", function () {
  describe("#meaningOfLife", function () {
    it("should return 42", function () {
      const result = meaningOfLife();
      assert.strictEqual(result, 42);
    });
  });
});

Tests can be run by invoking npm test in your project root. I've opted to keep all tests in a directory called test under the project root, and have test files for each corresponding source file with an added test.js suffix at the end of the filename. There are a few layers to this test, so let's go through each one.

Tests are defined by calling global Mocha functions that are defined when the mocha command is invoked. describe is used for organizing your tests. The way that I'm using it above is one level for what I'm testing in general, this being the index file, the meaningOfLife function, and then all tests that need to invoke that function. In this case there's only one test since this is a very simple function.

Tests are written inside of callbacks passed into the it function. One thing to note is how the test reads out like plain English. This is meant to make the purpose of the test to be more clear to the reader. In this example, we simply import and call the meaningOfLife function and check that it equals 42. We also use the assert call from Mocha to do this comparison, as it will raise an exception if the two expressions don't match.

Let's test an API

To demonstrate more practical tests, I've opted to create a basic API that returns data, and tests that the data has the expected format. The API is a simple express application that returns an array of movies as JSON by calling GET /movies against it.

Testing an API isn't as simple as calling a function, and checking a response, so we will use chai and supertest to help us. The tests are as follows:

const chai = require("chai");
const request = require("supertest");

const expect = chai.expect;
const app = require("../../app");

describe("Movies", function () {
  describe("GET /movies", function () {
    it("should return 200 OK with several movies", async function () {
      const response = await request(app)
        .get("/movies")
        .expect(200)
        .expect("Content-Type", /json/);

      const movies = response.body;
      expect(movies).to.be.an("array");
      expect(movies).length.to.be.greaterThan(0);
    });

    it("should have valid movies", async function () {
      const response = await request(app)
        .get("/movies")
        .expect(200)
        .expect("Content-Type", /json/);

      const movies = response.body;
      expect(movies).to.be.an("array");

      movies.forEach(movie => {
        expect(movie.name).to.be.a("string");
        expect(movie.year).to.be.a("number");
        expect(movie.rating).to.be.a("number");
        expect(movie.description).to.be.a("string");
        expect(movie.director).to.be.a("string");
        expect(movie.genres).to.be.an("array");
      });
    });
  });
});

Chai, as mentioned before, is an assertion library, and we use it here primarily for type checking the data coming back from the API. We use SuperTest to make API calls. We pass our app object to SuperTest, and it handles making sure the API is listening, and gives us access to some useful HTTP request builders that include assertion functions as well.

We import the expect function from Chai, and use it instead of the built-in Mocha ones. The tests read out like plain English, and are very easy to understand. Chai actually has several interfaces that allow you to write tests such as "should" and "assert". These are detailed on their website.

Hooks

Imagine that the API we're testing has a persistent database of some kind. The data stored in these database will naturally persist between tests, and this can be bad if we want tests to be reproducible. To solve this problem, we can use hooks. Hooks allow code to be run before and after test blocks.

In this case we want to use them to initialize and destroy the database the API will be using for each test to ensure the state is consistent every time a test is run, and will always have the same result.

Here is an example of the beforeEach and afterEach hooks in action.

const chai = require("chai");
const request = require("supertest");

const expect = chai.expect;
const app = require("../../app");
const db = require("../../db");

describe("Movies", function () {
  // Create the schema before each test in the describe block.
  beforeEach(async function () {
    const database = await db.database;
    await db.initDB(database);
  });

  // Drop the schema before each test in the describe block.
  afterEach(async function () {
    const database = await db.database;
    await db.resetDB(database);
  });

  describe("GET /movies", function () {
    it("should return 200 OK with several movies", async function () {
      ...
    });

    it("should have valid movies", async function () {
      ...
    });
  });

  describe("GET /movies/:id", function () {
    it("should query an individual movie", async function () {
      ...
    });
  });

  describe("POST /movies", function () {
    it("should create a new movie", async function () {
      ...
    });
  });

  describe("PATCH /movies/:id", function () {
    it("should update an existing movie", async function () {
      ...
    });
  });

  describe("DELETE /movies/:id", function () {
    it("should delete an existing movie", async function () {
      ...
    });
  });
});

I've removed the contents of the actual tests since they're a bit long, but they're in the GitHub repository if you're curious. The details of the db and app modules are outside the context of this article.

The main takeaway here is the code inside of the beforeEach hook will tell Mocha to create the database before each test, and the code in the afterEach hook will tell Mocha to destroy the database after each test. I should also mention it and describe are also hooks as well!

Mocha Hooks:

it: Defines a single test.
describe: Defines a block of tests.
before: Run only once before the first test in the describe block.
after: Run only once after the last test in the describe block.
beforeEach: Runs before every test in the describe block.
afterEach: Runs after every test in the describe block.

Conclusion

Effective use of testing libraries in Node.js like Mocha and Chai can help make it easy to write tests and make your application more reliable. The example projects used throughout this article can be found on my GitHub. I have only scratched the surface here when it comes to testing. There are also frontend testing frameworks such as Selenium and Cypress. We've actually written about Cypress on our blog as well, and you can find that here if you're curious.

This Dot is a consultancy dedicated to guiding companies through their modernization and digital transformation journeys. Specializing in replatforming, modernizing, and launching new initiatives, we stand out by taking true ownership of your engineering projects.

We love helping teams with projects that have missed their deadlines or helping keep your strategic digital initiatives on course. Check out our case studies and our clients that trust us with their engineering.

About the author(s)

Jamie Kuppens
I’m a software engineer with an interest in web development and some more esoteric things like emulator development.
@Reshurum @Reshurum

Exploring Angular Forms: A New Alternative with Signals

Exploring Angular Forms: A New Alternative with Signals In the world of Angular, forms are essential for user interaction, whether you're crafting a simple login page or a more complex user profile interface. Angular traditionally offers two primary approaches: template-driven forms and reactive forms. In my previous series on Angular Reactive Forms, I explored how to harness reactive forms' power to manage complex logic, create dynamic forms, and build custom form controls. A new tool for managing reactivity - signals - has been introduced in version 16 of Angular and has been the focus of Angular maintainers ever since, becoming stable with version 17. Signals allow you to handle state changes declaratively, offering an exciting alternative that combines the simplicity of template-driven forms with the robust reactivity of reactive forms. This article will examine how signals can add reactivity to both simple and complex forms in Angular. Recap: Angular Forms Approaches Before diving into the topic of enhancing template-driven forms with signals, let’s quickly recap Angular's traditional forms approaches: 1. Template-Driven Forms: Defined directly in the HTML template using directives like ngModel, these forms are easy to set up and are ideal for simple forms. However, they may not provide the fine-grained control required for more complex scenarios. Here's a minimal example of a template-driven form: ` ` 2. Reactive Forms: Managed programmatically in the component class using Angular's FormGroup, FormControl, and FormArray classes; reactive forms offer granular control over form state and validation. This approach is well-suited for complex forms, as my previous articles on Angular Reactive Forms discussed. And here's a minimal example of a reactive form: ` ` Introducing Signals as a New Way to Handle Form Reactivity With the release of Angular 16, signals have emerged as a new way to manage reactivity. Signals provide a declarative approach to state management, making your code more predictable and easier to understand. When applied to forms, signals can enhance the simplicity of template-driven forms while offering the reactivity and control typically associated with reactive forms. Let’s explore how signals can be used in both simple and complex form scenarios. Example 1: A Simple Template-Driven Form with Signals Consider a basic login form. Typically, this would be implemented using template-driven forms like this: ` ` This approach works well for simple forms, but by introducing signals, we can keep the simplicity while adding reactive capabilities: ` ` In this example, the form fields are defined as signals, allowing for reactive updates whenever the form state changes. The formValue signal provides a computed value that reflects the current state of the form. This approach offers a more declarative way to manage form state and reactivity, combining the simplicity of template-driven forms with the power of signals. You may be tempted to define the form directly as an object inside a signal. While such an approach may seem more concise, typing into the individual fields does not dispatch reactivity updates, which is usually a deal breaker. Here’s an example StackBlitz with a component suffering from such an issue: Therefore, if you'd like to react to changes in the form fields, it's better to define each field as a separate signal. By defining each form field as a separate signal, you ensure that changes to individual fields trigger reactivity updates correctly. Example 2: A Complex Form with Signals You may see little benefit in using signals for simple forms like the login form above, but they truly shine when handling more complex forms. Let's explore a more intricate scenario - a user profile form that includes fields like firstName, lastName, email, phoneNumbers, and address. The phoneNumbers field is dynamic, allowing users to add or remove phone numbers as needed. Here's how this form might be defined using signals: ` > Notice that the phoneNumbers field is defined as a signal of an array of signals. This structure allows us to track changes to individual phone numbers and update the form state reactively. The addPhoneNumber and removePhoneNumber methods update the phoneNumbers signal array, triggering reactivity updates in the form. ` > In the template, we use the phoneNumbers signal array to dynamically render the phone number input fields. The addPhoneNumber and removePhoneNumber methods allow users to reactively add or remove phone numbers, updating the form state. Notice the usage of the track function, which is necessary to ensure that the ngFor directive tracks changes to the phoneNumbers array correctly. Here's a StackBlitz demo of the complex form example for you to play around with: Validating Forms with Signals Validation is critical to any form, ensuring that user input meets the required criteria before submission. With signals, validation can be handled in a reactive and declarative manner. In the complex form example above, we've implemented a computed signal called formValid, which checks whether all fields meet specific validation criteria. The validation logic can easily be customized to accommodate different rules, such as checking for valid email formats or ensuring that all required fields are filled out. Using signals for validation allows you to create more maintainable and testable code, as the validation rules are clearly defined and react automatically to changes in form fields. It can even be abstracted into a separate utility to make it reusable across different forms. In the complex form example, the formValid signal ensures that all required fields are filled and validates the email and phone numbers format. This approach to validation is a bit simple and needs to be better connected to the actual form fields. While it will work for many use cases, in some cases, you might want to wait until explicit "signal forms" support is added to Angular. Tim Deschryver started implementing some abstractions around signal forms, including validation and wrote an article about it. Let's see if something like this will be added to Angular in the future. Why Use Signals in Angular Forms? The adoption of signals in Angular provides a powerful new way to manage form state and reactivity. Signals offer a flexible, declarative approach that can simplify complex form handling by combining the strengths of template-driven forms and reactive forms. Here are some key benefits of using signals in Angular forms: 1. Declarative State Management: Signals allow you to define form fields and computed values declaratively, making your code more predictable and easier to understand. 2. Reactivity: Signals provide reactive updates to form fields, ensuring that changes to the form state trigger reactivity updates automatically. 3. Granular Control: Signals allow you to define form fields at a granular level, enabling fine-grained control over form state and validation. 4. Dynamic Forms: Signals can be used to create dynamic forms with fields that can be added or removed dynamically, providing a flexible way to handle complex form scenarios. 5. Simplicity: Signals can offer a simpler, more concise way to manage form states than traditional reactive forms, making building and maintaining complex forms easier. Conclusion In my previous articles, we explored the powerful features of Angular reactive forms, from dynamic form construction to custom form controls. With the introduction of signals, Angular developers have a new tool that merges the simplicity of template-driven forms with the reactivity of reactive forms. While many use cases warrant Reactive Forms, signals provide a fresh, powerful alternative for managing form state in Angular applications requiring a more straightforward, declarative approach. As Angular continues to evolve, experimenting with these new features will help you build more maintainable, performant applications. Happy coding!...

Oct 4, 2024

5 mins

AngularJavaScript

Introduction to Zod for Data Validation

As web developers, we're often working with data from external sources like APIs we don't control or user inputs submitted to our backends. We can't always rely on this data to take the form we expect, and we can encounter unexpected errors when it deviates from expectations. But with the Zod library, we can define what our data ought to look like and parse the incoming data against those defined schemas. This lets us work with that data confidently, or to quickly throw an error when it isn't correct. Why use Zod? TypeScript is great for letting us define the shape of our data in our code. It helps us write more correct code the first time around by warning us if we are doing something we shouldn't. But TypeScript can't do everything for us. For example, we can define a variable as a string or a number, but we can't say "a string that starts with user_id_ and is 20 characters long" or "an integer between 1 and 5". There are limits to how much TypeScript can narrow down our data for us. Also, TypeScript is a tool for us developers. When we compile our code, our types are not available to the vanilla JavaScript. JavaScript can't validate that the data we actually use in our code matches what we thought we'd get when we wrote our TypeScript types unless you're willing to manually write code to perform those checks. This is where we can reach for a tool like Zod. With Zod, we can write data schemas. These schemas, in the simplest scenarios, look very much like TypeScript types. But we can do more with Zod than we can with TypeScript alone. Zod schemas let us create additional rules for data parsing and validation. A 20-character string that starts with user_id_? It's z.string().startsWith('user_id_').length(20). An integer between 1 and 5 inclusive? It's z.number().int().gte(1).lte(5). Zod's primitives give us many extra functions to be more specific about *exactly* what data we expect. Unlike TypeScript, Zod schemas aren't removed on compilation to JavaScript—we still have access to them! If our app receives some data, we can verify that it matches the expected shape by passing it to your Zod schema's parse function. You'll either get back your data in exactly the shape you defined in your schema, or Zod will give you an error to tell you what was wrong. Zod schemas aren't a replacement for TypeScript; rather, they are an excellent complement. Once we've defined our Zod schema, it's simple to derive a TypeScript type from it and to use that type as we normally would. But when we really need to be sure our data conforms to the schema, we can always parse the data with our schema for that extra confidence. Defining Data Schemas Zod schemas are the variables that define our expectations for the shape of our data, validate those expectations, and transform the data if necessary to match our desired shape. It's easy to start with simple schemas, and to add complexity as required. Zod provides different functions that represent data structures and related validation options, which can be combined to create larger schemas. In many cases, you'll probably be building a schema for a data object with properties of some primitive type. For example, here's a schema that would validate a JavaScript object representing an order for a pizza: ` Zod provides a number of primitives for defining schemas that line up with JavaScript primitives: string, number, bigint, boolean, date, symbol, undefined, and null. It also includes primitives void, any, unknown, and never for additional typing information. In addition to basic primitives, Zod can define object, array, and other native data structure schemas, as well as schemas for data structures not natively part of JavaScript like tuple and enum. The documentation contains considerable detail on the available data structures and how to use them. Parsing and Validating Data with Schemas With Zod schemas, you're not only telling your program what data should look like; you're also creating the tools to easily verify that the incoming data matches the schema definitions. This is where Zod really shines, as it greatly simplifies the process of validating data like user inputs or third party API responses. Let's say you're writing a website form to register new users. At a minimum, you'll need to make sure the new user's email address is a valid email address. For a password, we'll ask for something at least 8 characters long and including one letter, one number, and one special character. (Yes, this is not really the best way to write strong passwords; but for the sake of showing off how Zod works, we're going with it.) We'll also ask the user to confirm their password by typing it twice. First, let's create a Zod schema to model these inputs: ` So far, this schema is pretty basic. It's only making sure that whatever the user types as an email is an email, and it's checking that the password is at least 8 characters long. But it is *not* checking if password and confirmPassword match, nor checking for the complexity requirements. Let's enhance our schema to fix that! ` By adding refine with a custom validation function, we have been able to verify that the passwords match. If they don't, parsing will give us an error to let us know that the data was invalid. We can also chain refine functions to add checks for our password complexity rules: ` Here we've chained multiple refine functions. You could alternatively use superRefine, which gives you even more fine grained control. Now that we've built out our schema and added refinements for extra validation, we can parse some user inputs. Let's see two test cases: one that's bound to fail, and one that will succeed. ` There are two main ways we can use our schema to validate our data: parse and safeParse. The main difference is that parse will throw an error if validation fails, while safeParse will return an object with a success property of either true or false, and either a data property with your parsed data or an error property with the details of a ZodError explaining why the parsing failed. In the case of our example data, userInput2 will parse just fine and return the data for you to use. But userInput1 will create a ZodError listing all of the ways it has failed validation. ` ` We can use these error messages to communicate to the user how they need to fix their form inputs if validation fails. Each error in the list describes the validation failure and gives us a human readable message to go with it. You'll notice that the validation errors for checking for a valid email and for checking password length have a lot of details, but we've got three items at the end of the error list that don't really tell us anything useful: just a custom error of Invalid input. The first is from our refine checking if the passwords match, and the second two are from our refine functions checking for password complexity (numbers and special characters). Let's modify our refine functions so that these errors are useful! We'll add our own error parameters to customize the message we get back and the path to the data that failed validation. ` Now, our error messages from failures in refine are informative! You can figure out which form fields aren't validating from the path, and then display the messages next to form fields to let the user know how to remedy the error. ` By giving our refine checks a custom path and message, we can make better use of the returned errors. In this case, we can highlight specific problem form fields for the user and give them the message about what is wrong. Integrating with TypeScript Integrating Zod with TypeScript is very easy. Using z.infer<typeof YourSchema> will allow you to avoid writing extra TypeScript types that merely reflect the intent of your Zod schemas. You can create a type from any Zod schema like so: ` Using a TypeScript type derived from a Zod schema does *not* give you any extra level of data validation at the type level beyond what TypeScript is capable of. If you create a type from z.string.min(3).max(20), the TypeScript type will still just be string. And when compiled to JavaScript, even that will be gone! That's why you still need to use parse/safeParse on incoming data to validate it before proceeding as if it really does match your requirements. A common pattern with inferring types from Zod schemas is to use the same name for both. Because the schema is a variable, there's no name conflict if the type uses the same name. However, I find that this can lead to confusing situations when trying to import one or the other—my personal preference is to name the Zod schema with Schema at the end to make it clear which is which. Conclusion Zod is an excellent tool for easily and confidently asserting that the data you're working with is exactly the sort of data you were expecting. It gives us the ability to assert at runtime that we've got what we wanted, and allows us to then craft strategies to handle what happens if that data is wrong. Combined with the ability to infer TypeScript types from Zod schemas, it lets us write and run more reliable code with greater confidence....

Nov 15, 2024

7 mins

JavaScriptZod

CSS Hooks: A new way to style your React apps

In the world of web development, the management of styles has always been a crucial aspect of building modern and responsive user interfaces. With the rise of CSS in JS libraries like Material UI and Chakra, developers have started creating dynamic and reusable styles using JavaScript; however, the performance implications of these libraries have led to the exploration of alternative solutions. One such solution is CSS Hooks, a library that offers a different approach to managing styles in web applications. The Problem with CSS in React and CSS in JS Libraries In React applications, you typically write styles directly with the style prop by applying classes with className, or if you’re using a framework like Material UI or Chakra then you may use the sx attribute, which is more powerful than the baseline attributes. There are some performance concerns in the case of the sx attribute specifically. The framework has to dynamically generate classes with your styles and apply them to the document whenever it is used. This can become an issue with large and complex applications as your styles will be regularly regenerated on the fly as the user navigates through the application. This has led developers to seek alternative solutions that offer similar flexibility but with better performance. Introduction to CSS Hooks CSS Hooks improve upon the aforementioned issues by pre-generating your CSS using configuration hooks. The styles that these hooks generate remain static during the lifetime of the application and are reused wherever possible. CSS variables are utilized under the hood whenever dynamic behavior is needed. By dynamic behavior, I’m referring to advanced CSS features such as pseudo-class selectors and media queries, which are both traditionally unavailable for use with inline styles. CSS Hooks utilize an interesting trick under the hood to accomplish this, which we will now explore. The Power of CSS Variables and the Fallback Trick As mentioned before, CSS variables are utilized in order to support using advanced dynamic behavior such as pseudo-selectors in our inline styles. CSS variables are core to something known as the “fallback trick”. The gist of how the fallback trick works is to have the pseudo-selector or media query toggle the state of a variable between initial and an empty invalid value, and then we put the value that we actually want to toggle as the “fallback” value in the reference to the CSS variable inside of the inline style itself. Changing this fallback value inside of an inline style essentially allows us to control the values used by the pseudo-selector without having to regenerate linked stylesheets as we only have to change the inline style attribute. This is best explained with an example: ` This plain HTML and CSS code effectively allows us to utilize the focus pseudo-selector on any element that we want using inline styles only. This is what CSS Hooks effectively does in the background when you use dynamic styles. No nasty re-renders and mucking with stylesheets needed! How to use CSS Hooks Now that we know how CSS Hooks fundamentally work, let’s try using them. The library itself is much easier to use than the aforementioned example. Installation and usage of CSS Hooks is easy. Firstly, we must install the @css-hooks/react package into a React project with the package manager of your choice (npm, pnpm yarn, etc). Once that’s done, all that’s left is a little bit of configuration. You have to first use a utility function called createHooks that is exported from the package we just installed. The result of that function returns a couple of variables in an array that can be deconstructed, the first being a stylesheet in the form of a string, and the second being a function. ` I recommend putting this in a separate file that can be imported from multiple other places in the project as this is where we will be configuring hooks across the project. We’re exporting both hooks and css, and both are important. We need to include hooks into the DOM as it contains the styles of the hooks that we’ll be referencing in our components. In your root component you need to add the following tag so that the generated CSS can be used. ` How this is done may vary based on the libraries that you are using. After that’s done, you can integrate CSS Hooks into your components while writing inline styles mostly just like you would do normally, with the only change being the addition of an additional call to the css function that we exported earlier: ` Now that the core boilerplate is set up, your components can now utilize CSS Hooks in theory; however we need to actually add some hooks to start with before we can use them, or else we’ll just be limited to basic styles in the same way that React inline styles are. Make your own hooks We can define our hooks inside of the css-hooks file that we created earlier, the same one that exports the css helper function that gets CSS Hooks working with our components. Hooks are defined in the options parameters that are currently empty. Defining a simple hook that allows us to define inline hover styles for a component is very easy and can be done as follows: ` Although both the key and the value in this example are the same, they both serve different purposes. The key is the name of the hook that we reference when we want to use it, while the value is the spec. The spec is the actual CSS selector that we’re writing. The name can be anything that you want it to be so long as it doesn’t clash with any existing style properties or hooks. Recommended hooks Although making your own hooks may be necessary at times, there is another repository provided by the CSS Hooks authors that adds a way to generate many useful hooks called @css-hooks/recommended. I highly recommend using this library to generate your hooks if at all possible, and the usage is pretty simple. Here’s an example showing how you can use it to generate media queries, color scheme specific styles and pseudo-selectors: ` Then with that done, these can be utilized as follows: ` We can also modify the hooks configuration to add our own hooks in addition to the recommended hooks by using the spread operator: ` Then that custom hook can be used by simply using its key name: ` Summary CSS Hooks offers a performant alternative to traditional CSS in JS libraries by leveraging CSS variables and providing a unique approach to managing styles in web applications. By pre-generating the stylesheet based on configured hooks and utilizing CSS Variables for dynamic behavior, developers can create reusable styles without the performance overhead associated with dynamic stylesheet generation. If you're interested in exploring CSS Hooks further, be sure to check out @CSSHooks for more information. You can also find some of the examples demonstrated here in a working app in our demos repository. Thank you for reading!...

Feb 28, 2024

5 mins

CSSReact

Build Facial Recognition and Chatbot AIs using TypeScript with Jack Herrington

In this JS Drop, Danny Thompson is joined by YouTuber Jack Herrington to explore a unique TypeScript and AI project that lets you recognize TV show characters in real time. Just point your camera at a character, and instantly get their details or even chat with them as if they were real! Jack walks through the tech, explaining how client-side face recognition and server-side AI work together to make this possible using the Versal AI library. They discuss prompt engineering, building efficient APIs, and ensuring smooth, interactive AI responses. Jack also shares potential real-world applications, from entertainment to security. Chapters - 0:32 Project Overview – Jack explains the AI-powered character recognition project - 2:17 Setting Up the Project – Overview of how the application is structured and initial setup - 3:04 Client-Side AI – How face detection and character recognition work on the client side - 5:12 Switching to Server Side – Jack demonstrates server-side AI and setting up API endpoints - 8:20 Explaining AI Tooling – How tools and prompts are used to give context to the AI - 10:01 Detailed Prompt Structure – Breaking down the prompt and character context for AI responses - 12:40 Client-Server Interaction – Using the Versal AI library to manage streaming responses - 15:09 Handling Character Data – Training the AI on specific character images and details - 18:15 Practical Use Cases – Discussing potential real-world applications for the face recognition tool - 21:34 Challenges and Lessons Learned – Jack shares obstacles he faced and how he overcame them - 25:45 Building the API – Tips and considerations for creating reliable API endpoints - 28:40 Handling User Inputs – Testing unexpected questions and how the AI responds - 32:00 Using Advanced AI Models – Jack talks about choosing GPT-4 and issues with smaller models - 35:47 Introducing ProNextJS.dev – Jack discusses his new Next.js course, covering advanced topics - 37:20 Closing Thoughts – Danny and Jack wrap up with final thoughts and a link to the GitHub repo Follow Jack Herrington on Social Media Twitter: https://x.com/jherr Linkedin: https://www.linkedin.com/in/jherr/ YouTube: https://www.youtube.com/@jherr Sponsored by This Dot: thisdot.co...

Nov 11, 2024

2 mins

AIArtificial Intelligence

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Introduction to Testing in Node.js with Mocha and Chai

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Installing Mocha and Friends

The Basics

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Hooks

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