Javascript

Exploring the Hidden Gems of the Next Image Component: What You Might Be Overlooking

Published Feb 9, 2024

Updated Feb 9, 2024

5 min read

Welcome to a journey of discovery where we'll explore the often-overlooked capabilities of the Next.js Image component. At first glance, this component might seem like a straightforward tool, merely enhancing performance compared to a regular img tag. However, there's more to it than meets the eye. In this blog post, we'll uncover the magic of some features of the Next.js Image component. It's easy to use, yes, but it also packs a punch with features that go beyond simple image rendering. These hidden features can significantly impact your project, and missing them could mean not fully utilizing the component's potential. Let's dive into this exploration, unraveling the subtle yet powerful aspects of the Next.js Image component that are easy to overlook. So, without further delay, let's get started!

Size

First up, let's chat about width and height. These are rules for how big your image shows on the screen. You gotta use them most of the time. But hey, if you're pulling in an image directly into your code (that's what we call 'static importing') or you want your image to just fill up whatever space it's in (using the 'fill' prop), then you can skip these rules. Neat, right? But that's not all there's to it. You see, when Next.js fetches the image from the server, it does something behind the scenes. It doesn’t just grab any size of the image. Instead, Next.js is smart and picks the best size that fits the spot on your webpage. This means your images load faster and look just right, whether on a big desktop screen or a small phone screen. It's like having a personal assistant for your images, making sure they are always looking their best and not slowing down your site. Here's a part that's easy to miss: Next.js uses a feature called 'srcset' to decide which image size to download from the server. By default, Next.js uses the following default:

module.exports = {
images: {
	imageSizes: [16, 32, 48, 64, 96, 128, 256, 384],
}

So, imagine you have an image that's 75 pixels wide. Next.js will look at this list and pick the closest size. In our case, with these default settings, it would download the 96-pixel version. This occurs because Next.js selects the smallest available size from the list that exceeds the original's width, ensuring the image remains clear on all devices; even if your image is a bit smaller or larger, Next.js picks the nearest size from this list.

This is important to know because if you are looking to get the most performant image possible, you should be aware of these size values. By understanding how Next.js selects the image size, you can better tailor your images to match these presets. This way, you ensure that your site is not only loading the appropriate image size for different devices but also optimizing for speed and efficiency. Keeping an eye on these sizes helps you balance between image quality and fast loading times, which is key for a great user experience on your site.

Quality

Now, let's highlight something about image quality. It's often overlooked, but by default, Next.js sets the image quality to 75. Sure, you can tweak this with the quality prop, but here's the catch: bumping up the quality to 100 will significantly increase the image's file size. This means your images will look super sharp, but they'll also take longer to load. It's all about finding that sweet spot where your images look great without slowing down your website. Remember, a faster site often means a better experience for your visitors.

My advice is to always check what is better for you in each situation. Ask yourself: Consider whether it's more beneficial to opt for larger images at a slightly reduced quality, in alignment with the “srcset” configurations we explored, or if it's preferable to enhance the image quality, even if it means working with smaller dimensions? It's a balancing act. Usually, cranking up the quality to 100 isn't the best move because it makes the image file really big, which can slow down your site. It's all about making smart choices. Go for a quality that makes your images look good without burdening your site's load time. Sometimes, a slightly lower quality image, but well optimized, can be way more effective than a perfect but sluggish, high-quality image.

Loader

The loader prop in Next.js is a key player. It's not just about fetching images; it’s about fetching them smartly and securely. When using images from different places, like a CMS or your server, the loader prop helps you manage them efficiently and safely.

For instance, say you have images on Contentful. Next.js doesn't automatically know the best way to fetch these, but with a custom loader, you can define this process. This loader optimizes your images, selecting the right format, width, and quality. Your images not only look great but load faster, too.

Remember, when fetching images from external sources, it's crucial to use remotePatterns (inside your next.config file) for security reasons. This ensures your site stays safe while handling images from outside.

This approach is scalable and saves time. You set up the loader once, and it works for all your images, making your site efficient and your code neat.

Here’s an example:

// Here's our custom loader for Contentful images
export function contentfulLoader({ src, width, quality }) {
  // We prepare the URL for Contentful
  const normalizedSrc = `https://${src.substring(
    src.indexOf("images.ctfassets.net/"),
  )}`;
  const url = new URL(normalizedSrc);
  // Setting format, width, and quality
  url.searchParams.set("fm", "webp");
  url.searchParams.set("w", width.toString());
  url.searchParams.set("q", (quality || 75).toString());
  return url.href;
}

// And this is our overall custom image loader
export default function customImageLoader({ src, width, quality }) {
  // It decides which loader to use based on the image source
  if (src?.includes("images.ctfassets.net/")) {
    return contentfulLoader({ src, width, quality });
  }
  // If it's not a Contentful image, just return the source
  return src;
}

// next.config.js file

module.exports = {
  images: {
    remotePatterns: [
      {
        protocol: "https",
        hostname: "images.ctfassets.net",
      },
    ],
    formats: ["image/avif", "image/webp"],
    loader: "custom",
    loaderFile: "./src/loaders/imageLoader.js",
  },

}

Placeholder Prop

A little-known yet impactful feature of the Next.js Image component is the placeholder prop. It's all about improving the user experience while your images are loading. Imagine visiting a website and waiting for images to load – it can be a bit dull, right? The placeholder prop can make this wait a lot more pleasant. You can use this prop to display a blur effect or a solid color before the image fully loads. This creates a smoother visual experience. The blur placeholder is particularly popular because it gives a sneak peek of the image without revealing all the details. It's like a teaser, building anticipation for what's to come. Here’s how you can use it:

<Image
  src="your-image.jpg"
  alt="Description"
  placeholder="blur"
  blurDataURL="data:image/..." // a small preview of your image
/>

In this example, blurDataURL is a small, low-quality version of your image that loads much faster. Once the actual image is ready, it seamlessly replaces the blur. This approach significantly enhances the perceived loading speed and keeps your users engaged.

Incorporating the placeholder prop can make a big difference in how professional and polished your site feels, especially on slower connections where image load times are noticeable.

Conclusion

The Next.js Image component is a powerful tool that goes beyond simple image rendering. By understanding its hidden features and capabilities, you can optimize your project for performance and user experience. From utilizing the 'width' and 'height' properties to ensuring the right image quality and using the 'loader' prop for efficient image fetching, there are several aspects to consider. Additionally, the 'placeholder' prop can enhance the user experience by displaying a blur effect or solid color while images load. By exploring these often-overlooked features, you can unlock the full potential of the Next.js Image component and create a more engaging and optimized website.

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)

Jesús Padrón
@padron4497 @jesus4497

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

Tim Neutkens, Co-Author of Next.js on the State of Next

Watch this episode! Tim Neutkens, Co-author and Tech Lead for Next.js, discusses how open source maintainers are simplifying the web, and covers the challenges faced with the current Next.js setup. Tim talks about TurboPack, a solution that optimizes bundling, improves parallelism, caching, and module graph calculations. He also talks about TurboAC, which focuses on addressing performance and compatibility issues, providing seamless transitions for Next.js users. Tim highlights the importance of efficient bundling processes to avoid excessive recompilation and discusses the updates in Next.js versions to enhance caching, rendering behavior, and client-side caching. Tim also discusses some exciting upcoming features in Next.js 15. Tim Neutkens' Socials - Twitter: @timneutkens - GitHub: timneutkens - Bluesky: timneutkens.bsky.social - Website: https://timn.tech/ Links - Vercel on Twitter, LinkedIn, Facebook, Instagram, YouTube, GitHub and Vercel's website - Next.js on Twitter, GitHub, LinkedIn, YouTube, Instagram, Facebook, official Next.js website - Turbopack on Twitter, GitHub, YouTube, LinkedIn, Instagram, Official Turbopack Docs - Webpack on GitHub, Twitter, YouTube, and Official Webpack Website Show Notes - [00:00:02] Podcast episode featuring Tim Neutkens discussing Next.js and the upcoming release of TurboPack. - [00:04:27] JavaScript bundlers evolving to handle growth. - [00:07:58] TurboPack solves Webpack limitations efficiently. - [00:12:12] Bundler compatibility for optimal app performance. - [00:16:50] Client components separated in webpack instance. Turbo pack for better parallelism and stability. Industry moving towards server-side. Feed and rollup still relevant. Collaboration between tools for future. - [00:20:57] Replacing part with roll down, similar to Webpack. Overlapping ecosystem with Avonetic Conference. Limits with unbundling and loading on demand. Cycle of building frameworks and hitting limits. History of using Webpack for client-side code. Two compiler architecture for server and client. Coordination between server and client with Webpack. - [00:25:38] Server action imports, turbo pack improves performance. - [00:30:04] Next.js is popular for websites. - [00:34:18] Chipotle using Next in Vercel, exciting improvements. - [00:38:51] Next.js 15 release candidate with changes. This episode is sponsored by Wix Studio....

Aug 7, 2024

2 mins

VercelNextJS

How to build an AI assistant with OpenAI, Vercel AI SDK, and Ollama with Next.js

In today’s blog post, we’ll build an AI Assistant using three different AI models: Whisper and TTS from OpenAI and Llama 3.1 from Meta. While exploring AI, I wanted to try different things and create an AI assistant that works by voice. This curiosity led me to combine OpenAI’s Whisper and TTS models with Meta’s Llama 3.1 to build a voice-activated assistant. Here’s how these models will work together: * First, we’ll send our audio to the Whisper model, which will convert it from speech to text. * Next, we’ll pass that text to the Llama 3.1 model. Llama will understand the text and generate a response. * Finally, we’ll take Llama’s response and send it to the TTS model, turning the text back into speech. We’ll then stream that audio back to the client. Let’s dive in and start building this excellent AI Assistant! Getting started We will use different tools to build our assistant. To build our client side, we will use Next.js. However, you could choose whichever framework you prefer. To use our OpenAI models, we will use their TypeScript / JavaScript SDK. To use this API, we require the following environmental variable: OPENAI_API_KEY— To get this key, we need to log in to the OpenAI dashboard and find the API keys section. Here, we can generate a new key. Awesome. Now, to use our Llama 3.1 model, we will use Ollama and the Vercel AI SDK, utilizing a provider called ollama-ai-provider. Ollama will allow us to download our preferred model (we could even use a different one, like Phi) and run it locally. The Vercel SDK will facilitate its use in our Next.js project. To use Ollama, we just need to download it and choose our preferred model. For this blog post, we are going to select Llama 3.1. After installing Ollama, we can verify if it is working by opening our terminal and writing the following command: Notice that I wrote “llama3.1” because that’s my chosen model, but you should use the one you downloaded. Kicking things off It's time to kick things off by setting up our Next.js app. Let's start with this command: ` After running the command, you’ll see a few prompts to set the app's details. Let's go step by step: * Name your app. * Enable app router. The other steps are optional and entirely up to you. In my case, I also chose to use TypeScript and Tailwind CSS. Now that’s done, let’s go into our project and install the dependencies that we need to run our models: ` Building our client logic Now, our goal is to record our voice, send it to the backend, and then receive a voice response from it. To record our audio, we need to use client-side functions, which means we need to use client components. In our case, we don’t want to transform our whole page to use client capabilities and have the whole tree in the client bundle; instead, we would prefer to use Server components and import our client components to progressively enhance our application. So, let’s create a separate component that will handle the client-side logic. Inside our app folder, let's create a components folder, and here, we will be creating our component: ` Let’s go ahead and initialize our component. I went ahead and added a button with some styles in it: ` And then import it into our Page Server component: ` Now, if we run our app, we should see the following: Awesome! Now, our button doesn’t do anything, but our goal is to record our audio and send it to someplace; for that, let us create a hook that will contain our logic: ` We will use two APIs to record our voice: navigator and MediaRecorder. The navigator API will give us information about the user’s media devices like the user media audio, and the MediaRecorder will help us record the audio from it. This is how they’re going to play out together: ` Let’s explain this code step by step. First, we create two new states. The first one is for keeping track of when we are recording, and the second one stores the instance of our MediaRecorder. ` Then, we’ll create our first method, startRecording. Here, we are going to have the logic to start recording our audio. We first check if the user has media devices available thanks to the navigator API that gives us information about the browser environment of our user: If we don’t have media devices to record our audio, we just return. If they do, then let us create a stream using their audio media device. ` Finally, we go ahead and create an instance of a MediaRecorder to record this audio: ` Then we need a method to stop our recording, which will be our stopRecording. Here, we will just stop our recording in case a media recorder exists. ` We are recording our audio, but we are not storing it anywhere. Let’s add a new useEffect and ref to accomplish this. We would need a new ref, and this is where our chunks of audio data will be stored. ` In our useEffect we are going to do two main things: store those chunks in our ref, and when it stops, we are going to create a new Blob of type audio/mp3: ` It is time to wire this hook with our AudioRecorder component: ` Let’s go to the other side of the coin, the backend! Setting up our Server side We want to use our models on the server to keep things safe and run faster. Let’s create a new route and add a handler for it using route handlers from Next.js. In our App folder, let’s make an “Api” folder with the following route in it: We want to use our models on the server to keep things safe and run faster. Let’s create a new route and add a handler for it using route handlers from Next.js. In our App folder, let’s make an “Api” folder with the following route in it: ` Our route is called ‘chat’. In the route.ts file, we’ll set up our handler. Let’s start by setting up our OpenAI SDK. ` In this route, we’ll send the audio from the front end as a base64 string. Then, we’ll receive it and turn it into a Buffer object. ` It’s time to use our first model. We want to turn this audio into text and use OpenAI’s Whisper Speech-To-Text model. Whisper needs an audio file to create the text. Since we have a Buffer instead of a file, we’ll use their ‘toFile’ method to convert our audio Buffer into an audio file like this: ` Notice that we specified “mp3”. This is one of the many extensions that the Whisper model can use. You can see the full list of supported extensions here: https://platform.openai.com/docs/api-reference/audio/createTranscription#audio-createtranscription-file Now that our file is ready, let’s pass it to Whisper! Using our OpenAI instance, this is how we will invoke our model: ` That’s it! Now, we can move on to the next step: using Llama 3.1 to interpret this text and give us an answer. We’ll use two methods for this. First, we’ll use ‘ollama’ from the ‘ollama-ai-provider’ package, which lets us use this model with our locally running Ollama. Then, we’ll use ‘generateText’ from the Vercel AI SDK to generate the text. Side note: To make our Ollama run locally, we need to write the following command in the terminal: ` ` Finally, we have our last model: TTS from OpenAI. We want to reply to our user with audio, so this model will be really helpful. It will turn our text into speech: ` The TTS model will turn our response into an audio file. We want to stream this audio back to the user like this: ` And that’s all the whole backend code! Now, back to the frontend to finish wiring everything up. Putting It All Together In our useRecordVoice.tsx hook, let's create a new method that will call our API endpoint. This method will also take the response back and play to the user the audio that we are streaming from the backend. ` Great! Now that we’re getting our streamed response, we need to handle it and play the audio back to the user. We’ll use the AudioContext API for this. This API allows us to store the audio, decode it and play it to the user once it’s ready: ` And that's it! Now the user should hear the audio response on their device. To wrap things up, let's make our app a bit nicer by adding a little loading indicator: ` Conclusion In this blog post, we saw how combining multiple AI models can help us achieve our goals. We learned to run AI models like Llama 3.1 locally and use them in our Next.js app. We also discovered how to send audio to these models and stream back a response, playing the audio back to the user. This is just one of many ways you can use AI—the possibilities are endless. AI models are amazing tools that let us create things that were once hard to achieve with such quality. Thanks for reading; now, it’s your turn to build something amazing with AI! You can find the complete demo on GitHub: AI Assistant with Whisper TTS and Ollama using Next.js...

Sep 27, 2024

8 mins

AINextJSJavaScript

Svelte 5 is Here!

Svelte 5 was finally released after a long time in development. Fortunately, we've been able to test it for some time, and now it has a stable release. Let's dig into its features and why this is such a significant change, even though Svelte 4 code is almost 100% compatible. Svelte syntax everywhere This is one of my favorite additions. Previously, Svelte syntax was limited to a component element. Refactoring code from a component and moving it to a JavaScript file worked differently. Now you can use the .svelte.js or .svelte.ts extension, which allows you to use the same syntax everywhere. It's important to note that it's a way to express that this is not just JS, and what you write may be compiled to something else, just like .svelte files are not just html even though they look very similar. Runes The introduction of runes is one of the most significant changes in Svelte. Many users felt attracted to variables being instantly reactive in previous versions. ` There was, however, a lot of magic underneath and a considerable amount of work from the compiler to make it behave reactively. $state In Svelte 5, a reactive variable has to be explicitly declared using the $state rune, which brings a clear distinction between what is reactive and what isn’t. ` In the previous code, $state is not an actual function being called; it's a hint for the compiler to do something special with this declaration. Rune names always start with a dollar sign ($) and do not need to be imported. However, there’s much more to the changes than just the way we declare reactive variables. Runes bring a lot of new features that make Svelte 5 a great improvement. In Svelte 5, objects or arrays use Proxies to allow for granular reactivity, meaning that individual properties in an object are reactive, even if they are nested objects or arrays. If you modify a property, it will only update that property and will not trigger an update on the whole object. It also supports triggering updates when methods like array.push are called. In previous versions, an assignment was required to trigger an update: ` To have a similar behavior of svelte 4 (no deep reactivity), use the $state.raw() rune. This syntax of .* is common for related features of a rune: $state.raw() will require an assignment to trigger reactivity. ` Because proxies are used, you may need to extract the underlying state instead of the proxy itself. In that case, $state.snapshot() should be used: ` $derived We will use $derived and $derived.by to declare a derived state. The two runes are essentially the same, except one allows us to use a function instead of an expression, allowing for more complex operations to calculate the derived values. ` $effect Effects are useful for running something triggered by a change in state. One of the things you'll note from the $effect rune is that dependencies don't need to be explicit. Those will be picked from reactive values read synchronously in the body of the effect function. ` Something to bear in mind is that these dependencies are picked each time the effect runs. The values read during the last run become the dependencies of the effect. ` Depending on the result of the random method, foo or bar will stop being a dependency of the effect. You should place them outside the condition so they can trigger reruns of the effect. *Variants* of the effect rune are $effect.pre, which runs before a DOM update, and $effect.tracking(), which checks for the context of the effect (true for an effect inside an effect or in the template). $props This rune replaces the export let keywords used in previous versions to define a component's props. To use the $props syntax, we can consider it to return an object with all the properties a component receives. We can use JavaScript syntax to destructure it or use the rest property if we want to retrieve every property not explicitly destructured.. ` $bindable If you want to mutate a prop so that the change flows back to the parent, you can use the $bindable prop to make it work in both directions. A parent component can pass a value to a child component, and the child component is able to modify it, returning the data back to the parent component. ` $inspect The $inspect rune only works in dev mode and will track dependencies deeply. By default, it will call console.log with the provided argument whenever it detects a change. To change the underlying function, use $inspect.with().with): ` $host The host rune gives access to the host element when compiling as a custom element: ` ` Other changes Another important change is that component events are just props in Svelte 5, so you can destructure them using the $props() rune: ` A special children property can be used to project content into a component instead of using slots. Inside the components, use the [@render[(https://svelte.dev/docs/svelte/@render) tag to place them. ` Optional chaining (?.) prevents from attempting to render it if no children are passed in. If you need to render different components in different places (named slots before Svelte 5, you can pass them as any other prop, and use @render with them. Snippets Snippets allow us to declare markup slices and render them conveniently using the render tag (@render). They can take any number of arguments. ` Snippets can also be passed as props to other components. ` ` Conclusion Some exciting changes to the Svelte syntax were introduced while, at the same time, maximum compatibility efforts were made. This was a massive rewrite with numerous improvements in terms of performance, bundle size, and DX. Besides that, a new CLI has been released, making the whole experience of starting a project or adding features delightful. If you haven't tried Svelte before, it's a great time to try it now....

Nov 27, 2024

4 mins

Svelte

Let's innovate together!

We're ready to be your trusted technical partners in your digital innovation journey.

Whether it's modernization or custom software solutions, our team of experts can guide you through best practices and how to build scalable, performant software that lasts.

Exploring the Hidden Gems of the Next Image Component: What You Might Be Overlooking

Size

Quality

Loader

Placeholder Prop

Conclusion

Jesús Padrón

You might also like

Introduction to Zod for Data Validation

Tim Neutkens, Co-Author of Next.js on the State of Next

How to build an AI assistant with OpenAI, Vercel AI SDK, and Ollama with Next.js

Svelte 5 is Here!

Let's innovate together!

You might also like

Introduction to Zod for Data Validation

Tim Neutkens, Co-Author of Next.js on the State of Next

How to build an AI assistant with OpenAI, Vercel AI SDK, and Ollama with Next.js

Svelte 5 is Here!