ReactJS

What's New in Next.js

Published Nov 25, 2020

Updated Feb 14, 2023

4 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This article was written over 18 months ago and may contain information that is out of date.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.

What's new in Next.js

On October 27th 2020, the first ever Next.js Conf streamed to an audience of more than 34K.

Let's go over the most important highlights!

Next.js v10

The new version of Vercel's framework was released, boasting exciting new features that directly affect website performance and Web Vitals Metrics, allowing developers to have a better impact in search results.

So what are these features?

New `Image` Component

One of the core metrics of the Web Vitals is the Largest Contentful Paint (LCP), and it directly affects user experience as a web application loads.

Often, images are not properly optimized to have different behaviour based on the visitor device. For example, you may be downloading a 1000x1000 pixels image and only rendering a 200x200 pixels image on mobile devices.

Luckily, this new version introduces a new Image component that acts as a drop-in replacement for the HTML <img> element, with the advantage that comes with built-in optimization for different devices. Under the hood, images rendered with the new Image compoonent will be lazy-loaded, and will only render when they are within the viewport.

Migration

Integrating the new Image component is as easy as replacing all img elements with the new component:

Before

<img alt="Company Logo" src="/images/logo.jpg" width="200" height="125" />

After

import Image from 'next/image'

<Image alt="Company Logo" src="/images/logo.jpg" width="200" height="125" />

One important thing to note is that the width and height props are required. Although one may think it will affect responsive layouts, this is not the case, because the image will be automatically made responsive based on the aspect ratio from the provided dimensions.

Internationalization

After 2 months of gathering feedback from the community, in an RFC, the ability to add i18n to a Next.js application is now built into the framework. Before this release, it was only possible through a custom server, which is not encouraged anymore by the Vercel team.

Instead, you can now easily configure a Next.js application to have i18n capabilities by just adding some extra parameters to the configuration file:

next.config.js

module.exports = {
  i18n: {
    locales: ['en','es'],
    defaultLocale: 'en',
  },
}

The routing can happen at two different levels; subpath routing (adds the locale as an URL path, i.e. https://miwebsite.com/es/about) and domain routing (reads the locale from an absolute URL, i.e https://mywebsite.es).

To enable domain routing, the configuration file will need to be slightly different:

next.config.js

module.exports = {
  i18n: {
    locales: ['en', 'es'],
    domains: [
      {
        domain: 'mywebsite.com',
        defaultLocale: 'en',
      },
      {
        domain: 'mywebsite.es',
        defaultLocale: 'es',
      },
    ]
  },
}

This new version also has automatic language detection on the / route by checking the Accept-Language header, which will be matched against the provided configuration.

Improved `Link` component

Trying to create a link to a dynamic route by using a combination of the href and as props was somewhat confusing before. However, now the next/link component only needs the href prop.

Before:

<Link href="/blog/[slug]" as="/blog/hello-world" />

After:

<Link href="/blog/hello-world" />

This is not a breaking change so you can still continue using both props. However, now it is more developer-friendly.

Blocking fallback for `getStaticPaths`

What makes Next.js stands over other frameworks is the ability to incrementally generate static pages. This means, if you are building an e-commerce platform with lots of products, you won't need to statically generate all pages at build time. Instead, you can opt in to incrementally generate the pages in getStaticPaths.

Starting in Next.js 9.3, you could opt-in to this by adding a fallback property in getStaticPaths. At build time, a static fallback page was generated so the first time a user visited a given URL, it would be served, and once the data was fetched, a new page would be served, and pushed to the CDN for subsequent loads.

However, now we have the ability to avoid showing a fallback page at all and instead blocking the render until the data has been fetched by just adding fallback: 'blocking' to the returned object from getStaticPaths.

Before:

export async function getStaticPaths() {
  return {
    fallback: true,
  }
}

After:

export async function getStaticPaths() {
  return {
    fallback: 'blocking',
  }
}

`notFound` support for `getStaticProps` and `getServerSideProps`

The getStaticProps and getServerSideProps methods implemented a new boolean property: notFound. By adding it, your users will be redirected to your 404 page.

export async function getStaticProps () {
  return {
    notFound: true,
  }
}

redirect support for getStaticProps and getServerSideProps

A redirect property was also added to both methods. Allowing you to redirect to either an internal or an external page, with the ability to also mark the redirect as permanent.

export async function getStaticProps() {
  return {
    redirect: {
      destination: '/new-page',
      permanent: true,
    },
  }
}

Vercel's Next.js Analytics

Although this is not related only with the framework, Vercel released Next.js Analytics, a new feature of the platform that collects analytics from real users by measuring Core Web Vitals and generating reports.

Next.js Commerce

If you are planning to spin up an e-commerce functionality on top of Next.js, you can just clone an all-in-one starter kit that includes all of the previously mentioned new features.

Moving forward

We are planning to release a series of Next.js articles in the upcoming weeks, including in-depth guides on how to create a Next.js application using all of the new features, including best practices and tips from the community. Stay tuned!

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About the author(s)

Kevin Wolf
@kevinwolfdev @kevinwolfdev

Integrating AI Models Locally with Next.js ft. Jesus Padron

Jesus Padron from the This Dot team shows you how to integrate AI models into a Next.js application. Jesus walks through the process of running Meta's Llama 3.1 model locally, leveraging OpenAI's Whisper for speech-to-text conversion, and using OpenAI's TTS model for text-to-speech conversion. By the end of the episode, listeners will know how to create an AI voice assistant that processes voice input, understands the content, and responds audibly. Chapters: 1. Introduction to the Episode (00:00:03) 2. Overview of Llama 3.1 and Setup (00:02:14) 3. Setting Up the Next.js Application (00:04:40) 4. Recording Audio with MediaRecorder API (00:11:37) 5. Integrating OpenAI's Whisper for Speech-to-Text (00:36:46) 6. Generating Responses with Llama 3.1 (00:48:24) 7. Implementing Text-to-Speech with OpenAI's TTS (01:03:26) 8. Final Testing and Demonstration (01:06:37) 9. Summary and Next Steps (01:09:01) 10. Closing Remarks (01:14:19) Follow Jesus on Social Media Twitter: https://x.com/padron4497 Github: https://github.com/padron4497...

Sep 3, 2024

1 min

NextJSAI

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

Supercharging Next.js Development with Tailwind CSS

Supercharging Next.js Development with Tailwind CSS When creating a new Next.js project (and in general, any React.js application), choosing a styling solution can be a time-consuming part of the process. While there are plenty of approaches to styling a React.js application, in this case, we are going to use TailwindCSS. Unlike CSS-in-JS solutions that only provide the styling system or component libraries that give you opinionated component styles, TailwindCSS provides you with a set of unopinionated utility CSS classes that your components can leverage to build your User Interface, with the chance of extending them via configuration files. Scaffolding our application > Note: We will use yarn across this guide, but feel free to use npm if that is your personal choice. Let's create a new Next.js application. While there is an official create-next-app CLI tool, we will manually scaffold our project to avoid removing unnecessary files created by it. Create a new directory and cd into it. ` Initialize an empty package.json ` Install required dependencies ` Create a homepage in pages/index.js By default, the Next.js filesystem router will pick all pages created in the pages directory and make them available as routes. pages/index.js ` Add dev, build and start scripts to our package.json package.json ` Now, let's run yarn dev to start your development server and navigate to http://localhost:3000 to preview your unstyled Home page. Configuring TailwindCSS We have a basic web application now, but its lack of styling, colors, and a nice font makes it look boring and lifeless. Install TailwindCSS as a devDependency ` Generate a TailwindCSS config file Run yarn tailwindcss init to create a tailwind.config.js file if you want to later customize the default configuration or add more plugins. Additionally, if you are using the TailwindCSS VSCode Extension, you will get intellisense completions for your class names. Create a Global Stylesheet In order to import TailwindCSS utility classes, we need to create a stylesheet in styles/main.css, where we will use the @tailwind directive to import all the required classes and set up some global styles. ` Import the Global Stylesheet To add a Global Stylesheet, we need to create a custom App and import it there. pages/_app.js ` Configure PostCSS If you run yarn dev and navigate to http://localhost:3000, you will see an unstyled page like the following: This is happening because Next.js doesn't know (_yet!_) what to do with the @tailwind and @apply directives. For that, we will leverage Next.js's built-in PostCSS support to parse our CSS file. Create a postcss.config.js file in your project root with the following code: postcss.config.js ` Now, let's restart our development server to see it working: Adding More Styles Now that our TailwindCSS config is working and PostCSS is compiling, we can start consuming those rules in our app routes. Let's edit our Home Page to make it look better. pages/index.js ` It should look like this: Optimizing Build Result Our Next.js project is successfuly set up to use TailwindCSS 🎉. However, we can still run some optimizations to make it work with all browsers and reduce final CSS file size. Polyfill modern CSS Most modern webapps use modern CSS features that old browsers does not support, and manually covering all cases can be frustrating, time-consuming and pointless since we have tools like PostCSS Preset Env that can automate the process. Install postcss-preset-env ` Add postcss-preset-env as a plugin to postcss.config.js postcss.config.js ` Refer to the postcss-preset-env documentation for more details on how it works and what it solves. Purge unused utility classes If we run the yarn build command, we will notice some warnings from the PostCSS step: ` This is happening because the final build is including all of the TailwindCSS utility classes, which will create a *huge* StyleSheet and we don't want to slow down the page load for our users. Luckily, TailwindCSS config has an option to control the file size that we can leverage by configuring the purge property of our tailwind.config.js file. tailwind.config.js ` By using that configuration, PostCSS will analyze the code within components and pages directories, extract class names and match them with our generated TailwindCSS utility classes. This means the final CSS file will only have the neccessary classes to work and no dead code ✨. Moving forward We have concluded our mini guide on how to Supercharge your Next.js Development with TailwindCSS. Pretty easy, right? I personally think the folks at Vercel have been really working hard to make integrations like this as seamless as possible. If you want to keep digging into the available utility classes, how to customize your configuration, add more variants or even create your own plugins, refer to the TailwindCSS documentation. Also, if you just want a sneak peak on what you can do with TailwindCSS without creating a new project, go to the Tailwind Play tool where you will be able to try it in your browser. If you want a finished code sample, refer to this GitHub repository....

Oct 21, 2020

5 mins

ReactNextJSTailwind CSS

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

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.

What's New in Next.js

What's new in Next.js

Next.js v10

New `Image` Component

Migration

Internationalization

Improved `Link` component

Blocking fallback for `getStaticPaths`

`notFound` support for `getStaticProps` and `getServerSideProps`

Vercel's Next.js Analytics

Next.js Commerce

Moving forward

Kevin Wolf

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Integrating AI Models Locally with Next.js ft. Jesus Padron

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Introduction to Zod for Data Validation

What's new in Next.js

Next.js v10

New Image Component

Migration

Internationalization

Improved Link component

Blocking fallback for getStaticPaths

notFound support for getStaticProps and getServerSideProps

Vercel's Next.js Analytics

Next.js Commerce

Moving forward

Kevin Wolf

Integrating AI Models Locally with Next.js ft. Jesus Padron

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

Supercharging Next.js Development with Tailwind CSS

Introduction to Zod for Data Validation

Let's innovate together!

Integrating AI Models Locally with Next.js ft. Jesus Padron

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

Supercharging Next.js Development with Tailwind CSS

Introduction to Zod for Data Validation

New `Image` Component

Improved `Link` component

Blocking fallback for `getStaticPaths`

`notFound` support for `getStaticProps` and `getServerSideProps`