Javascript

Performance: Optimizing Images on Gatsby Sites

Published Aug 4, 2020

Updated May 30, 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This article was written over 18 months ago and may contain information that is out of date.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.

Performance: Optimizing Images on Gatsby Sites

Our Labs website is written using Gatsby, and uses images in many places. We have images that are both statically hosted, and images that are hosted in Contentful.

The Problem

We found that many of our static images weren't compressed as optimally as they could be and were inflating page sizes.

This example comes from a page with many images that are being used as-is with no post-processing in the site build. Some of the images are lossless compressed PNGs that can be JPGs instead, and some of them are already JPGs but their sizes far exceed the size of the container they're in.

The Solution

Instead of manually optimizing each static image we can let gatsby-image do this for us.

Static Images

The static images can be easily fixed by replacing all uses of <img> with <Img> from gatsby-image. GQL queries will also need to be updated to return fixed or fluid images. gatsby-image doesn't take image urls directly like a normal <img> tag does, but instead, takes a special fluid or fixed type that optimizes the images differently. The fixed type should be used when the width and height are known, and will not change. The fluid type should be used if the image will stretch to fill the container that it's in, depending on the screen size.

Here's an example of an <img> for avatars being replaced with a fixed image.

import Img from 'gatsby-image';

...

{/* Before using the built-in img */}
<img src={testimonial.avatarUrl} alt="avatar" />

{/* A fixed image using gatsby-image */}
<Img fixed={testimonial.avatarUrl.childImageSharp.fixed} alt="avatar" />

The type of avatarUrl needs to be changed from a string to a sharp fixed image in the GQL query. This is also where optimization parameters such as size and quality can be configured.

# Before
items {
  id
  name
  twitter
  avatarUrl
  quote
  category
}

# After
items {
  id
  name
  twitter
  avatarUrl {
    childImageSharp {
      fixed(width: 44, height: 44) {
        ...GatsbyImageSharpFixed
      }
    }
  }
  quote
  category
}

Here, just width and height are specified, but there are other useful parameters such as quality and toFormat, which can be useful if you want to make sure images are converted and compressed to your liking during the build. Here's an example that ensures images have a size of 160x160 and are JPGs with 80% quality:

fixed(width: 160, height: 160, toFormat: JPG, quality: 80) {
  ...GatsbyContentfulFixed
}

All static image paths returned by the query should be relative so that gatsby-image can find them. Absolute image paths will not work with it like they do with <img>.

Optimizing Images from Contentful

While the gatsby-image package makes it very easy to optimize images when you're dealing with content templates, it doesn't work well on its own if you're dealing with markdown or pre-supplied HTML. Posts on our Labs blog are written in markdown, and are stored with Contentful, and we render posts by converting them to HTML with gatsby-transformer-remark.

We're able to optimize these images using gatsby-remark-images-contentful. This plugin extends gatsby-transformer-remark to output images that use parameters from Contentful's Image API, and to generate different sized versions of images that are chosen based off of the size of the device. This plugin also adds a "blur up" effect as the image is downloaded, and holds the size of the incoming image to prevent a reflow. It's also notable that using this plugin will give us image lazy loading as well, which can speed up load times for image heavy pages substantially.

Here's how we configured this plugin in gatsby-config.js:

{
  resolve: 'gatsby-transformer-remark',
  options: {
    plugins: [
      {
        resolve: `gatsby-remark-images-contentful`,
        options: {
          // This should be the maximum width that your content containers can be.
          maxWidth: 768,
        },
      },
    ],
  },
},

Before & After Results

Here's an example of how our teams page has improved and went from a 856kB image weight to 300kB with these simple changes.

Before

After

Here's an example of an image heavy blog post with images from Contentful that had its initial load size cut by almost 50%, from 6.1MB to 3.4MB.

Before

After

The gains seen here come mostly from lazy loading provided by gatsby-image, so images are only downloaded once they're about to come into view; however savings also come from serving images of appropriate sizes, so a mobile device such as the Pixel 2 will have an initial load of 1.6MB. We don't forcefully format all blog post images to JPG currently as there may be legitimate reasons to not use them, e.g. you want to keep transparency or embed animated gifs.

Closing

Gatsby makes it easy to optimize your sites without expending too much effort. If you're already using Gatsby, I'd encourage you to make sure you're using it to its fullest potential.

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

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

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

How to Implement Soft Delete with Prisma using Partial Indexes

Most APIs and applications that allow you to manage content have some form of deletion functionality. Sometimes you may want to make something recoverable after it’s been deleted, or perform the actual deletion later, and soft deletion is a way to accomplish this. Entities in your database may also have unique identifiers such as slugs associated with them as well, and when you delete things you typically expect these identifiers to become available for use again. In the case of soft deletes though your deleted entities will still be in the database! If you’re using Postgres or SQLite you can get the best of both worlds by using partial unique indexes, which will in the case of soft deletes would only add slugs on undeleted records to the index; however if you’re connecting a partial index compatible database with Prisma then everything isn’t rainbows and sunshine when it comes to partial indexes. A Simple Soft Delete Implementation Before getting into the issue with partial unique indexes, let’s implement soft delete in Prisma using middleware. The following example is adapted from the Prisma documentation regarding soft deletes, though with some slight alterations for convenience. ` The notable change here is this version could be extended to work with multiple models fairly easily by modifying the MODELS_SUPPORTING_SOFT_DELETE constant, and we also handle the throwable variants of the find actions as well. The latter change requires we create a custom type named ExpandedPrismaAction with additional actions as the imported Prisma types don’t have these actions for some reason even though they are encountered in actual usage. Anything specified in the model's constant is assumed to have a field named deletedAt that is nullable. In this example specifically it’s a timestamp field since it’s nice to know when something has been deleted, but it could also be a boolean if you so choose. With that done, soft deletion is transparently implemented, at least insofar as select queries go. If you plan on blindly running update and delete queries as well, then you will need to add middleware for those actions as well. The Problem So as stated before we plan on using unique slugs with our model, in this case slugs for posts, and to free slugs for usage later we need to exclude them from the unique index without deleting the post records outright. Unfortunately, at the time of writing this article Prisma doesn’t allow you to define partial indexes in your schema when defining unique indexes using @@unique inside of your schema. Thankfully, there is a way to workaround this issue. The Solution To workaround this issue you can manage your unique indexes outside of the Prisma schema, though it does come with its own pitfalls that I will cover later. This can be done by creating partial indexes in a migration file instead of defining them in the schema file. It should be mentioned that if you do this then you should ensure a conflicting index is not defined in the schema, or else Prisma will try to overwrite the other index. First let’s create a basic schema with the following contents defining posts: ` In the above schema I’ve left a comment that mentions where a partial unique index definition would go if it was supported. The problem with keeping the definition solely in the migration is that it makes it difficult to discover, and also doesn’t explain the reason why it’s there. Now that we have a Post model with a deletedAt column, create an empty migration and do the following: ` Then the contents of the newly created migration file will be as follows: ` With that all initialized and the middleware setup, and router handler defined, everything should work! Check out the full example on GitHub as I don’t show the full implementation of the example API in this article. The route handlers don’t look any different than you would expect as the middleware handles all of the heavy lifting for us. It is worth noting that DELETE handlers do look different with soft-delete as we’re not intercepting delete calls to Prisma. This is how the implementation of the deletion looks for this example: ` Caveats So there are some caveats to implementing partial delete in this way. The first being that the schema doesn’t reflect the state of the database 1:1. Thankfully, Prisma doesn’t care about untracked indexes existing, but knowing the index exists requires looking in the migration files instead of the schema. This is worked around by leaving a comment, but it still isn’t great. Another downside is that upsert calls do not work with the Prisma client. Making upserts with Prisma requires a unique key, but since Prisma doesn’t track this unique key the typings needed to call upsert do not exist. This can be worked around by making raw queries without the query builder. Summary I hope this article has helped you ease the integration of soft deletes into your application. You can find a full example on → GitHub ← that includes all of the above code snippets, a buildable project, and instructions on how to get it all working....

Feb 2, 2024

4 mins

Prisma

Building a Stripe App: A Step-by-Step Guide to QR Code Generation

Building a Stripe App: A Step-by-Step Guide to QR Code Generation Why Build a Stripe App? I recently participated in an audio space with the Stripe team, and something they said really stuck with me: the Stripe app store is a growing area that isn't overly saturated yet. There's a lot of potential for new apps, and companies can use this opportunity to grow. I work at a company called This Dot Labs, and we've created several Stripe apps and even own one. After looking at the data, I can confirm that the Stripe team was right! Creating a QR Code Generator App For this tutorial, we'll build a QR code app that can take a URL and generate a code for it. This is a good use case to help you understand the ins and outs of Stripe's developer tools. Why QR Codes? QR codes are useful tools that have become common in e-commerce, restaurants, and other industries. While Stripe already has a QR code tool, we'll make our own to familiarize ourselves with their syntax and problem-solving approaches. Project Structure Before we dive into the implementation, let's look at the structure of our Stripe App QR Code project: * .vscode: Contains settings for Visual Studio Code * source/views: Holds the main application views * .gitignore: Specifies files to ignore in version control * stripe-app.json: Defines the Stripe app configuration * ui-extensions.d.ts: TypeScript declaration file for UI extensions * .build: this is where the built Stripe app gets placed. Step-by-Step Implementation 1\. Install Stripe Locally First, you need to install Stripe on your local machine. The documentation provides great instructions for this: * For Mac users: Use Brew to install * For Windows users: Download the package and add it to your environment variables You can find the details here in the stripe docs to install the Stripe CLI https://docs.stripe.com/stripe-cli When using Windows, you must do stripe login from Powershell, NOT from Git bash or any other tool. After the server is up, then you can continue using git bash for everything else. After stripe login, you need to enter stripe apps start. Once you do that, the server is up and running and you can go back to using git bash or any other tool. 2\. Install Dependencies We'll be using an extra package for QR code generation. Install it using npm: ` 3\. Set Up the Main Component Let's look at the home.tsx file, where we'll use Stripe's UI components: ` These components are similar to other UI libraries like Bootstrap or Tailwind CSS. 4\. Create the UI Structure Our app will have: * An input field for the URL * Validation using a regex pattern * Error handling for invalid URLs * QR code generation and display Here is the Home.tsx file that is located in the src/views folder ` * ContextView` is at the top level of the app where we see the Title and the link to the Stripe Docs that we placed in our Context View. * Box is how you use Divs. * Banners can be used to show notification errors or any other item you wish to display. * Textfields are input fields. * Everything else is pretty self-explanatory. 5\. Handle Content Security Policy One problem I personally ran into was when I tried to redirect users, the Stripe policies would block it since I did not express that I knew what it was doing. I had to go into the stripe-app.json file and mention the specific security policies. For this particular exercise, I kept these as null. This is my stripe-app.json file. ` 6\. Configure App Views As you can see here, the stripe-app.json file shows the views for each file I have. The Home.tsx file and the Invoice.tsx are also included This is our way of saying that for each view we have, show the app functionality on that page. Our stripe-app.json file will show it but also, the manifest.js file in our .build folder will also show the same. Any view that doesn't have a file will not show the application's functionality. So, if I were to go to transactions, the app would not show the same logic as the home or invoices page. By following these steps, you'll have a fully functional QR code generator app for Stripe. This is just a simple example, but the potential for Stripe apps is massive, especially for businesses serving e-commerce customers. If you need help or get stuck, don't hesitate to reach out, danny.thompson@thisdot.co. The Stripe team is also very active in answering questions, so leverage them as a resource. Happy coding!...

Dec 18, 2024

4 mins

stripe

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Performance: Optimizing Images on Gatsby Sites

Performance: Optimizing Images on Gatsby Sites

The Problem

The Solution

Static Images

Optimizing Images from Contentful

Before & After Results

Closing

Jamie Kuppens

You might also like

Introduction to Zod for Data Validation

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

How to Implement Soft Delete with Prisma using Partial Indexes

Building a Stripe App: A Step-by-Step Guide to QR Code Generation

Let's innovate together!

You might also like

Introduction to Zod for Data Validation

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

How to Implement Soft Delete with Prisma using Partial Indexes

Building a Stripe App: A Step-by-Step Guide to QR Code Generation