Learn Apollo, Cloudinary and Gulp

cloudinary

We'll learn how to integrate Cloudinary into our Strapi application so that we can upload image assets in our Strapi dashboard and the resulting image will be stored in Cloudinary and ready for use in our application.

We'll create a `config/plugins.js` file and paste in some config code that sets Cloudinary as the provider with `cloud_name`, `api_key`, and `api_secret`.

Then we'll install the Cloudinary provider plugin with `npm install @strapi/provider-upload-cloudinary`, add the middleware security that lets us use Cloudinary, and finally add Cloudinary credentials to our `.env` file.

Install and Add the Cloudinary Provider to a Strapi Application

Apollo provides a universal GraphQL API on top of your existing services, so you can build new application features fast without waiting on backend changes.

apollo

We will implement the delete shop in our Nextjs application. When the user selects the delete button for a shop in the shop's list, it will execute the `DeleteShopAndProducts` mutation and delete the shop and all associated products. 

Implement Delete Shop Mutation with Apollo Client in Nextjs

Each shop will need to have products added to it by the vendor. To do this we'll need to dynamically create pages based on which shop we want to add to. Next.js has dynamically routing that we will utilize here. We'll set the shop id as the dynamic portion of the route and display a new product form. 

Here we will create that product form component and use Apollo `useMutation` like we did before to take in the form data. You'll notice a new keyword in our mutation query `connect` which we can use because we have a `@relation` set between products and shop. This connect will associate the product to the shop through the id that we grab from the route.

Create Dynamic Next.js Shop Page to Add Products for a Shop with Apollo useMutation

Next.js is a minimalistic framework for server-rendered React applications.

To make an authenticated request, we will need to get logged in users auth tokens from Auth0. Auth0 exposes a `getAccessToken` function that we can use in Next.js `getServerSideProps` to fetch the user token. It's important to note that this function can only be used server side. We'll pass that token into the page we need to send authed requests.

To enable Apollo Client to set the headers we'll extend the client so that allows us to pass in auth tokens and sets the client context. We will then destructure the client from our `useMutation` hook and set the context to include the user token.

Set Dynamic Auth Header for Apollo Client using Next.js getServerSideProps

We are ready to create new shops for our vendors. To do this we'll define a `CreateShop` mutation that will take in all the data that our form provides. Then `useMutation` will accept that mutation and give us a function to pass our data into as well as give us the new created shop data. We'll pass in user information that we get from Auth0. 

This lesson finishes up with an error because Fauna doesn't know if the request came from an authenticated user or not. We'll solve this with dynamic headers in the next lesson.

Create New Shop with Apollo Client useMutation

Now that Apollo Client is set up and ready to be used, we can finally query data from Fauna to use in our application. 

We'll go to the Fauna GraphiQL playground and copy the `findShopById` query that we ran previously. This query will be passed into Apollo's `useQuery` hook.

Query Data with GraphQL in Next.js from a Fauna Database

To build out our Shop products page on the frontend we will need data coming in from the database. To do that we need to connect Fauna to Next.js. We'll do this through Apollo Client because it will give us a lot right out of the box.

To connect our database we'll configure Apollo Client with `httpLink` and `authLink`. On the Fauna side of things we'll create a role and key for Read permissions (just Read for now) and grab the graphql endpoint to supply as environment variables for Next.js.

Connect a Frontend Next.js Application to a Serverless Fauna Database with Apollo Client

Media is an important part in creating a good UX for potential customers of your store, but when serving large unoptimized images, you can end up giving them a poor experience with long page loads.

We can instead use Cloudinary to automatically optimize our images and resize them on the fly with Cloudinary's programmable media API to deliver a more performant online store.

* [Cloudinary URL-Gen for JS - cloudinary.com](https://cloudinary.com/documentation/sdks/js/url-gen/index.html)

Optimize Media with Cloudinary to Deliver Smaller and Performant Images

🚨The Cloudinary UIX has been deprecated. If you are using it, you need to replace it with the App. [Here](https://hygraph.com/docs/guides/integrations/connect-cloudinary#migration) is a step-by-step guide on how to do it. 🚨

We will take an existing UI extension, Cloudinary, clone it down, and create our own instance, installing it into our GraphCMS content editor. Once we add that Cloudinary asset as a field, we will select those images directly from our Cloudinary account and serve those images right inside of our app.

Add and Configure Cloudinary UI Extension to Add Images From a Cloudinary Media Library

We are going to start visualizing what our data looks like by using graphcms' Playground. Here we can query our data and explore our content API right from the dashboard. 

We are going to use Next.js getStaticProps to query our data at compile time so we don't have to make the browser wait for that request when it's getting loaded. 

To actually make our query, we are going to be using [Apollo Client](https://www.apollographql.com/docs/react/).

Query the GraphCMS GraphQL API with Apollo GraphQL

If users will be often querying the number of comments without also querying the list of comments

Then fetching the entire comment collection is wasteful. It's also not compatible with any pagination features that don't return the entire collection.

In this lesson, we will create a custom Data Loader specifically for counting items

Count the Items in a Collection with a Custom Data Loader

The simplest way to count the comments on a post is to use a Data Loader that returns all of the comments and return the length.

As long as the primary access pattern is to always fetch the number of comments along with the comments themselves, the Data Loader will batch them together and you'll get the count for free with no extra database requests.

Count the Items in a Collection By Reusing a Data Loader

One-to-many relationships are a prime candidate for scaling issues. In this lesson we'll add a new loader to fetch all the comments for a given post ID.

Fetching Many Items by Key with GraphQL DataLoaders

The DataLoader class is reusable and creating a new loader is as simple as changing the batch function

Fetching an Item by ID with a GraphQL DataLoader

The GraphQL Foundation has an official DataLoader package that is well-maintained and more feature complete than what we've built so far.

Install the DataLoader Package

If a cache persists between requests, a logged-in user could cache some sensitive data that guest users could access by mistake

An easy way to prevent this is to make new DataLoaders on every request using Apollo's context method, which also makes it easy to inject the DataLoader into any resolver


Create a New Cache on Every GraphQL Request Using Apollo Context

We can make this data loader reusable by refactoring it into a class with configurable scheduling and batch functions

Refactor the Data Loader into a Reusable Class

Querying a thousand posts doesn't have to mean a thousand individual database requests.

We're going to build a batching layer that adds a millisecond pause into each resolver while it gathers all the required post IDs and then fetches them in a single request


Batch Database Requests with a GraphQL API

We're going to build a cache that sits between the GraphQL resolvers and the database and ensures we only have to fetch each user once, while later requests for the same users will return them from the cache immediately


Cache Database Requests Across GraphQL Resolvers

It's easy enough to use GraphQL to query an array of posts from a database. But if you try to include the author of each post, you can run into scaling issues where the server makes an additional request for each and every post.

Using the console, we'll show how to identify when this is happening

And by the end of this series, you'll know how to use data loaders to prevent it.


Detect Scaling Issues When Loading Data In a GraphQL API

Apollo, by default, does not assume what a given query might return. Anytime you invoke a query that wasn't invoked before, or it has been invoked, but not with the same variables, Apollo always asks the backend for the results. In some cases, however, we know that a given query can re-use a specific object or list of objects already in the cache.

Implement cache redirect policies to avoid slow network requests for new queries

In this lesson, you'll learn how to enhance the accuracy of components generated by Cursor Composer by aligning your project's package.json with Cursor's rules. We'll guide you through extracting the list of installed packages from your package.json and incorporating them into your .cursorrules file, along with brief descriptions of their functionalities. By providing Cursor with this contextual information, you'll enable it to generate components that are more precise and functional on the first attempt. The lesson includes demonstrations of component generation both with and without these enhancements, highlighting the improvements in accuracy and efficiency.

Boost Cursor Composer Accuracy: Integrating package.json with Cursor Rules

Speed up your bug-fixing process dramatically with Cursors Composer. Take a warning from your logs, search for relevant terms, then seamlessly import both the warning and search results into Composer. Add documentation and apply fixes to all search results in one go. This streamlined workflow resolves multiple issues rapidly, saving valuable development time.

Batch Fix Every File in Search Results with Cursor's Composer

 We map over the initial set of results then use `Promise.all` to efficiently fetch the details of each pokemon rendered in the table. This allows us to add more columns to our pokemon table.

Fetch and display row details with Promise.all in Tanstack Table

map over the initial set of results then use `Promise.all` to efficiently fetch the details of each pokemon rendered in the tanstack table

Create a custom hook to fetch remote data by passing a promise to the React 19 `use` hook. Then render this data in Tanstack Table by memoizing the results and passing them to `useReactTable`.

Render remote data in a Tanstack Table with React 19's use hook and a promise

Create a custom hook using React 19’s use to fetch data with a promise. Render it in a Tanstack Table by memoizing and using useReactTable.

You know when you're deep in the code trenches and need someone to challenge your assumptions? That's exactly what this is about.

Instead of waiting for someone to poke holes in your master plan, you can paste that strategy straight into AI Studio and let it pressure test your ideas. We're talking about using this tool to validate everything from your feature proposals to your ambitious refactoring strategies.

The real magic happens when you hit that microphone button. Now you're in a real-time verbal sparring match. It's like rubber duck debugging but the duck actually pushes back and makes you defend every architectural decision you've made.

Here's the workflow:
1. Drop your master plan into the system instructions
2. Tell the AI to challenge every assumption
3. Hit that mic and defend your choices like you mean it
4. Watch your plan either emerge stronger or get rightfully dismantled

This approach is perfect when you're:
- Stuck in your own head with a complex problem
- Need to validate if your approach holds water
- Want to catch edge cases before they catch you
- Looking for opportunities you might have missed because you're too close to the code

It's like having a senior dev on call 24/7, ready to challenge your ideas and push you to think deeper about your solutions.

Now go forth and get your ideas challenged. Your projects will thank you for it.


An Uncomfortable and Challenging Conversation with AI Studio

In this lesson, you'll learn how to effectively brainstorm your project ideas before working with AI agents. We'll cover:

1. The importance of having a clear plan before starting work with AI agents
2. Using traditional brainstorming techniques to gather your thoughts
3. Leveraging video recording to capture your brainstorming session
4. How to review and analyze websites for inspiration
5. Documenting likes, dislikes, and potential features for your project
6. Using AI tools (like AI Studio) to organize and process your recorded brainstorming session

The lesson emphasizes a modern approach to brainstorming that combines traditional techniques with video recording, making it easier to later process your ideas through AI tools. This method helps ensure that no ideas are lost and that your thoughts can be effectively organized by AI for future reference.

How to Brainstorm Your Project Features to Prepare for AI Agents

fast-check allows us to create composite generators that suit our testing needs. We'll generate two numbers to create a point simulating an X and Y and assert that the distance should always be greater than or equal to 0.

Generate composite objects with fast-check fc.record

Just using fast-check by itself can make you tests verbose and tough to read. Lucky for us there are many integrations with fast-check such as the `@fast-check/jest` package that will help remove some boilerplate from our test.

Tidy up your test with @fast-check/jest

Many people share .cursorrules files, but trying to craft your own from scratch is a huge pain. Instead, this lesson shows you how to grab ALL community-created .cursorrules files to build a custom rule set for your projects. We'll use Gitingest and Google AI Studio to gather these examples and generate an awesome starting point customized to your specific project.

Generate Custom .cursorrules for Your Project Based on Community Examples

The GitHub CLI allows you to search issues and filter on different criteria like amount of comments. This allows you to search for real-world situations and pains/solutions people have using different technology. 

With the GitHub CLI in hand we'll drop 100s of issues as context in Googles AI Studio and have Gemini determine the 10 most frequently asked questions. This will give us a sense of how people use a library and where people get hung up.