Learn Cloudinary, Http and Npm

Astro is a flexible web framework that allows you to build fast websites to suit your needs.

By default, Astro will build your site using Static Site Generation (SSG). This means the data in your site is loaded at build time so that the end-user gets served static pages that load quickly.

While Astro defaults this way, it also has Server-Side Rendering (SSR) that you can enable if you have more serverful needs in your application.

Along similar lines, Astro utilizes HTML and the browser as a default but when you need more flexibility you can opt into an Astro Island. Islands use React, Vue, Angular, or any frontend JavaScript framework you are used to enhance the user experience in your site.

astro

Now that we have our database set up, we need to use it!

On our blog post page, we have a likes button waiting for us to implement the likes feature. On page load, we'll make a fetch call to a `/api/likes/[slug].ts` GET end point to return the number of likes for the page. 

Similarly, when someone clicks the like button we want to send a POST request to that same endpoint.

On the server side we will set up `GET` and `POST` handlers to implement this functionality. In those functions we'll use Drizzle to return the likes for that blog post and in the case of a `POST` we'll create a record if it doesn't exist or increment a like if it does and return.

Astro API endpoints utilize browser `Response` API so you'll get a chance to handle returning different status's based on the data present.

Create Astro API Endpoints to Get and Update Database Records using Drizzle ORM

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

A Monorepo is where you have multiple projects inside a singular repository.

monorepo

Introduction to Monorepos with NPM Workspaces

npm is the package manager for JavaScript. Find, share, and reuse packages of code from hundreds of thousands of developers — and assemble them in powerful new ways.

Thank you for completing my course!

You’ve learned what monorepos are, how to create them from scratch, how to install different projects inside of it, and how to run different scripts inside of a monorepo. 

So where do you go from here? I suggest creating your own Monorepo project template and publish it on Github. Take a look at Yarn Workspaces, pnpm, Learna, Nx and Turborepo for different monorepo tools. 

I also highly suggest Migrating some of your existing projects to a monorepo. Have a main frontend application that consumes a shared UI library package and a backend service. Another way would be to have a backend service, a mobile application and a frontend application. 

Best of luck and happy coding!

Intro to Monorepos with NPM Workspaces Outro

All of our applications and libraries have `test` and `typecheck` commands already written inside of them. We are able to run both the `test` and `typecheck` commands all at the same time as well. 

This has a downside. They don’t run at the same time, it’s just one after the other. To make them run at the same time, we will implement `concurrently` again.

Running Quality Checks in your Monorepo

We already have a utils package that outputs JavaScript. If we want to use that package inside any of our applications, in our case, blog or dashboard, we would have to install it first.

Share Code Between a Library and Applications Inside a Monorepo

Right now, it is not possible to run all the Dev Scripts across all the packages of the monorepo at the same time, because once we run `npm run dev` with the `-ws` flag, we will run the dev-command in the block first, but that command never ends. It is always watching for changes.

In order to run the `dev` scripts across all the packages in the monorepo, the first thing that we have to do, is to create a dev command for each one of the packages in the root package.json file. Then we can use `concurrently` to change them. We go `concurrently` and we tell it to run the utils, then the blog and then the dashboard scripts.

Run Dev Scripts for All Packages Simultaniously

We can install a dependency inside a monorepo by running npm install, then the name of the dependency, in our case, it’s going to be Lodash. But, we are going to add different flags to that.

If we use the `-w` flag, we would have to specify in what package we want to install Lodash. We will do this for `monorepo/utils`. We could also use the `-ws` flag to install Lodash across all the packages in the monorepo. We can also use `-W` in uppercase to install Lodash in the root of the project.

Install Dependencies for Specific Packages in a Monorepo

To run specific commands inside a monorepo we have to type `npm run` then the name of the command, `build`. We then add the flag `-w` for watch mode and specify which package we want to run the build command. In this case, it’s going to be the `utils` package. 

The full command will look like, `npm run build -2 @monorepo/utils`.

If you want to run all of the commands, you'll need to add the `-ws` flag. 

The full command for running all of our build commands will look like, `npm run build -ws`. 

Run App Specific Commands in a Monorepo

Creating a TypeScript library inside the monorepo is easy if we use tsup. To configure tsup, we have to set up the entry point. In our case, it's going to be the index.ts file inside the src directory.

We will also specify some extra options for tsup such as generating the types and source maps. We can also output different formats. In this case, we want to output an ESM file and a CommonJS file.

Add a TypeScript library to your Monorepo using TSUP

In order to create applications inside our monorepo, we can use different CLI tools like create-next-app or the Vite CLI. We'll install them inside the apps folder. We will create a blog and a dashboard app, one using create-next-app and the other using the Vite CLI.

It is also important to note that the only package-lock.json file that should exist, exists at the root of the project.

Install a Next.js and a Vite.js Applications Into a Monorepo

For this course, we are going to be using NPM Workspaces to configure our monorepo. We will have two workspaces, one for our apps and one for our libraries. We configure those in our package.json.

Configure NPM Workspaces in your Package.json

A monorepo is a group of small projects that can be combined to build bigger projects, or they could just be small projects that doesn't know anything about the other projects.

One great thing about monorepos is that the code is available for everyone at every time. If you are a frontend engineer, and you're working in the blog application and you see that there is a bug in the backend application, you can go and fix it right away.

What are Monorepos

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

JavaScript® (often shortened to JS) is a lightweight, interpreted, object-oriented language with first-class functions, most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic, and supports object-oriented, imperative, and functional programming styles.

javascript

There are some really known patterns to migrate packages in large applications. You might find some code referencing to a different package (such as `@my-namespace/my-package/next`) or specific branches (like `@my-namespace/my-package#next`). 

Anything that helps during package migrations or bumps having several majors of differences.

The good news is that you can have it done directly via NPM! What can be done in this case is an idea called "package alias". So you can have multiple versions of the same package running in your app and you can work on the upgrades mitigating possible issues.

Install Multiple Versions of the Same Package in NPM with Package Aliases



The Hypertext Transfer Protocol (HTTP) is an application-level protocol for distributed, collaborative, hypermedia information systems. It is a generic, stateless, protocol which can be used for many tasks beyond its use for hypertext, such as name servers and distributed object management systems, through extension of its request methods, error codes and headers.

http

We're adding Github authorization to our client app

Create a function that is called on window load. In that function store the parameters that get sent over to the Github auth server. 

In our query params, we will set the response_type parameter to "code" in order to ask the auth server for a code. Asking for a code from the auth server is more secure than asking for the access token through a browser

Finally, create a URL from the params and an authorization endpoint. Using a query selector select an anchor tag and set the href attribute to the URL that you created.

Add GitHub Authorization to an OAuth Client App

Express is a minimal and flexible Node.js web application framework that provides a robust set of features for web and mobile applications.

express

We'll create our client-server using the `express` and `cors` packages

Express will be used to create our HTTP application. We'll need it to post to our endpoints and listen to our port.

We need the `cors` package to make cross-origin requests since the front-end will be a different origin than the server.

Create an OAuth Client-Server for code/token exchange

We'll be parsing the authorization code sent in our query before sending it to the server.

We do this by creating a function that parses our query and retrieves the code parameter. If we find a code in the query we send the code to our server by using `fetch` to make a `POST` request. In the body of the request we send the code and the state in stringified JSON.

Once we make the request we can await a response and store its JSON data (the auth token) in a variable. In a real application, we might also redirect to the homepage or the page the user was just on.

Parse for the Auth Code in a Query before Sending It to a Server

We're going to refactor some of our HTTP application code in order to create the ability to exchange the code inside our HTTP request for a token. 

We can do this by creating a function that takes the code from a request and posts a query that contains our client id and secret to our token endpoint. We then await a response, parse the data from the response, and then return the parsed data.

Exchange Auth Code in a Request for an Access Token

We're going to fetch a user with that token we exchanged the code parameter for in the previous lesson. 

All we have to do is make a fetch request to the `/user` endpoint with an Authorization header that contains the token and then return the data. Like this ``Authorization: `Bearer ${token}``

Fetch Protected Data from a Resource Server with an Access Token

 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

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.

Search 100s of Issues with GitHub CLI as Context for AI Studio

I often find myself wondering how to use certain APIs in certain situations, and I have a couple of keywords that I try and search up on Google. 

What you can do in this situation is turn to GitHub and see how other people have actually used this code on public projects. The GitHub CLI enables us to run searches like this, and we can take the results and work on them programmatically.

Inside of Cursor with their Composer AI agent, we can search for the files we want, tell the agent to download them, and inspect them for better understanding of how those things work together.

Search All of GitHub's Code and Pass the Results to AI Agents in Cursor

Implement virtual scrolling to handle large datasets in an HTML table. Start with a basic table that renders 10,000 rows of data. The browser can struggle with this amount of DOM nodes to render at one time, especially on low end devices. 

We implement tanstack react virtual to render only the necessary amount of rows the user is viewing. The implementation includes proper column sizing, accessibility considerations, and convenient scroll controls, resulting in a smooth and performant table experience regardless of the dataset size.

Create a row virtualized HTML table with Tanstack Virtual

Learn how to implement virtual scrolling in React tables using TanStack Table and Tanstack Virtual with the useWindowVirtualizer React hook.

The most difficult tasks will require the strongest AI models. And those models simply aren't available as AI agents inside of your tooling. So the best workflows are to drag and drop the files you need to be changed and analyzed inside of a reasoning model such as OpenAI's O1 Pro, then letting it create a giant plan for your dumber AI agents to work on. The key here is that instead of just copying and pasting the entire plan into Cursors Composer or your AI agent, it's to create a file inside of your project and ask your agent to tackle the plan one step at a time. This allows you ample opportunity between each step to test and verify the micro steps along the way. Otherwise, as I've experienced many times, a session can go way off track and you burn a lot of time and burn a lot of AI requests Cursors Composer and it starts churning through a plan Cursors Composer and making lots of unwanted changes. Cursors Composer and it's to guide an AI agent to hold its hand along the way, especially through the most complex tasks. Cursors Composer and the most complex tasks Cursors Composer and the most complex tasks require a step-by-step plan.

Planning AI Agent Sessions with Reasoning Models

AI agents will inevitably make mistakes—it's just a matter of when. A key tool in your toolbox, once they do make mistakes after working on a problem for a while, is to use **git diff** to capture all of the changes they've made. 

Once you have those changes in a text file, you have a portable document that you can test against other AI models. This allows you to move the problem into smarter reasoning models with smaller context windows, helping you isolate and focus on the issue at hand.

**git diff** has been invaluable when working with AI agents because they'll often make many good changes but also slip in one tiny error. Finding that one small mistake is **git diff**’s superpower.

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