Learn Cloudflare, Faunadb and Reactfire

Dive into the world of real-time web development with this hands-on course! You’ll learn how to build a dynamic chat application using Fauna Streams and Next.js 14, focusing on the essentials and best practices.

**What You’ll Learn:**
- **Next.js Best Practices:** Master Next.js best practices for building fast, maintainable apps.
- **Server-Side Rendering (SSR):** Learn SSR techniques and master features like server actions, server side form submission etc.
- **Database Access Patterns:** Efficiently connect and retrieve data using Fauna.
- **Real-Time Updates:** Implement real-time updates with Fauna Streams.
- **Basic Authentication:** Secure your chat app with simple authentication methods.

**Course Highlights:**
- Practical, project-based learning.
- Clear, step-by-step instructions.
- Focus on modern, real-time web development.
- Tips and tricks for optimizing performance.

By the end of this course, you’ll have a working real-time chat app and a solid grasp of the tools and techniques used to build it.


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

Build a Real-time Next.js 14 Chat App with Fauna 

FaunaDB is a global serverless database that gives you ubiquitous, low latency access to app data, without sacrificing data correctness. It eliminates layers of app code for manually handling data anomalies, security, and scale, creating a friendlier dev experience for you and better app experience for your users.

faunadb

Now that we have user information available to us, we want to connect messages created to the users that are sending them. When we add `read` privileges to the `User` model, we can call `User.identity()` to get the user data we need to set for messages. 

Update Fauna Messages Collection with Authors

React is one of the web’s most popular frameworks for building JavaScript applications. 

If you know what you’re doing, React can drastically simplify how you build, use, and maintain code.

Whether you’re a React newbie or you’re ready for advanced techniques, you can level-up with egghead. Explore our in-depth courses, lessons, and community resources to build more powerful applications and crack open your career.

react

For user signin, we'll implement a proper server action by creating a `actions.js` file where our server actions can live. In our login page, we'll set the component to be a client component to hold some state. This way we can use the new `useFormState` hook from `react-dom` and pass in our login server action.

Within the action we'll call our `UserLogin` function from Fauna and set a cookie with the secret we get back that we can use throughout our application.

Implement Fauna Signin with Next.js Server Actions and useFormState React Hook

Now it's time to actually sign users up for our appliaction.

We'll implement the `doSignup` function that we created earlier. This function will run on the server with the `'use server'` directive so we can instantiate a fauna client and call `UserRegistration` within this function.

From there we'll redirect the user to the `/signin` page which will error out because it's not implemented yet.

Implement Signup Functionality through Fauna with Next.js 14 Server Actions

To have actual users in our application we need to represent those users in our database. Fauna has quite a few ways you can do user authentication but we'll be creating our own using a few built-in Fauna helpers.

We'll start by setting up the necessary Fauna logic for users. We'll add a `User` collection as well as a `functions.fsl` file that we'll expose user sign up and log in. Here we'll see how Fauna gives us a built in `Credentials` document to handle email and password for a user. It also has a login function that it exposes to make that easy for us which will generate a token for a user on login.

After setting up our functions, we'll add an `UnAuthRole` as well as an `AuthRole` and give proper actions for each role.

Add Fauna User Authentication to a Next.js App

To stream messages, we'll implement logic very similar to how we stream the creation of rooms from earlier.

First, we need to convert `MessageList` into a client component. We'll get an interesting error saying that only plain objects can be passed to client components from server components. This happens because the response we get back from Fauna is pretty complex. To fix this error we'll create a simple messages array and pass that down to the `MessageList` component.

In the `MessageList` component we'll create a `useEffect` that will initiate a Fauna stream within a ref and push new messages into state that we are holding in the component.

Stream Chat Messages in Real-time in Next.js from Fauna

Now it's time to actually create messages and display them in rooms within our application.

We'll start by creating a `MessageForm` component that will send a `Message.create` query to Fauna on form submission.

Next, we'll display these messages by updating our room query to also get all the messages for that room. You'll see that we can still just perform one query because we pass in the room id and call `Message.where(.room == room)` to the query.

One note here is if you console log your messages in your page component, you'll notice that the logs show up in the terminal and not the browser. This is because we are using a server component.

Create and Display Messages using Fauna within a Room in Next.js

We'll take a moment to explore how Fauna handles one-to-many relationships inside the fauna shell. 

First we'll create a `Message` collection.

Then we'll connect to the cloud database by running `fauna shell`, because I'm logged and configured this connects to the database I'm working with.

In the terminal we'll see how to create multiple messages that are associated with a room and query for those messages by room.

Explore Faunas one-to-many Relationships between Collections in the Terminal

With rooms being created and displayed on the index page, it's now time to create dynamic routes for each room so that when you click on a room name, you are actually navigated to that room.

We'll do this by using Next.js dynamic route syntax `/rooms/[...id]/page.js`. This will render a page on any `/rooms/:id` passing the component we define the `:id` as params.

Here we'll take that id and fetch the room data from fauna with a `Room.byId` query.

Create Dynamic Routes in Next.js for Chat Rooms

We can add rooms but our application does not update and display rooms when a room is added. This should happen in real-time.

To fix this we'll add a useEffect that will query for all our rooms and use fauna's `client.stream` API to ensure that this happens in real-time.

List Available Chat Rooms in Real-time in Next.js from Fauna

The first step to building a chat app is to have rooms to chat in. We'll need to create a Room collection (or table) in Fauna that we'll be adding to.

In our Next Application we'll create a simple form that has a submit handler that will create those rooms in Fauna. We need to hold some state in our component so we'll turn the page into a client component with `'use client'`.

There will be a little bit more Fauna set up to get things working. We need to create a key for our application to interact with our database so we'll create a key by running `Key.create` in our `fauna shell`. Then we'll install the `fauna` client and make a `client.query` to create the room.

Create a Fauna Room Collection in Next.js with the fauna Client

The first step is to install and configure our database that will be backing our Next.js application. We'll be using Fauna so we head over to the Fauna website and create a database.

Back in our terminal we will install `fauna-shell` so we can interact and manage our database from within our project. After some initializing steps from the CLI we'll be set up to build the app out.

Initialize and Configure a Fauna Database inside a Next.js project with fauna-shell

Cloudflare secures and ensures the reliability of your external-facing resources such as websites, APIs, and applications. It protects your internal resources such as behind-the-firewall applications, teams, and devices. And it is your platform for developing globally-scalable applications.

cloudflare

Supabase Function Hooks allow us to subscribe to change events in the database - such as `insert`, `update` and `delete` - and make a HTTP request with the changed data.

In this lesson, we refactor our `revalidate` route to handle inserts, updates and deletes.

Additionally, we create a Function Hook to subscribe to all change events on the `articles` table, and automatically send a POST request to the `revalidate` route of our Cloudflare Worker.

This automatically refreshes the KV store anytime a value is changed in the database, and decouples the revalidation of our cache, from our user requesting data. This means, theoretically, users should never have to wait for a request to the Supabase origin server, as the cache is automatically populated, updated and cleared to remain in sync with the database.

## Code Snippets

**Update Revalidate route to handle insert, update and delete**

```javascript
router.post(
  "/revalidate",
  withContent,
  async (request, { SUPABASE_URL, SUPABASE_ANON_KEY, ARTICLES }) => {
    const { type, record, old_record } = request.content;
    const supabase = createClient(SUPABASE_URL, SUPABASE_ANON_KEY);

    if (type === "INSERT" || type === "UPDATE") {
      await writeTo(ARTICLES, `/articles/${record.id}`, record);
    }

    if (type === "DELETE") {
      await ARTICLES.delete(`/articles/${old_record.id}`);
    }

    const { data: articles } = await supabase.from("articles").select("*");
    await writeTo(ARTICLES, "/articles", articles);

    return json({ received: true });
  }
);
```

**Run wrangler development server**

```bash
npx wrangler dev
```

**Publish Cloudflare Worker with Wrangler CLI**

```bash
npx wrangler publish
```

## Resources

- [Supabase Functions blog](https://supabase.com/blog/2021/07/30/supabase-functions-updates#function-hooks-alpha)
- [Supabase.js docs](https://github.com/supabase/supabase-js)
- [Wrangler CLI docs](https://developers.cloudflare.com/workers/wrangler/commands/)
- [KV Storage docs](https://developers.cloudflare.com/workers/runtime-apis/kv/)

Automatically revalidate KV Store cache on change with Database Webhooks in Supabase

Cache Busting or Invalidation, is the process of clearing stale data from a cache. After the first request, the response data from Supabase is cached in our KV store, and never fetched again. This means if data in the database changes, our KV store will never be updated.

In this lesson, we create a `revalidate` route, which fetches fresh data from Supabase and updates the cache.

## Code Snippets

**Revalidate cache for articles route**

```javascript
router.get(
  "/revalidate",
  async (request, { SUPABASE_URL, SUPABASE_ANON_KEY, ARTICLES }) => {
    const supabase = createClient(SUPABASE_URL, SUPABASE_ANON_KEY);
    const { data: articles } = await supabase.from("articles").select("*");
    await writeTo(ARTICLES, "/articles", articles);
    return json({ received: true });
  }
);
```

**Run wrangler development server**

```bash
npx wrangler dev
```

## Resources

- [Supabase.js docs](https://github.com/supabase/supabase-js)
- [Wrangler CLI docs](https://developers.cloudflare.com/workers/wrangler/commands/)
- [KV Storage docs](https://developers.cloudflare.com/workers/runtime-apis/kv/)

Cache-busting with Cloudflare KV stores and Supabase

KV Storage is a read-optimized, eventually consistent cache that can be accessed from Cloudflare Workers. The interface for reading and writing is similar to `localStorage` in the browser.

In this lesson, we create a `read-kv` route for reading from the KV store. This automatically gets passed any KV stores that have been bound to the Cloudflare Worker. Initially, the store is empty, so returns the value `null`.

Additionally, we create a `write-kv` route to populate the store with test data.

To reduce the overhead of stringifying and parsing JSON objects, we create two helper functions to easily read and write values to any KV store.

Lastly, we publish a production version of our Cloudflare Worker, to confirm that writing these value locally - to our preview store - does not affect our production store.

## Code Snippets

**readFrom helper function**

```javascript
export const readFrom = async (cache, path) => {
  const data = await cache.get(path);
  return JSON.parse(data);
};
```

**writeTo helper function**

```javascript
export const writeTo = async (cache, path, data) => {
  await cache.put(path, JSON.stringify(data));
};
```

**Read KV route**

```javascript
router.get("/read-kv", async (request, { ARTICLES }) => {
  const articles = await readFrom(ARTICLES, "/articles");
  return json(articles);
});
```

**Write KV route**

```javascript
router.get("/write-kv", async (request, { ARTICLES }) => {
  const articles = [{ title: "test3" }, { title: "test4" }];
  await writeTo(ARTICLES, "/articles", articles);

  return json(articles);
});
```

**Publish Cloudflare Worker**

```bash
npx wrangler publish
```

**Run wrangler development server**

```bash
npx wrangler dev
```

## Resources

- [Wrangler CLI docs](https://developers.cloudflare.com/workers/wrangler/commands/)
- [KV Storage docs](https://developers.cloudflare.com/workers/runtime-apis/kv/)

Read and write KV Storage cache from Cloudflare Worker

Supabase is an open source Firebase alternative. A service to:

- Listen to database changes.
- Query your tables, including filtering, pagination, and deeply nested relationships (like GraphQL).
- Create, update, and delete rows.
- Manage your users and their permissions.
- Interact with your database using a simple UI.

Supabase is not a 1-to-1 mapping of Firebase. Most notably, Supabase uses Postgres rather than a NoSQL store. 

supabase

Itty Router is a tiny router package that has a similar API to Express. In this video, we install Itty Router and the Extras packages.

Additionally, we refactor our existing worker to use the router, and declare a dynamic route to fetch a single article.

To handle paths and articles that don't exist, we add a catch all route that returns a 404 status.

Lastly, we use the `json` helper to automatically stringify JSON objects and set the `Content-Type` header for `application/json`.

## Code Snippets

**Install Itty Router and extras**

```bash
npm i itty-router itty-router-extras
```

**Run wrangler development server**

```bash
npx wrangler dev
```

**GET route for all articles**

```javascript
router.get(
  "/articles",
  async (request, { SUPABASE_URL, SUPABASE_ANON_KEY }) => {
    const supabase = createClient(SUPABASE_URL, SUPABASE_ANON_KEY);

    const { data } = await supabase.from("articles").select("*");
    return json(data);
  }
);
```

**GET route for one article**

```javascript
router.get(
  "/articles/:id",
  async (request, { SUPABASE_URL, SUPABASE_ANON_KEY }) => {
    const supabase = createClient(SUPABASE_URL, SUPABASE_ANON_KEY);
    const { id } = request.params;

    const { data } = await supabase
      .from("articles")
      .select("*")
      .match({ id })
      .single();

    if (!data) {
      return status(404, "Not found");
    }

    return json(data);
  }
);
```

**Catch all route for 404**

```javascript
router.all("*", () => status(404, "Not found"));
```

## Resources

- [Itty Router repo](https://github.com/kwhitley/itty-router)
- [Itty Router Extras repo](https://github.com/kwhitley/itty-router-extras)

Proxy Supabase requests with Cloudflare Workers and Itty Router

Supabase JS is an NPM package which provides a simple interface from JavaScript to our Supabase project. It allows us to query and mutate data using its Object Relational Mapping (ORM) syntax, and subscribe to realtime events.

In this video, we install the Supabase JS package and create a new client using our project's URL and Anon Key. These can be found in the Supabase dashboard for our project, under `Settings > API`.

We store these values as secrets in our Cloudflare account, and use them to instantiate a new Supabase client.

Additionally, we write a query to select all of our articles from our Supabase instance, and send them back as the response from our Cloudflare Worker.

In order to send a JSON response, we first stringify the object we get back from Supabase, and then set a `Content-Type` header to notify the browser that this will be a type of `application/json`.

## Code Snippets

**Install Supabase JS**

```bash
npm i @supabase/supabase-js
```

**Create a Cloudflare secret**

```bash
npx wrangler secret put NAME
```

**Add a secret for SUPABASE_URL**

```bash
npx wrangler secret put SUPABASE_URL
```

**Run wrangler development server**

```bash
npx wrangler dev
```

**Add a secret for SUPABASE_ANON_KEY**

```bash
npx wrangler secret put SUPABASE_ANON_KEY
```

**Query data from Supabase**

```javascript
const { data } = await supabase.from("articles").select("*");
```

**Send JSON response**

```javascript
return new Response(JSON.stringify(data), {
  headers: {
    "Content-Type": "application/json",
  },
});
```

## Resources

- [Selecting data with Supabase JS](https://supabase.com/docs/reference/javascript/select)
- [Introducing Secrets and Environment Variables to Cloudflare Workers](https://blog.cloudflare.com/workers-secrets-environment/)
- [Cloudflare docs for sending JSON responses](https://developers.cloudflare.com/workers/examples/return-json/)

Query Supabase from Cloudflare Worker

Cloudflare Workers are serverless functions that run at the Edge! 🔪

The Wrangler CLI is a tool for interfacing with your Cloudflare account, and creating or publishing your Workers.

In this video, we use the Wrangler CLI to log in to our Cloudflare account and authorize the `wrangler` command to make changes on our behalf.

Additionally, we create a worker and host it locally with the `npx wrangler dev` command. We then demonstrate making a GET request to our Cloudflare Worker from the browser, and confirm it is sending the correct response.

By modifying the response locally, we can see that changes are reflected any time we refresh the browser.

## Code Snippets

**Login to Cloudflare with Wrangler CLI**

```bash
npx wrangler login
```

**Create Cloudflare Worker**

```bash
npx wrangler init supabase-at-the-edge
```

**Run wrangler development server**

```bash
npx wrangler dev
```

## Resources

- [Cloudflare Wrangler docs](https://developers.cloudflare.com/workers/wrangler/get-started/)

Create a Cloudflare Worker with the Wrangler CLI

Let's implement the delete shop functionality to the buttons that we've added to the shop manage page. Fauna allows you to define custom resolvers to run multiple database operations so you don't have to make multiple trips to the database.

We'll define a new mutation in our GraphQL schema that will take in a shop id and sets the `@resolver` directive which will tell Fauna to create a custom resolver that we can define within Fauna.

In our Fauna database, you'll see that we have a new user generated function created. This function accepts Fauna Query Language which is the native api for interacting with Fauna. We'll perform a multi-step operation by deleting the shop and then all of the products associated with that shop.

You can [read more about Fauna Query Language here and take the getting started guide to get a feel for the language](https://docs.fauna.com/fauna/current/api/fql/).

Run Multiple DB Operations with Custom Resolvers and Fauna Query Language

When a vendor manages their shops, they might want to click into the shop and mange the products associated with that shop. Let's create a page for that.

I'll create a details route that will host this information. On this page we will write a `GetShop` query that will take in the shop id that is pulled from the route. Similar to the shops page, we will iterate over the products on the shop and add buttons to manage individual products.

Create Shop Details View that uses GraphQL to Query Fauna by Shop Id

If you have been poking around the queries at this point in the course, you might notice that when you query for a product, you can get all the associated shop information. This is because we defined the `@relation` on the `Shop` property within `Product`. This allowed Fauna to create an index behind the scenes to easily fetch this data. 

We haven't done the same for the `Product` property on the `Shop` so we can't access the data. When we add a new `@relation` to `Product` we can then grab all the product information from shop queries that we like. 

This is based on the [Document Model Relationship in Fauna that you can read more about here](https://docs.fauna.com/fauna/current/learn/tutorials/graphql/relationships).

Add a Product Relation to Shop to understand Document Model Relationship in Fauna

This lesson demonstrates how to utilize DeepSeek's R1 model to access its reasoning capabilities and then seamlessly transition to other models for subsequent tasks. By extracting the reasoning output from DeepSeek R1, you can leverage its powerful reasoning while gaining the speed, cost efficiency, and configuration flexibility of alternative models. This two-phase approach begins with DeepSeek’s new R1 model, praised for its open-source nature and value. You then gain the adaptability to switch to other models depending on your needs or existing workflows. This lesson provides a step-by-step walkthrough of this process, along with demo code to help you implement this technique yourself.

Combine DeepSeek R1 Reasoning with GPT 3.5 Turbo for the Cheapest, Fastest, and Best AI

While much focus in AI has been on optimizing input and generating concise outputs, a new model in Google's AI Studio dramatically expands output possibilities with a capacity of 65,000 tokens. This represents a significant shift, opening doors to new application scenarios, especially for developers and creators. This lesson explores a practical and immediately useful scenario: leveraging this large output capability within Google's AI Studio to generate extensive sample datasets for your projects. Whether you are developing a new application, building a resource, or require robust datasets for testing and validation, this model offers a powerful solution. We'll demonstrate how to move beyond the constraints of short responses and utilize the full potential of large output models to generate the data volume and variety you need, enhancing your development workflow through Google's AI Studio.

AI Studio's New 65k Output Opens Exciting Possibilities

This lesson explores how to use Cursor to generate and maintain English specifications as a form of living documentation. Learn how these automatically created documents can serve as a consistent and up-to-date representation of your code's behavior. The video highlights how reviewing and refining these English specifications can help ensure that code modifications align with the intended purpose and that documentation accurately reflects the current codebase, promoting better long-term code maintainability and clarity.

Here's the "english-spec.md" used in the video:

```markdown
> You are an AI specialized in analyzing source code and producing structured, plain-English representations of its functionality.  
>  
> **Your Task**:  
> 1. Read and interpret the code I provide.  
> 2. Produce a technical, English-only summary of what the code does.  
> 3. Use headings and bullet points to clearly outline the roles of each function, class, variable, or important logic block.  
> 4. **Do not include any actual code** in your final output. Instead, translate the code’s content into an English explanation (like a specification or documentation).  
> 5. Keep the explanation concise but thorough. Focus on explaining the purpose, process, and relationships between functions or modules in the code.  

**User Prompt to the AI**:

> **Instruction**: Convert the following code into a single file of pure English text that outlines exactly what each part of the code does. Use bullet points, headings, and descriptive language. Do not show any code—only explain how the code behaves. This is intended to serve as a human-readable spec or “translation” of the code.  
>  
> **Code**:  
> ~~~  
> [PASTE YOUR CODE HERE]  
> ~~~  

**Example Input/Output #1**:

- **Example Code (Input):**
  ~~~js
  // example1.js
  const greetUser = (userName) => {
    const greeting = `Hello, ${userName}!`;
    console.log(greeting);
  };

  greetUser("Alice");
  ~~~

- **Example Explanation (Output)**:
  ~~~
  # Overview
  - This file defines a single function which greets a user by name and prints a message to the console.
  
  # Function: greetUser
  - Accepts one parameter (`userName`).
  - Constructs a greeting string that combines static text with the `userName`.
  - Outputs the greeting using `console.log`.

  # Behavior Flow
  - The function is called with the argument "Alice".
  - Upon invocation, "Hello, Alice!" is logged to the console.

  # Key Points
  - Shows how to create a greeting with a dynamic name.
  - Demonstrates basic string interpolation and console logging.
  ~~~

**Example Input/Output #2**:

- **Example Code (Input):**
  ~~~py
  # example2.py
  class Calculator:
      def __init__(self, initial=0):
          self.value = initial
      
      def add(self, amount):
          self.value += amount
      
      def subtract(self, amount):
          self.value -= amount
      
      def get_value(self):
          return self.value

  calc = Calculator(10)
  calc.add(5)
  calc.subtract(3)
  result = calc.get_value()
  print(result)
  ~~~

- **Example Explanation (Output):**
  ~~~
  # Overview
  - This file defines a `Calculator` class that keeps track of an internal numerical value and provides methods to modify it.
  
  # Class: Calculator
  - **Constructor** (`__init__`):
    - Accepts an optional initial value (default is 0).
    - Stores the initial value in a `value` property.
  - **Method: add**:
    - Takes an `amount` and increments the internal `value` by that amount.
  - **Method: subtract**:
    - Takes an `amount` and decrements the internal `value` by that amount.
  - **Method: get_value**:
    - Returns the current internal `value`.
  
  # Behavior Flow
  - An instance of `Calculator` is created with an initial value of 10.
  - The `add` method is called with 5, raising the value to 15.
  - The `subtract` method is called with 3, lowering the value to 12.
  - The `get_value` method is called to retrieve the current value (12).
  - The result (12) is printed to the console.

  # Key Points
  - Demonstrates basic OOP with a class that manages state.
  - Showcases constructor, addition, subtraction, and retrieval of a stored value.
  ~~~  

---

## Final Prompt

Below is a full, combined version of the prompt you can provide to an AI to guide it in generating the English-only, bullet-pointed explanation. Feel free to modify or adapt this as needed:

~~~
You are an AI specialized in analyzing source code and producing structured, plain-English representations of its functionality.

Your Task:
1. Read and interpret the code I provide.
2. Produce a technical, English-only summary of what the code does.
3. Use headings (e.g. # Overview, # Functions, # Classes, # Behavior Flow) and bullet points.
4. Do not include any code in your final output—only explain how the code behaves.
5. Keep it concise but thorough, focusing on purpose, process, and relationships between functions or modules.

Below are some examples of how code might be transformed into an English-only explanation:

Example Input:
-------------------
~~~js
// example1.js
const greetUser = (userName) => {
  const greeting = `Hello, ${userName}!`;
  console.log(greeting);
};

greetUser("Alice");
~~~
Example Output:
-------------------
# Overview
- This file defines a single function which greets a user by name and prints a message to the console.

# Function: greetUser
- Accepts one parameter (`userName`).
- Constructs a greeting string that combines static text with the `userName`.
- Outputs the greeting using `console.log`.

# Behavior Flow
- The function is called with the argument "Alice".
- Upon invocation, "Hello, Alice!" is logged to the console.

# Key Points
- Shows how to create a greeting with a dynamic name.
- Demonstrates basic string interpolation and console logging.


Example Input:
-------------------
~~~py
# example2.py
class Calculator:
    def __init__(self, initial=0):
        self.value = initial
    
    def add(self, amount):
        self.value += amount
    
    def subtract(self, amount):
        self.value -= amount
    
    def get_value(self):
        return self.value

calc = Calculator(10)
calc.add(5)
calc.subtract(3)
result = calc.get_value()
print(result)
~~~
Example Output:
-------------------
# Overview
- This file defines a `Calculator` class that keeps track of an internal numerical value and provides methods to modify it.

# Class: Calculator
- **Constructor** (`__init__`):
  - Accepts an optional initial value (default is 0).
  - Stores the initial value in a `value` property.
- **Method: add**:
  - Takes an `amount` and increments the internal `value` by that amount.
- **Method: subtract**:
  - Takes an `amount` and decrements the internal `value` by that amount.
- **Method: get_value**:
  - Returns the current internal `value`.

# Behavior Flow
- An instance of `Calculator` is created with an initial value of 10.
- The `add` method is called with 5, raising the value to 15.
- The `subtract` method is called with 3, lowering the value to 12.
- The `get_value` method is called to retrieve the current value (12).
- The result (12) is printed to the console.

# Key Points
- Demonstrates basic OOP with a class that manages state.
- Showcases constructor, addition, subtraction, and retrieval of a stored value.

---

**Now**: Please apply the same conversion process to the following code. Convert it into a plain-English document that describes what the code does in detail, using bullet points and headings. Create the document next to the original with the filename as `${fileName}.md`. Do not include any source code in the output—only the explanation:

[INSERT YOUR CODE HERE]
```

Programming in English with Cursor

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.