Learn Android, Cloudflare and D3

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

D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG and CSS. D3’s emphasis on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework, combining powerful visualization components and a data-driven approach to DOM manipulation.

In this lesson, we'll clean up some of our placeholder elements. We'll then look at D3's ordinal scale to create a color scale that maps our regions to colors. We'll apply the colors based on region to our data points via the SVG `g`(group) element to apply the color to both our bars and their corresponding inline labels.

Create a Color Scale with D3's scaleOrdinal

Looking at our bars against each other with the x-axis as a guide gives us some relative information, but doesn't deliver the most precise values. In this lesson, we'll add additional labels to show the exact values of each data point in the context of the individual bars.

Clarify Data in D3 with Additional Labels and Rounded Domain Values

In this lesson, we'll see how we can add labels down the y-axis using our band scale. We'll see how to render the labels in React and how we can precisely position the labels based on values calculated by D3 and our scales.

Render a Y Axis for a Bar Chart with D3 and React

In this lesson, we'll take advantage of our `xScale` and the bottom margin to create an axis to start giving our data meaning. We'll access `ticks` from the scale and then use React to render and position the SVG `rect` and `text` elements to create the x-axis for our chart.

Render an X Axis for a Bar Chart with D3 and React

When applying scales to data elements in D3, it's common to combine the scale with an accessor function in order to apply the scale to the appropriate key on a data object. In this lesson, we'll create scaled accessors to refactor and tighten up some existing code.

Create Scaled Accessor Functions from D3 Scales

A band scale allows us to map a discrete set of values to a continuous range. In this lesson, we'll use a band scale in D3 to take the distinct "region" keys in our sample data and map those to evenly spaced and sized elements on our y-axis. We'll use this band scale to show a bar in the bar chart for each region, setting their size and position based on this scale.

Layout SVG Elements on a Y Axis with a D3 Band Scale in React

Drawing data accurately requires a consistent and precise mechanism for mapping data values to pixels on the screen. We can accomplish this mapping in D3 using scales. There are various scales available in D3, depending on your needs. In this lesson we'll using a linear scale to map a data domain of continuous values to continuous range of pixel values. We'll render a new SVG element, setting its width from our scale.

Set an SVG Element's Width Based on a D3 Linear Scale in React

When you want to add data visualizations to your web app, D3 is pretty much the accepted standard.

In this course, we'll start from scratch and build out a bar chart for some sample data using D3's utilities to wrangle the data and then we'll handle the rendering with React.

We'll start by laying out the SVG element that will hold everything, then we'll move on to creating scales that will give us the information we need to render our data as well as axes to give our data some meaning.

Along the way, we'll cover accessor functions, linear scales, band scales and finally, we'll use an ordinal scale to map data objects to colors.

This course certainly won't cover all of D3, but we'll cover enough of the fundamentals to give you a solid jumping off point for future D3 and React data visualization work.

Introduction to Create a Bar Chart with React and D3

We've seen how to return easy and a little complex data from our KV, but how about in a real-world application? We are going to remove the code we have created and set up a simple HTML application that will return our data based on an API which we will create. 

Update Cloudflare Workers KVs Data from an API Route

Behind the scenes, I've updated the HTML UI and added in some helper JS functions to allow us to render new to-do's. We are going to start off by adding in the UI for a user to be able to input their own to-dos. Then we are going to work on creating a backend to store that data and retrieve it using our KV key of `"data"`. 

Filter Data with Cloudflare Workers KV Keys

Our application works but there is one major flaw. Everyone sees the same to-do's. To make to-do's user-specific, we are going to link users' data to their IP addresses so people only see to-dos based on their IP address. 

Identify Users in a Cloudflare Worker by IP to Serve User-specific Data

Now that we have our Workers project up and running, we need to create a KV namespace. A KV namespace is a key-value database that is replicated to Cloudflare's edge. Using the terminal, we will create our namespace and add it to our wrangler.toml file. 

Create a Cloudflare Workers KV Namespace

Creating a new workers project using Wrangler is pretty easy. We first will install and double-check that we have Wrangler installed. Then we will connect Wrangler to our cloudflare project. Lastly, using the terminal, we can easily create our new Wrangler project. 

Create a New Cloudflare Workers Project with Wrangler

We can store and retrieve our data from our KV but not the status of whether or not it's checked off. To do that, we create a function called `completeTodo` that will target the checkbox, find the todo based on its id, flips the boolean from false to true and saves that data, and then updates our to-dos. 

Edit Stored Data using the Cloudflare KV Key

We've learned how to write and receive data from our database, but we've only done it with fairly simple values. Now we are going to see how to do the same thing but with complex key values. There are two ways of doing this. One is the default route where we just return the value back from our KV. The second is where one route takes in a name parameter and sets it as the value for a key. The second path is the one we are going to look at in this lesson. 

Set Complex Key Values in a Cloudflare Workers KV Namespace

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.