Learn Graphcms, Mac and Parti-corgi

In this course, Colby is going to do a deep dive into creating a full store for your products. You'll learn how to set up and use GraphCMS as your content management system. You'll learn about the Cloudinary UI extension and how to use it to host your images. You'll learn about Snipcart and using the useSnipcart hook to handle the cart state and the checkout. You'll learn how to implement Stripe as your payment gateway. And lastly, you'll learn how to deploy a Next js app to vercel from github with environment variables.

snipcart

Ecommerce Product Management & Storefront with GraphCMS, Snipcart, & Next.js

graphcms

Displaying a list of physical locations is helpful, but showing them on a map, giving your potential customers the ability to see how close each store really is to their location is even better.

GraphCMS supports adding coordinates as a field to our data models, where we can dynamically pull those coordinates in and add them to a map.

We'll walk through how we can loop through our store locations and add each one to a new React Leaflet map. We'll also learn about some pitfalls of using React Leaflet in an app framework like Next.js and how we can solve it with dynamic imports and manual image imports.

* [Dynamic Import - nextjs.org](https://nextjs.org/docs/advanced-features/dynamic-import)
* [Leaflet Map Marker Rendering - github.com](https://github.com/Leaflet/Leaflet/issues/4968)

I also have a whole course on how to [Build Maps using React Leaflet](https://egghead.io/courses/build-maps-with-react-leaflet) if you want a further dive on the topic. 

Add All Store Locations to a React Leaflet Map with Location Data From GraphCMS

If our store supports physical locations, we want to give our customers the opportunity to easily find out those locations so they can visit us in person. That means, we need to show them somewhere on our site.

We can take advantage of GraphCMS custom Schema Models to create a new document for each store.

We'll walk through setting up a new model for our stores and how we can query them in our app to list out all of our locations.

List Store Locations on a Page Using a Store GraphCMS Schema and Data

We are going to create a schema as we did in lesson 2, but this one is going to be for our Categories at the bottom of the home page. This will be slightly different because we are going to add a Reference field. This way we can create a reference between our categories and our products. 

Then over in the content tab, we will add our existing products to an Apparel category, an Accessory category, and a Featured category. 

Create a Product Category Graphcms Schema to Manage Product Groupings

We want to set up a schema for the most important part of our store, our products. We set up all the fields that we'll need, including the name, the image, and the price.

We also set up the description, which we'll use later when we're setting up individual product pages. Then we will see how we can easily construct and query for our data inside of GraphCMS and append that products query to the end of our existing query.

There, we will pass along that data, just like our existing home page data to our app, where we dynamically render our first four products inside of GraphCMS.

Manage Product Details with a Custom GraphCMS Schema Model

To manage my product data, I'm going to use [graphCMS](https://graphcms.com/) which is a content management system that's going to give us a content API backed by GraphQL. You'll need to login, or register an account, with just the free version for this course. I will also run you through the initial setup for graphCMS. 

Use GraphCMS Schema Models to Manage Page Content

Set up GraphCMS to Manage Data with a GraphQL API

Switching project contexts causes needless friction and opportunities for distraction as we try to pull up related tabs and applications. I learned a trick from [David Sparks](https://www.macsparky.com/) on how to use the Mac automation software [Keyboard Maestro](https://www.keyboardmaestro.com/main/) to create palettes of shortcuts grouped by context. This has been an awesome boost to my productivity!

Seamlessly Switch Project Contexts with Keyboard Maestro

PostgreSQL is a powerful, open source object-relational database system.

postgres

YugabyteDB is an open source, high-performance distributed SQL database for powering global, internet-scale applications.YugabyteDB is also a cloud-native database, so it can be deployed across both public and private clouds, including  Kubernetes environments. In regards to serving as a backend for microservices, YugabyteDB brings together three must-haves: a PostgreSQL-compatible SQL API, low-latency read performance, and globally distributed write scalability. YugabyteDB with its global data distribution brings data close to users for multi-region and multi-cloud deployments. 

In this lesson we will see how we can create YugabyteDB cluster locally on our Mac for dev environment and prototyping 

Set up YugabyteDB Distributed SQL Cluster Locally on MacOS

An inclusive community of content creators helping each other grow.

parti-corgi

[Streamlabs](https://streamlabs.com/) has two major halves: Streamlabs OBS, which is an OBS competitor, and Streamlabs-the-SaaS, which includes tools like chatbots, polls, giveaways, and loyalty rewards.

WTF is Streamlabs?

Many streamers use colorful banners on their stream pages that reflect their branding. Here's how you can create and use them.

* [Panel Maker](https://nerdordie.com/resources/free-resources/customizable-twitch-panels/)

Creating Panel Banners on Twitch

How to Livestream Code and Design on Twitch

https://www.elgato.com/en/gaming/stream-deck

What is a Streamdeck?

It's likely that you will have many streams where no one is there, or people are there and they aren't telling you they are. Does it matter?

What if nobody comes to watch my livestream?

While there are many useful tools and toys to pull together for a livestream, the truth is you don't need any of them to start.

What gear do I need to livestream?

We cover how to get your stream key, how to use that stream key to test bandwidth and CPU usage settings, and go live on Twitch for the first time.

Going live on Twitch for the first time and testing bandwidth

We introduce OBS and compare it to SLOBS, download and set up our first scene including desktop capture and audio tips.

Introduction to OBS and creating your first scene

We cover some of the twitch interface and open a twitch account

Starting a Twitch account to livestream

A short look at what I do before I change from my "Starting Soon" scene to my "live scene" and a brief overview of what to except from the collection

Introduction to Livestreaming

In this video, we'll learn how to modify the default screenshot keyboard shortcut on macOS so that it copies our screenshot to our clipboard. This comes in handy if you share screenshots often.

Change Default Screenshot Key to Copy to Clipboard

Joe Previte is an active egghead instructor and blogger. This is a collection of screencast versions of selected blog posts. You can find his blog here: [joeprevite.com](https://joeprevite.com/)

Joe's Blog Posts as Screencasts

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.