Learn Cycle, Flux-architecture and Leaflet

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

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

So far, we haven't noticed any issues with map state, but that doesn't mean there aren't any issues!

**[Click here for exercise instructions](https://github.com/colbyfayock/launchtime-workshop/tree/master/lessons/05%20-%20Managing%20Leaflet%20State%20in%20a%20React%20App%20with%20Hooks#4-adding-markers-in-a-single-source-to-avoid-mismanaged-state)**

Manage Stale Map Data in React Leaflet 

Now that we have finished up with our map app, let's do a little cleaning up! Here we are going to go through our Location Event Handler and clean it up for when the page unmounts.  

**[Click here for exercise instructions](https://github.com/colbyfayock/launchtime-workshop/tree/master/lessons/10%20-%20Use%20Leaflet's%20Geolocation%20API%20to%20Find%20Locations%20Near%20you#1-clean-up-location-event-handler-resources-when-page-unmounts)**

Clean up Location Event handler Resources when Page Unmounts in React useEffect

Now that we can pinpoint our location, we noticed that it's not exactly the most accurate indication of where we're at. To help, Leaflet provides an accuracy indicator in meters that we can use to show on the map the margin of error for that location.

**[Click here for exercise instructions](https://github.com/colbyfayock/launchtime-workshop/tree/master/lessons/10%20-%20Use%20Leaflet's%20Geolocation%20API%20to%20Find%20Locations%20Near%20you#4-add-a-circle-to-the-map-designating-the-accuracy-of-the-browsers-location)**

Add a React Leaflet Circle to the Map Designing the Accuracy of the Browser's Location

Most of us are able to identify where we're located on the map, but it can be hard to visualize that in context to other locations. To help, we can add a marker to the map to show our location.

To get started, we'll add a marker to a specific location to see how it relates to the restaurants that are on our map.

**[Click here for exercise instructions](https://github.com/colbyfayock/launchtime-workshop/tree/master/lessons/10%20-%20Use%20Leaflet's%20Geolocation%20API%20to%20Find%20Locations%20Near%20you#1-add-a-marker-to-a-location-when-clicking-a-button)**

Add a React Leaflet Marker to a Location when Clicking a Button

Rather than setting a location manually, web browsers provide a way that we can request access to someone's location. This lets us personalize features and particularly in our use case, find where the person is on the map to see what's close to them.

**[Click here for exercise instructions](https://github.com/colbyfayock/launchtime-workshop/tree/master/lessons/10%20-%20Use%20Leaflet's%20Geolocation%20API%20to%20Find%20Locations%20Near%20you#2-create-a-button-that-finds-your-location-and-navigates-the-map-to-that-location)**

Add a Button that Finds your Location and Navigates the Map to the Location with Leaflet

While we were able to find our location, Leaflet's geolocation features don't set a marker by default. What they do provide is event handlers that allow us to know when the location is found.

**[Click here for exercise instructions](https://github.com/colbyfayock/launchtime-workshop/tree/master/lessons/10%20-%20Use%20Leaflet's%20Geolocation%20API%20to%20Find%20Locations%20Near%20you#3-use-the-browsers-location-to-add-a-marker-to-the-map)**

Use the Browser's Location to Add a React Leaflet Marker to the Map

As we just noticed, we don't get the shadows to our markers by default. While we can add a custom one, we can use the default shadows which will work just as well with our custom markers.

**[Click here for exercise instructions](https://github.com/colbyfayock/launchtime-workshop/tree/master/lessons/09%20-%20Customize%20Restaurant%20Location%20Markers%20with%20Custom%20Images#3-add-the-default-shadows-back-to-our-markers)**

Import and Add the Default React Leaflet Shadows back to our Markers

Not only can we use the Leaflet Icon instance to add a custom image, we can create completely custom HTML! This opens the door to a lot of possibilities that you can't do with only an image like using CSS to add effects.

For this extra credit, use a different Leaflet icon type called DivIcon to transform our markers into custom HTML, which you can then style with CSS.

**[Click here for exercise instructions](https://github.com/colbyfayock/launchtime-workshop/tree/master/lessons/09%20-%20Customize%20Restaurant%20Location%20Markers%20with%20Custom%20Images#1-replace-the-marker-with-custom-html-and-style-with-css)**

Replace the Marker with Custom HTML and Style with CSS

In lesson 10, we are going to be focusing on how to add a marker to the map on button click to designate a location, create a button that finds and navigates to your location, add a marker to the map showing your location and add a radius to the map showing the accuracy of your location

**[Click here for exercise instructions](https://github.com/colbyfayock/launchtime-workshop/tree/master/lessons/10%20-%20Use%20Leaflet's%20Geolocation%20API%20to%20Find%20Locations%20Near%20you#-lesson-10-use-leaflets-geolocation-api-to-find-locations-near-you)**

Section 10 - Use Leaflet's Geolocation API to Find Locations Near You

Before we create a completely custom icon, we need to set up our GeoJSON configuration to allow us to manually create markers in the first place. We'll start by recreating our current markers.

**[Click here for exercise instructions](https://github.com/colbyfayock/launchtime-workshop/tree/master/lessons/09%20-%20Customize%20Restaurant%20Location%20Markers%20with%20Custom%20Images#1-recreate-restaurant-markers-with-geojson-configuration-option)**

Recreate React Leaflet Markers with the GeoJSON pointToLayer Configuration Set 

Now's the fun part! With our configuration, we can now substitute the default markers with our own custom ones.

For this lesson, I've provided you with a custom image that you can use:

`src/assets/images/utensils-marker.png`

**[Click here for exercise instructions](https://github.com/colbyfayock/launchtime-workshop/tree/master/lessons/09%20-%20Customize%20Restaurant%20Location%20Markers%20with%20Custom%20Images#2-replace-the-default-location-markers-with-a-custom-image)**

Replace the Default React Leaflet Location Markers with a Custom Image

Just like we changed the styles of our markers before, we can change the colors of shapes.

**[Click here for exercise instructions](https://github.com/colbyfayock/launchtime-workshop/tree/master/lessons/08%20-%20Add%20Restaurant%20Delivery%20Zones%20to%20Map%20with%20Shaded%20Regions#1-change-the-color-of-the-delivery-zone)**

Change the Color of the React Leaflet Delivery Zone

In a lot of cases, not every marker is the same. Sometimes we have a restaurant, but other times we might have a museum, so we should be able to visually represent that type of location to make our map easier to use.

We'll learn how to create custom markers by using a new configuration option with our Leaflet GeoJSON instance.

**[Click here for exercise instructions](https://github.com/colbyfayock/launchtime-workshop/tree/master/lessons/09%20-%20Customize%20Restaurant%20Location%20Markers%20with%20Custom%20Images)**

Section 09 - Customize Restaurant Location Markers with Custom Images

We were able to show all of our delivery zones, but it's a little overwhelming and it can be hard to determine which zone is associated with which marker.

**[Click here for exercise instructions](https://github.com/colbyfayock/launchtime-workshop/tree/master/lessons/08%20-%20Add%20Restaurant%20Delivery%20Zones%20to%20Map%20with%20Shaded%20Regions#3-only-showing-the-delivery-radius-on-marker-hover)**

Only Showing the Delivery Radius in React Leaflet on Marker Hover using DOM Events

In this Group, it's all about crafting the experience around the map. Here are are going to be adding restaurant delivery zones to the map with shaded regions, customizing restaurant location markers with custom images, and learn how to use Leaflet's Geolocation API to find locations near you. 

It's all about adding features that will make using our map easier. Let's dive in! 

Group 3 - Customizing Your Map

There's nothing like finding a restaurant, seeing they deliver, and getting excited about what you're going to order from their menu only to find out you're not in their delivery zone. It's not fun, but we can give our website visitors a better way to search by showing them each restaurant's delivery zone right on the map!

**[Click here for exercise instructions](https://github.com/colbyfayock/launchtime-workshop/tree/master/lessons/08%20-%20Add%20Restaurant%20Delivery%20Zones%20to%20Map%20with%20Shaded%20Regions#-lesson-08-add-restaurant-delivery-zones-to-map-with-shaded-regions)**

Section 08 - Add Restaurant Delivery Zones to Map with Shaded Regions

For this step, we're going back to our restaurant locations GeoJSON file. We want to be able to provide a value for how far away from each restaurant they're willing to deliver.

**[Click here for exercise instructions](https://github.com/colbyfayock/launchtime-workshop/tree/master/lessons/08%20-%20Add%20Restaurant%20Delivery%20Zones%20to%20Map%20with%20Shaded%20Regions#1-adding-a-delivery-radius-to-our-restaurant-data)**

Add a Delivery Radius to our Restaurant GeoJSON Data

Now that we're setting our delivery radius, let's use it! We'll use that value to create a new Circle shape on the map.

**[Click here for exercise instructions](https://github.com/colbyfayock/launchtime-workshop/tree/master/lessons/08%20-%20Add%20Restaurant%20Delivery%20Zones%20to%20Map%20with%20Shaded%20Regions#2-using-the-delivery-radius-to-add-a-shaded-circle-to-the-map)**

Add a Shaded React Leaflet Circle to the Map Based on GeoJSON Data

We have our content! But the default styles aren't the greatest. It's a little hard to read and takes a little more room than it should -- but we can fix that!

**[Click here for exercise instructions](https://github.com/colbyfayock/launchtime-workshop/tree/master/lessons/07%20-%20Add%20Restaurant%20Info%20to%20Your%20GeoJSON%20Document%20and%20Display%20It%20in%20a%20Tooltip%20on%20the%20Map#4-update-the-styles-of-our-popups)**

Update the Styles of React Leaflet Popups

Hardcoding your script to search for only one type of file (like `.md`) is a major limitation. This lesson shows you how to make your script smarter by teaching it to infer the desired file type directly from the user's natural language prompt.

You'll implement a two-stage AI process. The first stage analyzes the user's request to determine the file extension they're interested in (e.g., `md`, `txt`). The second stage then uses that file type to dynamically search the file system, find the relevant files, and execute the command.

**Workflow demonstrated in this lesson:**

* Add a preliminary `generateObject` call to extract a `fileType` from the user's initial prompt.
* Define a list of supported `fileTypes` (e.g., `['md', 'txt']`) and use a detailed prompt to guide the AI, instructing it to choose from the list or use a default.
* Dynamically construct the `globby` pattern using the inferred `fileType` (e.g., `*.$[fileType]`).
* Implement error handling to inform the user if they request an unsupported file type or if no files of the specified type are found.
* Demonstrate how the script defaults to searching for Markdown files but can correctly search for other types when specified (e.g., "summarize my profile from my pdf").

This technique adds a significant layer of intelligence to your script, allowing it to adapt its behavior based on the user's intent and making it a far more flexible and intuitive command-line tool.

John Lindquist

Dynamically Inferring File Types from User Prompts in Local AI Scripts

Hardcoding command logic and prompts directly into your script makes it rigid and difficult to extend. This lesson demonstrates a more flexible and powerful pattern: defining your script's commands as individual Markdown files in a dedicated directory.

This approach allows you to easily add, remove, or modify commands without touching the core script logic. You'll learn how to dynamically load these command files, present them as options to the AI, and inject the corresponding instructions into your prompt, creating a truly modular and extensible tool.

**Workflow demonstrated in this lesson:**

* Create a `commands` directory to house your command definitions (e.g., `summarize.md`, `translate.md`).
* In each file, write the specific prompt or instructions for that command.
* Use `globby` to discover all available command files at runtime.
* Create a `commandMap` that maps the filename (e.g., "summarize") to its file content.
* Update the Zod schema to use an `enum` of the dynamically discovered command names.
* Dynamically construct the final text generation prompt by injecting the instructions from the user-selected command file.
* Add a `profile.md` file to demonstrate how the script can act on user-created content with the new file-based commands.

By the end of this lesson, your script will be architected for easy expansion, allowing you to build a robust library of custom commands simply by adding new text files.

Creating Dynamic, File-Based Commands for Local AI Scripts

AI models won't always give you the answer you expect; sometimes they'll return invalid data or "hallucinate" a response that doesn't fit your schema. This lesson teaches you how to make your local AI script more robust by implementing essential error handling for user commands.

You'll learn how to validate the AI's output against a predefined list of valid commands and gracefully catch errors when the model generates an unexpected response, ensuring your script fails predictably and provides helpful feedback to the user.

**Workflow demonstrated in this lesson:**

* Define a list of `validCommands` (e.g., `summarize`, `translate`, `review`) to constrain the AI's choices.
* Wrap the `generateObject` call in a `try...catch` block to handle cases where the AI's output doesn't match the Zod schema.
* Implement a type guard function (`isValidCommand`) to check if the command returned by the AI is in your list of valid commands.
* Provide clear error messages to the user, indicating what went wrong and what the valid commands are.
* Test the script with invalid commands (like "taco" or "delete") to confirm that the error handling works as expected.

By adding these checks, you make your script significantly more reliable and user-friendly, preventing it from crashing on bad input and guiding the user toward correct usage.

Handling Invalid Commands and AI Errors in Local Scripts

Once your script can understand a user's command and identify a target file, the next step is to perform an action. This lesson shows you how to use the Vercel AI SDK's `generateText` function to process a file's content and produce a relevant, AI-generated response.

You'll learn how to read the content from the file path you've already identified, combine it with the inferred command into a new, more detailed prompt, and then pass it back to your local AI model to generate a final text output, such as a summary or a review.

**Workflow demonstrated in this lesson:**

* Import the `generateText` function from the `ai` SDK.
* Read the file content from the `filePath` returned by `generateObject`.
* Construct a new, structured prompt that includes the original command (e.g., "summarize") and the full content of the file.
* Pass this new prompt to the `generateText` function.
* Log the resulting AI-generated text to the console.
* Run the script with commands like "Summarize our TypeScript config" or "Review my package file" to see the full, context-aware output.

This lesson completes the core cycle of your AI script: understanding a command, reading the relevant context, and generating a useful, text-based result, all on your local machine.

Generating Text with a Local AI Model Based on File Content

A command is useless without knowing what to act upon. This lesson elevates your local AI script by giving it context from your project's file system. You'll learn how to find all relevant files and empower the AI to intelligently select the correct one based on your prompt.

The core technique involves using the `globby` package to scan for files and then feeding that list into a Zod `enum`. This constrains the AI's choices to a valid set of files, allowing it to accurately determine which file you're referring to in a natural language command.

**Workflow demonstrated in this lesson:**

* Install and import the `globby` library to find files based on a pattern (e.g., all `.json` files).
* Add a `filePath` field to your Zod schema.
* Define the `filePath` as `z.enum(files)`, where `files` is the array of file paths returned by `globby`.
* Pass a natural language prompt like "Summarize our typescript config" to the script.
* Observe how the AI correctly identifies `tsconfig.json` from the list of available JSON files and returns it as structured data.

This lesson is a crucial step toward building powerful, context-aware tools that can intelligently interact with your project's files based on simple, human-readable commands.

Providing File System Context to Local AI Scripts with Globby and Zod Enums

Hardcoded prompts are great for testing, but a truly useful script needs to respond to dynamic user input. This lesson shows you how to make your local AI script interactive by capturing command-line arguments.

You'll learn how to use Node.js's built-in `parseArgs` utility to take a user's natural language command directly from the terminal and pass it to your local language model for processing.

**Workflow demonstrated in this lesson:**

* Import the `parseArgs` function from the `node:util` module.
* Configure `parseArgs` to accept positional arguments from the command line.
* Replace the static, hardcoded prompt with the dynamic input provided by the user.
* Run the script with different sentences (e.g., `"Download a file"` or `"I want to download a file"`) to demonstrate that the AI can correctly infer the core command regardless of the phrasing.

By the end of this short lesson, your script will be able to accept and process commands directly from the user, making it a flexible and reusable command-line tool.

Processing Dynamic User Input in Local AI Scripts

While command-line chats with local AI models are useful, the real power comes from scripting. This lesson walks you through the initial setup for creating powerful, local AI-driven scripts by combining Ollama, the Vercel AI SDK, and Zod.

You'll learn how to take a natural language command, like "Concatenate all of the markdown files in my Downloads folder into a single file," and use the `generateObject` function to intelligently extract structured, actionable data from it.

**Workflow demonstrated in this lesson:**

* Install and set up the `ollama-ai-provider`, `ai`, and `zod` packages.
* Create a local AI model instance using Ollama and the Gemma 3 model.
* Define a Zod schema to specify the exact data structure you want to extract (e.g., `{ command: string }`).
* Use `generateObject` to pass a natural language prompt to the local model and receive a structured JSON object that matches your schema.
* Run the script using `bun` to see the local model successfully infer the "concatenate" command from a full sentence.

This foundational lesson unlocks the ability to build sophisticated, automated workflows by translating human language into machine-readable commands, all running entirely on your local machine.

Scripting Local Language Models with Ollama and the Vercel AI SDK

### **Build Powerful, Local AI-Driven Command-Line Scripts**

Go beyond simple AI chats and unlock the true potential of local language models by learning to script them. This course will guide you step-by-step through the process of building a flexible and intelligent command-line tool that can understand natural language, interact with your file system, and automate complex tasks—all running entirely on your local machine.

Starting with a blank TypeScript file, you'll learn how to combine the power of **Ollama**, the **Vercel AI SDK**, and **Zod** to create a script that's not just functional, but smart. We'll begin by teaching the AI to parse a simple command from a sentence. From there, we'll progressively add layers of sophistication: processing dynamic user input, making the script aware of your project's files, and generating meaningful text-based output.

You'll also learn critical patterns for building robust and extensible tools. We'll cover how to handle invalid commands and unexpected AI responses gracefully. Then, you'll refactor the script to use a modular, file-based system for its commands, allowing you to easily add new functionality without touching the core logic. Finally, we'll add a layer of intelligence that allows your script to infer exactly what you mean, right down to the type of file you want it to process.

By the end of this course, you will have built a powerful, personalized AI assistant and gained the foundational skills to create sophisticated local automations for any task you can imagine.

**What You'll Learn:**

* How to script local language models using Ollama and the Vercel AI SDK.
* To parse and process dynamic user input from the command line.
* To use Zod schemas, including `enums`, to validate and structure AI model output.
* Techniques for providing file-system context to your scripts with `globby`.
* How to generate context-aware text based on file content.
* Robust error handling for invalid user commands and unexpected AI responses.
* To build a modular and extensible tool by defining commands in separate files.
* To intelligently infer user intent, such as dynamically determining file types from a prompt.

Build a local AI-powered command-line assistant from scratch. Learn to use Ollama, Vercel AI SDK & Zod to parse input, automate tasks, and run scripts offline.

This lesson demonstrates how to give Cursor direct access to your terminal output using the [Nx MCP extension](https://nx.dev/getting-started/ai-integration?utm_source=youtube&utm_medium=videos&utm_campaign=Nx-AI&utm_id=Nx-AI), enabling automatic error detection and fixing in monorepo projects.

**Workflow demonstrated in this lesson:**

- Encounter a runtime error when serving a Vite application in an Nx monorepo
- Install and configure the Nx Console extension with MCP integration
- Ask Cursor to check your terminal for errors and fix the underlying issues
- Watch as the AI accesses running task details and terminal output through Nx MCP tools
- Observe how it analyzes workspace relationships to identify incorrect imports and missing dependencies

**Key benefits:**

- Seamless Error Handling: AI gains direct access to terminal output and running task information
- Automatic Diagnosis: Accurately identify the root cause of errors without manual copying
- Context-Aware Fixes: Leverage workspace knowledge to implement proper solutions
- Reduced Context Switching: Eliminate the need to manually relay information between terminal and AI



Juri Strumpflohner

Enable Cursor to directly access the terminal to read the output and fix issues

See how Nx MCP gives AI coding assistants direct access to terminal output and running tasks, enabling automatic error detection and fixing.

This lesson demonstrates how to solve the predictability problem when using AI to generate code in monorepos by combining Cursor with Nx generators.

**Workflow demonstrated in this lesson:**

- Explore how Nx generators provide predictable scaffolding for new libraries
- Set up [Nx Console with MCP](https://nx.dev/features/enhance-AI?utm_source=egghead&utm_medium=videos&utm_campaign=Nx-AI&utm_id=Nx-AI) integration to expose generator capabilities to Cursor
- Use the AI assistant to brainstorm a new feature with the help of the Nx MCP tools
- Have Cursor leverage the Nx MCP tools to trigger the appropriate Nx generator for creating a React-based feature library
- Observe how the AI assistant takes the generated library and further integrates it into the codebase

**Key benefits:**

- **Predictable Code Structure**: Ensure generated libraries follow your project's and organization's conventions
- **Consistent Setup**: Maintain uniform configuration across TypeScript projects and package.json files
- **Guided Implementation**: Combine AI instructions with structured, predictable code generation

Enable Cursor to generate predictable project setups

Learn how to combine Cursor AI with Nx generators for predictable library creation in monorepos, ensuring consistent structure across your codebase.

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