Learn Angular-material, Linux and Serverless

Amazon Web Services (AWS) is a powerful expansive cloud infrastructure platform for building modern web applications.

AWS provides services for data, presentation, authentication, security, video encoding, and much more. Offering over 175 fully-featured services from data centers globally, AWS powers a large percentage of the internet.

Now that we are SSH'ed into our EC2 instance we can start modifying the server to actually run the application that we want to to run. Before we do that we want to switch users because you initially SSH you are the root user.

We will start by adding a `ghost-admin` user that we will give sudo permissions to. Once we are logged into that user we will update the server to ensure we are on the latest and greatest versions before installing additional software.


Add and Log In to an AWS EC2 Instance with a sudo User

Node.js is a platform built on Chrome's JavaScript runtime for easily building fast, scalable network applications. Node.js uses an event-driven, non-blocking I/O model that makes it lightweight and efficient, perfect for data-intensive real-time applications that run across distributed devices.

node

Ghost has two hard dependencies which need to be installed before it can run. Those are Nginx and Node.js.

We will start by installing Nginx and set Nginx up to control the traffic coming into the EC2 server. After this, we'll install NVM using the raw install script that they provide on GitHub. With NVM installed we can then install the latest version of Node.js


Install Nginx and Node.js on a AWS EC2 Server

To run Ghost, we first need to install the ghost cli. This can be done with npm now that Node.js is installed on the server.

After installing the CLI we will create a directory `/var/www/ghost` that the actual installation of Ghost will occur in. We'll update the permissions of this file to include the `ghost-admin` user that we are logged in as.


Install Ghost CLI and Create Directory

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

javascript

In this video you'll learn how to deploy a React app to AWS using AWS Amplify Hosting's drag and drop folder functionality. The app will also automatically be globally distributed on AWS's global CDN.

Deploy a React App to AWS in 24 seconds

Starting from a prebuilt frontend, Jason Lengstorf guides you through the creation of a "Corgi Up-boop App" that uses Netlify Functions and Hasura. You can view [the deployed final product on Netlify](https://egghead-serverless-functions.netlify.app/).

You'll set up the Netlify CLI, and configure a local development environment before writing and deploying functions with a few different use cases.

The first example function bypasses CORS restrictions to retrieve corgi data from an example third-party API.

Next, you'll configure environmental variables to securely communicate with the Unsplash API without exposing private keys to the client.

Serverless functions don't persist data by themselves, so we will use  Hasura to create a GraphQL API for tracking the number of "boops" each corgi receives.

This course is an excellent illustration of the speed of Serverless functions, and how fast you can implement functionality in your web applications.

An all-in-one workflow that combines global deployment, continuous integration, and automatic HTTPS.

netlify

Build a Corgi Up-boop Web App with Netlify Serverless Functions and Hasura

Environment variables set in a `.env` file can be imported using the Netlify CLI with the following command: `netlify env:import`.

This allows you to manage environment variables directly on Netlify, providing the benefit of security and flexibility of sharing environment variables with your team. 

From inside Netlify, you can access the environment variables in:

`Site settings` > `Build & deploy` > `Environment` > `Environment variables`

Import and Set Environment Variables from a .env file using Netlify CLI

CORS limits websites from communicating with other domains without the full consent of both sites. Consuming data from a 3rd Party REST API makes it difficult since we can't properly configure the appropriate CORS headers. To solve this, we'll set up a proxy server to request the data using a Netlify function that avoids CORS altogether.

The Third-Party API that we'll be working with will provide the following data: `id`, `name`, `favoriteSong` for each corgi. 

[http://no-cors-api.netlify.app/api/corgis](http://no-cors-api.netlify.app/api/corgis)

We will also use `node-fetch` a light-weight module that brings the `fetch` API to fetch the data into our application.

Circumvent CORS when Accessing a Third-Party API using Netlify Functions 

There are a few ways you can run Netlify serverless functions while you are developing them. 

You can: 

- Navigate to the directory under the `/.netlify/functions/` path
- Curl them using the full URL
- Use an app like Postman

Manually Trigger Netlify Serverless Functions Locally

Netlify makes deploying to production a breeze by configuring your Netlify CLI to your Netlify account and integrating with GitHub.

We'll log in to Netlify through the CLI and initialize a site by completing all the options that the CLI runs us through. Once this is done, Netlify will trigger new builds for your site every time you push to GitHub.

Deploy Serverless Functions to Production on Netlify using the Netlify CLI

Netlify makes developing serverless functions easy with the netlify-cli (`ntl` for short). You'll be able to build and test functions locally as well as publish your functions from the CLI. 

We will install netlify-cli globally and create a `netlify.toml` file that will configure where the CLI should look to run functions that we define, in our case `functions/`. When the application is served up, Netlify runs functions under `/.netlify/functions/`.

Set up a Local Development Environment for Serverless Functions Using Netlify

A serverless function is something that is going to allow us to do server-like things without having to manage servers, but an important distinction here is that serverless does not mean that there are **no** servers. 

The environment is actually running on top of an operating system and uses physical servers, but the responsibility for provisioning and managing the infrastructure belongs entirely to the service provider.

Some of the benefits of using serverless functions: 

- **Enhanced scalability.** No need for developers to implement code to scale. It scales automatically.
- **Reduced time to market.** Serverless is an optimized set of tools for building quickly.
- **No boiler code.** A serverless function is just a tiny piece of logic. Reducing the complexity of software.
- **More time for user experience.** Serverless functions allow teams to focus resources on the user experience and not on managing servers.
- **Security.** We can call third-party APIs securely without exposing important data on the client-side.

What Are Serverless Functions?

Vim is a highly configurable text editor built to make creating and changing any kind of text very efficient. It is included as "vi" with most UNIX systems and with Apple OS X.

Every programmer starting to work in a *nix environment eventually finds themselves in VIM, the default text editor. Often by mistake! Because VIM lacks the typical polished GUI of normal programs, it can be hard to discover the correct sequence of commands to even exit the program. Once you learn how to exit VIM, then the next task is to also learn how to make VIM do what you want -while- exiting!

In this lesson we cover 7 different ways of exiting VIM, whether you are in EX mode or Command (also known as Escape) mode, whether you are editing a single file or several at once.

Exit Vim

Installing the AWS CLI on your local workstation may be one of the biggest boosts to productivity you can make if you use AWS often. Almost all AWS features are available via the CLI allowing you to script, schedule and automate many AWS related tasks.
In this lesson, I’ll show you how to setup and verify the AWS CLI.

Installing the AWS CLI on OS X and Linux

AngularJS is an open-source JavaScript framework, maintained by Google, that assists with running single-page applications. Its goal is to augment web-based applications with model–view–controller (MVC) capability, in an effort to make both development and testing easier.

angularjs

Themes convey meaning through color, tones, and contrasts. And the theme color palettes, alphas, and shadows deliver a consistent tone to your application and a unified feel for all Angular Material UI components. Theming allows developers to change the intention and meaning of the application by changing the colors and shadows. 

Accessibility is a huge - often overlooked - feature in modern web applications. Angular Material components all implement ARIA features whenever possible. And for usage scenarios where the ARIA attributes are not explicitly provided and cannot be inferred, then Angular Material will provide details console warnings alerting the developer.

Let’s use the Angular Material Theming service to change the background, primary, accent, and warning colors used within the entire application. These will be configured at startup using the $mdThemeProvider.  

Let’s also resolve the two ARIA warnings shown in console. These originate from icon buttons where the ARIA label cannot be inferred. So let’s add a static aria-label and a dynamic, interpolated aria-label to those buttons.

> This course presented by Thomas Burleson and Aaron Frost

Angular Material: Theming and ARIA

Most Angular Material UI components are declarative and used directly in the HTML markup. Some components, however, are programmatic and must be configured and invoked from Javascript. Programmatic components have a short-term, limited DOM lifespan and include Dialogs, Toast, and BottomSheet.

Let’s use the BottomSheet service to configure and display an Angular Material BottomSheet container which in turn will display a nested, custom Contact Sheet view.

> This course presented by by Thomas Burleson & Aaron Frost.

Angular Material: Using a BottomSheet

Both flexbox CSS and the Angular Material Layout allow web applications to `respond` to the size of the browser at any given point. No matter what the browser width may be, the web app adjusts its layout sizing accordingly. Combine responsive functionality with media query support and your web applications will have `adaptive` features to adapt to the width of the browser at a specific points. More than fluidly adjusting the widths and heights of elements, Adaptive applications can change the rendered UX with new positions, new components, and new styles. Angular Material Layout hides the complexities of implementing adaptive features with a singularly unique and simple HTML syntax.
Let’s use the Adaptive extensions of the Angular Material API to hide the SideNav on small devices (like iPhone or Android). We will explore the use of the $mdMedia service to easily lock open the SideNav on larger devices. And we will see how the $mdSideNav service can be used to programmatically close the SideNav after a user is selected.

> This course presented by Thomas Burleson and Aaron Frost.

Angular Material: Adaptive Layouts

Angular Material UI components have been designed from the ground-up to use dynamic, live data. Supporting AngularJS interpolation, expressions, and databindings… each component exposes an intuitive API and fully supports the existing ng-<xxx> directives published in AngularJS.

Let’s replace the hard-coded, static HTML with dynamic, mock data using AngularJS data bindings, ng-repeat, and the ng-click directives. Our UX remains unchanged, yet now we are using custom application logic and dataservices.

> This course has been created by Thomas Burleson & Aaron Frost.

Angular Material: UI Components with Dynamic Data

Angular Material is an Angular-native, UI component framework from Google. It is a reference implementation of Google's Material Design and provides >30 UI components and services essential for mobile and desktop AngularJS applications.

In this course, we will quickly build a real-work application Angular UI application, take a tour of Angular Material, and learn how ngMaterial can be used in your projects.

*presented by Thomas Burleson and Aaron Frost*

Introduction to AngularJS Material

The Angular Material Layout API wraps the flexbox CSS features with an amazingly easy, powerful HTML markup API. The Layout API hides the complexities of the CSS, is intuitive to use, and eliminates the need for developers to become flexbox experts to implement modern page layouts. 

In this lesson we will learn how to use the Angular Material Container components and the Layout API to implement the layouts specified in design wireframe.

> This course has been created by Thomas Burleson and Aaron Frost. 

Angular Material: Containers with the Layout API

Learn how to install Angular Material with `npm install`  and load the required libraries and stylesheets into your blank web page.

> This course has been created by Thomas Burleson and Aaron Frost

Angular Material: Installing with NPM

Angular Material provides over 30 UI components and services. The framework offers a suite of components and containers: from simple Button components with built-in ink and hover effects, theming, and ARIA support… to more complex container components like mdList and Tabs.  

Let’s use some of those components in our SideNav and Content containers. For now we will use hard-coded, HTML markup. This allows us to confirm the UX matches that specified in the design docs before we start working on our application logic.

> This course has been created by Thomas Burleson and Aaron Frost.

Angular Material: Intro to UI Components

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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