Learn Git, Html and Requirejs

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

This project is a Next.js example of Kent C Dodd's Frontend Masters Advanced Remix Workshop project.

The aim of this project and course is to take a large project using the traditional Next.js pages architecture and migrate the full project to use the new app directory architecture.

We will fork project repo and clone it to get setup with the project locally. 

Fork and Clone the Next.js 13 Project to Get Started

Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.

Getting started with contributing to open source projects can be very confusing and stressful. This talk will help clear away the confusion and pinpoint why and how you can make your first open source contribution. 



How to Make Your First Open Source Contribution

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

We'll discuss the minimum set of files that are required to get up and running with Vue.

We'll add an `index.html` file as our entry point, a `main.js` file that will contain our JavaScript code and a `styles.css` that will contain all of our CSS. We also load Vue into our project with UNPKG which exposes the Vue object to us.

Set up the Minimum Files Needed to Run Vue 3

It always happens again. We have some weird bug in our codebase, hard to debug and just looking at the Git history doesn't help much. Git has a powerful command to help us: `git bisect`.

We have to simply identify a commit where the bug was not present, and the bad commit (usually the latest of the `master` or `main` branch). Then we invoke `git bisect` with both of them. The rest is just following along and answering questions. Let's see how that works.

Find that bad commit that broke your App with Git Bisect

HTML 5 is the structure of our web pages. It is the markup that represents the DOM (document object model).

html

We'll round out the HTML for our form fields by adding radio buttons and checkboxes, including each in a checked, unchecked and disabled state. You'll learn about best practices for both field types semantically as well as when to use each.

Radio buttons should be used when you want a single selection out of a set of options while checkboxes should be used when you want multiple selections out of a set. A key difference between radio/checkbox and other inputs is that you wrap the label around the options which creates a direct relationship between the label and input so a `for` attribute is not strictly necessary.

Create Semantic, Accessible HTML for Radio Buttons and Checkboxes

Continuing on from our text inputs, we'll make our next field type including a select with the `multiple` attribute. Additionally, we'll include these fields in a disabled state. Both types will use the `.form-group` created in the previous lesson for a touch of layout styling.

You'll notice the same pattern here as with text inputs in that to make select inputs fully semantic and accessible you will need to add a `for` attribute that matches the `id` of the select element

Create Semantic, Accessible HTML for Form Selects

Learn how to ensure accessibility of text form inputs from the start by defining semantic HTML. To do this, we'll need a label for each input that we create. This label needs to be associated with the input through a `for` attribute which allows screen readers to know that a label is associated as well as allows a click of the label to focus the input.

We'll create HTML for text and textarea, as well as disabled versions of both.

Create Semantic, Accessible HTML for Text Form Inputs

Learn how to make a fully functioning Tic Tac Toe game that doesn't need any client-side JavaScript!

For this course, we'll be using Pug and Stylus which are preprocessors for HTML and CSS respectively.

Learn how to:
- Generate markup with loops and template literals
- Leverage native HTML behavior
- Create SVG
- Use CSS variables and scope
- Implement game logic with HTML forms
- Animate SVG with CSS
- Use CSS Animation to provide motion
- Use Pug Mixins
- Disable motion when users don't want it
- Apply visual effects using CSS filter and backdrop-filter
- Generate random animations using Pug and CSS variables

Cascading Style Sheets bring order and beauty to our applications.

Tic Tac Toe with CSS and SVG

If you're repeating blocks of markup or code in Pug, you can use a mixin to reduce the maintenance overhead. Here, we look at some of the ways you can use Pug mixins.

Use Pug Mixins for Reusable Blocks of Markup

We can use SVG to create a cross with one path. But, what if want to animate a cross as if it's being hand-drawn? We can do this by defining more than one path for our SVG.

Use SVG to Create an Animatable Cross

Use the circle element in SVG to define a radius and center points for a circle.

Use SVG to Create a Circle

Use SVG to create lines with the path element. We can hand-code a path definition and set a stroke width and stroke linecap. We can adjust the path definition to accommodate for the SVG viewBox.

Use SVG to Create Lines

You can use an element with the type "reset" within a form to reset a form on click. It's particularly useful when we don't want users to refresh a page in order to get back to an initial state.

Use Type Reset to Reset Form State

Use the HTML preprocessor Pug to generate markup for a form. Here we generate a set of labels and checkboxes. We link these using ES6 Template Literals with a loop index.

Use Pug For Loops to Generate Form Markup

The CSS filter property lets us apply the power of SVG filter effects to HTML elements. One little-known effect included in modern browsers is [`<fePointLight>`](https://developer.mozilla.org/en-US/docs/Web/SVG/Element/fePointLight). We'll use it together with `<feDiffuseLighting>` to add a light source to an `<h1 />` element.

Add a Light Source with CSS and the fePointLight SVG Filter

You can create diffs in markdown to show what has changed in a code snippet. I use this in blog posts to highlight changed lines for readers. This works for code snippets in most markdown packages and on Dev.to.

To show a line of code changing in a function, you can do this:

```diff
function addTwoNumbers (num1, num2) {
-  return 1 + 2
+  return num1 + num2
}
```

First, instead of specifying the programming language, use diff after the backticks. Then at the beginning of any lines of code you want to show as removed, add a -. At the beginning of any lines of code you want to show as added, add a +.

The code would look like this:

````
```diff
function addTwoNumbers (num1, num2) {
-  return 1 + 2
+  return num1 + num2
}
```
````

Create a Diff in Markdown to Show What Has Changed in a Code Snippet

WAI-ARIA, the Accessible Rich Internet Applications Suite, defines a way to make Web content and Web applications more accessible to people with disabilities. It especially helps with dynamic content and advanced user interface controls developed with Ajax, HTML, JavaScript, and related technologies. 

aria

In this exercise, we will be going over 2 examples of when to use aria-hidden (hint: decoration) and when to use hidden.

In the first example, we will be hiding a "middle dot" that we have for formatting. This middle dot isn't necessary to be read out, so we add an aria-hidden attribute on that span.

When we use hidden, we will completely hide things from the viewport AND the screen reader.

aria-hidden vs. hidden

Express is a minimal and flexible Node.js web application framework that provides a robust set of features for web and mobile applications.

express

In this lesson, we will use Auth0 to generate a JWT that we can use to validate future requests to our server.

We will then return that token and test that it works when authenicated by our middleware.

During the lesson, we'll also briefly look at a strategy to keep your credentials out of source control using a .gitignore file. 

Get a JWT From Auth0 in Express to Be Used in Authentication

In this lesson, Mark will summarize why it is good to use RequireJS for managing JavaScript dependencies. We'll go over an example of a plain vanilla JavaScript implementation of jQuery, and refactor the code to use RequireJS.

WTF is RequireJS?

By default, git provides `master` as the default branch. This lesson shows how to get round that and use an alternative as the default branch.

The key is to switch to a new branch before you add your first commit, I choose `main` in this lesson. When I push `main` up to Github as the first branch, it gets set to the default.

Configure a New Github Repo to Use a Different Default Branch

By default, git provides `master` as the default branch. This lesson shows how to get round that and use an alternative as the default branch for an existing repo.

Configure an existing Github repo to use a different default branch

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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