Learn Ramda, Tweenmax and Webpack

A library designed specifically for a functional programming style, one that makes it easy to create functional pipelines, one that never mutates user data.

ramda

In this lesson we'll see how to use the `without` function to remove items in a known list from an array. Then we'll generate our exclusion list and apply it to `without` using `reduce`, `min`, `max` and `converge` to detect the highest and lowest values in an array and creating a new array that excludes those values.

Discard the High and Low Values From an Array of Numbers with Ramda

Learn how you can use ramda.js to bring functional programming concepts into your JavaScript code. Ramda offers composability and immutability right out of the box, so you can leave your imperative code behind and build cleaner, more maintainable code.

Functional Programming in JavaScript with Ramda.js

webpack is a tool to build JavaScript modules in your application. To start using webpack from its cli or api, follow the Installation instructions. webpack simplifies your workflow by quickly constructing a dependency graph of your application and bundling them in the right order. webpack can be configured to customise optimisations to your code, to split vendor/css/js code for production, run a development server that hot-reloads your code without page refresh and many such cool features.

webpack

As you refactor and modify applications, it's difficult to manage and keep track of files as they become unused. Keeping this "dead" code around adds noise to your application and reduces clarity. Just as ESLint can tell us when variables become unused, Webpack (with the help of the unused-files-webpack-plugin) can tell us when entire files become unused. First, we'll install the plugin with npm and save it as a devDependency. Next, we'll use npm run scripts to build a new command that will run Webpack with the plugin. Finally, we'll learn how to use Webpack environment variables to conditionally add plugins to your Webpack config. By the end of the lesson, you'll have a useful cli command you can run to check for unused modules in your Webpack build

Delete Unused Code with Webpack and unused-files-webpack-plugin

Babel has support for the latest version of JavaScript through syntax transformers. These plugins allow you to use new syntax, right now without waiting for browser support.

babel

In this lesson, we'll configure webpack to use the Babel loader for our JavaScript files, allowing us to transpile our source files and generate a bundle for deploying our application.

Configure webpack to Load JavaScript Files through Babel with babel-loader

While we're developing an application, having a local server to preview our work is a great convenience. With `webpack`, we can take advantage of `webpack-dev-server` to serve up files and automatically reload the browser when we make changes. In this lesson, we'll install and configure `webpack-dev-server` in our boilerplate project.

Serve a webpack Bundle while Developing with webpack-dev-server

We can separate our custom application code from the common libraries we leverage, such as React and ReactDOM. In this lesson we'll configure `webpack` to treat React and ReactDOM as external dependencies. Then we'll update our HTML template to conditionally include the CDN links for those libraries for production builds.

Externalize Dependencies to be Loaded via CDN with webpack

Bundle size has a huge impact on JavaScript performance. It's not just about download speed, but all the JavaScript we ship to the browser needs to be parsed and compiled before it can be executed. Keeping our bundle in check can be difficult, but it's much easier when we can see where the bloat is coming from. In this lesson, we'll add the `webpack-bundle-analyzer` plugin to inspect the output of our production build.

Analyze a Production JavaScript Bundle with webpack-bundle-analyzer

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

One approach to building high performance applications with `webpack` is to take advantage of code-splitting to only load the needed JavaScript on initial load and asynchronously load additional JavaScript bundles when needed. In this lesson, we'll create add some code-splitting to our placeholder application and configure our project to support the dynamic `import()` syntax.

Asynchronously Load webpack Bundles through Code-splitting and React Suspense

In this lesson, we'll add React and ReactDOM to our project and create an entry point for the React application. We'll also configure Babel with the React preset so Babel can transpile our JSX into JavaScript that will run in a browser.

Configure babel for React with preset-react

The `development` and `production` modes in `webpack` optimize the output in different ways. In `development` mode, the focus is on faster builds and a better developer experience. In `production` mode, the focus is on highly optimized bundles, leading to a better user experience. The good news is, we can have both. In this lesson, we'll separate our `webpack` config into two configurations and update our scripts to run the right config for our needs.

Create Separate webpack Configs for Development and Production with webpack-merge

The Chrome Developer Tools (DevTools for short), are a set of web authoring and debugging tools built into Google Chrome.

chrome-devtools

A potential downside to all the transformations our tools perform on our code is that when you need to debug your application, the code being served isn’t the code you wrote. This is where source maps save the day. We can serve transformed and bundled code, then debug in DevTools against our source code right in the browser. In this lesson we’ll take a look at the default debug experience without source maps, then we’ll configure webpack to generate source maps and look at the enhanced experience.

Generate Source Maps through webpack for a Better Debugging Experience with source-map

As of `webpack` version 4, there is a top-level configuration option called `mode`. Mode allows us optimize for build speed during development, while optimizing for runtime performance and smaller bundles for production builds. In this lesson, we'll add the mode key to our webpack configuration and look at the difference in output between the `production` and `development` settings.

Control the Output of webpack with the mode Setting

Even if it's only a simple one, we'll need to serve up an HTML file to run our JavaScript app. We can hardcode the bundle path into a script tag, but then if we want to change our bundle name, we need to update that in our webpack config and in our html file. In this lesson, we'll see how to use the `HtmlWebpackPlugin` to inject the script tag for our bundle into an HTML template file.

Inject a JavaScript bundle into HTML with the HtmlWebpackPlugin

It's inconvenient during development to have to run a script each time we make a change to our source. We can have webpack generate an updated bundle as we make changes. In this lesson, we'll create an `npm` script to run webpack in watch mode and  see it in action as we modify some source files.

Update your Bundle with each File Save with webpack's watch Mode

Generating new bundles when we change files is a great start, and the live-reload provided by webpack dev server is convenient, but a reload means we lose our application state. When you want to see the results of minor adjustments while your application is in a specific state, it would be more convenient if we could have our changes reflected without losing that state. In this lesson, we'll add hot reloading to our development webpack server so our changes will be reflected in the browser when we save new changes, without the need to a refresh.

Hot Reload a React App in Development with react-hot-loader

In this course, we'll start with an empty directory and build up a starter project for building a React application.

Along the way, we'll work with webpack and babel to transform, code-split, and bundle our code. We'll create npm scripts to execute commands and work with git and GitHub to track changes and share our code in public. We'll build quality into our project with Jest for unit testing and ESLint for static analysis. We'll configure Prettier for formatting and then enforce all these quality checks before commit with git hooks. We'll even incorporate some accessibility checks and enforce some best practices in React with react-axe, React's StrictMode and a default ErrorBoundary component.

Whether you end up using the resulting project to start new projects of your own, or you continue to use great community tools like Create React App, you'll leave this course understanding the underlying tools and how they work together.

Course Overview: Modern JavaScript Tooling with React

In this lesson, we'll add some initial files to our project, install `webpack` and `webpack-cli`, and setup an npm script to create a JavaScript bundle using `webpack`. We'll inspect the resulting bundle to understand what webpack does by default. We’ll also add a .gitignore file to avoid committing node_modules and files that result from a build process.

Install webpack and create a default bundle

We _could_ use `webpack` without any configuration and that would bundle our JavaScript, but our options will be limited. In this lesson, we'll create a webpack configuration file and we’ll explicitly define the entry point, and some output options as a starting point to build on.

Explicitly Define an Entry Point with a webpack Configuration File

JavaScript files aren't the only type of asset that `webpack` can bundle. In this lesson, we'll add a CSS file to our boilerplate and then install and configure the appropriate loaders in `webpack` to handle the CSS that's imported into our project.



Automatically Import CSS in JavaScript with webpack using style-loader and css-loader

Building an application with React typically involves several tools. Whether you configure these on your own or use a tool meant to save you from the configuration like Create React App, the tools are still there. When you come up against a situation where you need to start from scratch or make changes to a webpack or babel configuration, it helps to understand what each tool does, how they fit together, and how they are configured. 

In this course, we’ll start from an empty directory and by the end, we will have configured a slim but powerful boilerplate project. Along the way, we’ll touch on each of the major tools and build up to the final setup to gain an understanding of each tool’s purpose and how they all work together to help you build and deliver an application.

Modern JavaScript Tooling with React

The default service worker that comes with create-react-app doesn't allow for very much configuration.  We'll replace that default service worker in two ways.

First, we'll create a blank service worker js file, and use that as our custom service worker.

Next, we'll re-write the default webpack config with `react-app-rewired`, and utilize the `InjectManifest` workbox webpack plugin.  This will allow us to create a totally custom service worker that still allows us to use workbox, without ejecting our app.

Use a Custom Service Worker in a create-react-app PWA without Ejecting

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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