Learn Babel, Elasticsearch and Flux

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

We take the opengraph image we wrote in codesandbox and copy it into our serverless function. We use Rollup to compile the component into an IIFE (immediately invoked function expression) that can run in our headless browser environment and cover a wide set of rollup plugins that we need for compatibility. We end the lesson having successfully screenshot our opengraph react component from our Netlify Function

**Note**:  Due to Netlify changing their handling, the path in `fs.readFileSync` is affected. Update to:
```js
const script = fs.readFileSync(path.resolve(__dirname, "image.js"), "utf-8");
```

Compiling a React to an IIFE with Rollup to execute and render in a headless browser

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

Converting all of our modern JavaScript into ES5 compatible syntax is a great way to use modern features while targeting older browsers. What happens when the browsers natively support these language features? Then it no longer makes sense to transform that code or to include polyfills that will go unused. In this lesson, we’ll add the `@babel/polyfill` package and configure `babel-preset-env`

Target specific browsers with babel-preset-env and the babel pollyfill

We transpile our JavaScript code so we can use modern language features, even if browser support isn't quite where we need it to be. This includes proposed features that aren't yet part of the official language specification. In this lesson, we'll see how we can support the proposed class property syntax for JavaScript with a Babel plugin.

Support Proposed JavaScript Features with Babel Plugins

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

In this lesson, we’ll install Babel and use it from the terminal to see how it can take JavaScript that uses modern language features and output JavaScript that will run in down-level browsers.

Transform Modern JavaScript Features with Babel

What is already a nice workflow using Gulp, Browserify, Watchify & Browsersync to enable auto rebuilds & live browser reloading can be further improved by adding Babelify. A simple wrapper around the popular transpiler Babel, Babelify can ‘transform’ source files as they pass through our stream to enable certain Javascript features not yet available in the Browser or Node. We end the lesson by looking at how to generate & extract sourcemaps using `exorcist`.

Gulp and Browserify - Adding Babel & Source Maps

With this lesson, we will bring it all together, using what we've learned in the series to fisnish out the React Flux architecture example.

The completed code for this series can be found [here on Github](https://github.com/eggheadio/egghead-react-flux-example).

React Flux: Wrapping Up

As your React Flux application grows, it is inevitable that you start to see functionality and code repeated. With React, you can easily keep things DRY by using mixins.

React Flux: Remove Duplicate Code with Mixins

By default, React doesn't have built in routing. We will add routing to our application with the react-routing-component.

React Flux: Routing with react-router-component

Organizing modules and components is a critical aspect of the development of any single-page web application (SPA). In this lesson we will look at refactoring the Flux React store into a more module structure.

React Flux: Project Organization

In Flux our Components are referred to as Views, but they are the same familiar Components associated with any React development.  In Flux, however, we have the benefit of the architecture to keep us from having to pass down through a long chain of children Components any functionality that may be embedded in our Stores.  In this Lesson we will wire up all of our Components in record time utilizing the architecture we've already established.

React Flux: Components/Views

Stores are where the real work in our application is done.  Dispatchers broadcast Actions to all Stores, and the Stores registered to listen for those Actions will perform any logic needed to update our Views.  In this lesson we will establish our first Store and register the Actions we wish to respond to.

React Flux: Stores

Actions contain no functionality, but rather describe an event in our application.  In this lesson we will describe our Actions as they relate to our application.  These Actions will then be available for triggering in our Views and for execution in our Store.

React Flux: Actions

Flux has four major components: Stores, Dispatchers, Views, and Actions.  These components interact less like a typical MVC and more like an Event Bus.  In this lesson we will start by creating our Dispatcher which queues up our Actions as Promises and executes them as they are received.  We'll also create an AppDispatcher which, unlike the Dispatcher, is more specific to our application.

React Flux: Overview and Dispatchers

Our build process during this series will utilize both gulp and browserify.  In this lesson we will take a moment to setup our environment and all of the tooling required to begin developing our Flux Application.  We've kept the build process very simple so porting it to grunt should be a breeze, but this also serves as a simple primer for those who may not be familiar with gulp.

React Flux: Development Environment Setup

<p>Henry Zhu is currently a steward for Babel, and today he'll be discussing the management and lifestyle side of working on an open source project full-time.</p><p>The financial side to open source is interesting. Henry talks about where he gets his money and how he can be financially independent while still working on open source full time. Most of the money comes from donations through Open Collective and Patreon. A lot of the donations come from users, but corporations do donate as well. Henry also discusses the stability of it, and how the NPR model of asking for donations can lead to uncertainty about the next month.</p><p>Henry says he's enjoying the management and marketing side of being a steward about as much as he enjoys coding. It's a side that people don't often think about with open source. This side of it includes a lot of marketing and interacting with the greater community, as well as the other maintainers and contributors to babel.</p><p>How does Henry handle all this responsibility when there is no one above him telling him when to take a break, or when to go on vacation? Henry and Joel talk about having a positive "selfishness", and taking care of yourself so that the project can prosper. If you want to be doing it for years you have to make sure that you don't burn out.</p><hr /><h3>Transcript</h3><p><a href="https://egghead.simplecast.com/episodes/henry-zhu/transcript">"Henry Zhu, Maintainer of Babel" Transcript</a></p><h3>Resources:</h3><ul><li><a href="https://babeljs.io/">Babel</a></li><li><a href="https://www.amazon.com/Power-Habit-What-Life-Business/dp/081298160X">The Power of Habit</a></li><li><a href="https://www.amazon.com/You-Are-What-Love-Spiritual/dp/158743380X">You Are What You Love</a></li><li><a href="https://www.tinyhabits.com/">Tiny Habits</a></li><li><a href="https://kaidavis.com/articles/">Kai Davis</a></li></ul><h3>Henry Zhu:</h3><ul><li><a href="https://twitter.com/left_pad?lang=en">Twitter</a></li><li><a href="https://www.henryzoo.com/">Website</a></li><li><a href="https://github.com/hzoo">Github</a></li><li><a href="https://www.patreon.com/henryzhu">Patreon</a></li><li><a href="https://opencollective.com/babel#">Open Collective</a></li></ul><h3>Joel Hooks:</h3><ul><li><a href="https://twitter.com/jhooks">Twitter</a></li><li><a href="https://joelhooks.com/">Website</a></li></ul>


Today, Henry Zhu, the lead maintainer of Babel, discusses what it's like to work on a large and important open source project. Henry gets into how Babel receives funding, how corporations are involved in open source, stewardship of an open source project, mental and physical health, and not compromising his values.  

Henry Zhu, Maintainer of Babel

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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