Learn Ramda, React-storybook and Vscode

John Lindquist

AI Dev Essentials #7: Microsoft's AI Blitz, Google's NotebookLM Shines & New Coding Agents

AI agents are reshaping coding in GitHub and VS Code, while NotebookLM’s mobile app quietly revolutionizes how developers collect and recall research on the go.

Visual Studio Code is an integrated development environment (IDE) built and maintained by Microsoft. It is offered free of charge.

vscode

We look at the various features of the VSCode Extension for Playwright and how it can help you with your testing workflow.

Playwright VSCode Extension

Fast, easy and reliable testing for anything that runs in a browser.

cypress

[Storybook](https://storybook.js.org/) is already powerful for being able to develop components in isolation as well as using those stories as living documentation for others. [Nx](https://nx.dev) goes a step further. It leverages the fact that Storybook exposes an `iframe.html` that allows to access the rendering of the story without the actual Storybook frame. On top of that, Nx generates a Cypress configuration to run tests against the Storybook stories.

Prefer to read along as well? Here's the [accompanying article](https://blog.nrwl.io/use-cypress-with-next-js-and-nx-to-battle-test-your-react-components-da273f664f81).


Write Cypress Tests for our React Storybook Components with Nx

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

In this lesson, we develop a new React component in an [Nx](https://nx.dev) library with Storybook. We explore how to leverage [Storybook](https://storybook.js.org/) to develop the component in isolation, how to expose the component props s.t. we can then dynamically change them in the Storybook rendering and much more.

Prefer to read along as well? Here's the [accompanying article](https://blog.nrwl.io/use-storybook-with-tailwind-in-an-nx-workspace-5ceb08edad71).

Develop React Components in Isolation with Storybook and Nx

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

Developing UI components is sometimes a highly visual effort. We continuously want to have feedback on how a particular change affects our component rendering and adjust accordingly. Most often, as a developer, we include these components in their target applications, just to get that visual feedback. This is far from ideal though.

[Storybook](https://storybook.js.org/) solves exactly this! It allows you to develop components in isolation and experiment with the inputs and outputs of a component. Furthermore, once the Storybook story is fully developed, it can serve as documentation for other developers to explore.

Prefer to read along as well? Here's the [accompanying article](https://blog.nrwl.io/use-storybook-with-tailwind-in-an-nx-workspace-5ceb08edad71).

Configure Storybook for your React Based Nx Workspace Library

Building a component library is a great way to gain efficiencies in creating accessible React apps and Storybook is a great tool for doing that. Using the accessibility add-on to Storybook you can test your components in isolation for accessibility issues before composing them into an application.

If you can write and test each of these components initially with accessibility in mind then you now have:

* A library of reusable components with accessibility baked in.
* You won’t need to solve the same problems over and over again.

## Resources
* https://storybook.js.org/
* https://www.npmjs.com/package/@storybook/addon-a11y

Audit Component Accessibility Using Storybook

In this lesson, we include the `getServerSideProps` and `getStaticProps` exports into a story, using Storybook loaders. We mock the fetch request using MSW.

Mock getServerSideProps and getStaticProps Request with MSW and Storybook Loaders

In this lesson, we add the Mock Service Worker (MSW) library to Storybook for API endpoint mocking in Storybook. We configure MSW by hand for use with Next.js features like `getServerSideProps` and `getStaticProps` in stories.

Integrate MSW and Storybook

In this lesson, we configure `next/image` for use in Storybook. `next/image` is a Next.js component that creates highly performance-optimized images to users based on device capability and browser dimensions. It can be unoptimized for compatibility in development environments like Storybook.

Configure next/image Component to be Unoptimized

In this lesson, we add Storybook controls to page stories using Storybook args. With args, stories can be manipulated and transformed in the browser. Additionally, we configure all stories in a module to use default args.

Add Controls to Next.js Page Props with Story Args

In this lesson, we configure Storybook to load global stylesheets for all stories. This is a great way to get general CSS into all stories and useful practice for adding resets, fonts, and globally shared stylesheets.

Import Global Stylesheets for all Stories in preview.js

In this lesson, we create a story for a Next.js page. Pages are components. So we can easily document and test them in Storybook.

Create Stories for Next.js Pages

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

[Nx](https://nx.dev) combines a lot of the best tools on the market, integrates them, and provides tooling to provide the best possible developer experience. One such pair is [Cypress](https://www.cypress.io/) and [Storybook](https://storybook.js.org/). Storybook allows developing components in isolation, which is also a great basis for testing components, which is where Cypress shines.
In this lesson, we're exploring some of the `@nrwl/storybook` code generation utilities that allow you to not only configure Storybook for a React application but automatically also set up a Cypress test configuration for it along with some spec files generated from the Storybook stories.

Use Cypress and Storybook to test your React Components in Isolation

[Storybook](https://storybook.js.org/) is a framework that allows you to develop components in isolation. It is ideal for a variety of use cases, most commonly when you're building some shared component toolkit. As such, Nx comes with a dedicated package `@nrwl/storybook` that adds a series of useful code generators to add storybook to an existing React library, but also to automatically generate stories and more.

Use Storybook to Develop React Components in Nx

In this lesson, we will use the [foambubble template](https://github.com/foambubble/foam-template) to get started building a second brain in VSCode. The template includes a number of extensions. The primary extention is foam itself which generates links between notes that you write similar to the popular personal knowledge management service Roam Research.

**Note:** foam is in alpha so the api is likely to change quite a bit. They project that they have implemented 10% of the features currently roadmapped.



Create a Second Brain in VSCode with Foam

When working in a codebase that has frontend, backend, middleware, service, and other concerns all in the same GIT repository, it can be helpful to color-code your editor windows to quickly recognize in what area of your stack you are working. 

We can easily do this with a few VS Code settings in the `.vscode/settings.json` file in a directory. We can then open those directories in new VS code windows and visually see the difference between our frontend code and our backend code, eliminating cognitive overhead and context switching between, for example, ReactJS and NodeJS.

An example configuration for this color coding:

```json
{
  "workbench.colorCustomizations": {
    "titleBar.activeBackground": "#663399"
  }
}
```

Visually Organize Applications in VS Code with Window Color-Coding

In this lesson, we will learn how to use custom ENV variables to customize the PS1 Bash/Zsh command prompt in the VS Code terminal, without affecting our custom terminal themes in other applications. We will see how this can be useful for keeping the terminal clean for screen recordings.

I'm using Zsh in this video, but this works in Bash with `~/.bashrc` as well.

Customize the PS1 Bash/Zsh Command Prompt in VS Code with Environment Variables

Even though cypress has a well-designed API, we're not going to remember every single command.

In this quick lesson we're going to learn how to enable intelligent code completion for cypress tests in VSCode so our editor will help us write e2e tests much quicker

Enable intelligent code completion for cypress tests in VSCode


In this lesson you will learn how to create a minimalistic JavaScript playground with quokka.js and zen mode. You will also get an overview of some cool features quokka provides.

[Download quokka.js here](https://marketplace.visualstudio.com/items?itemName=WallabyJs.quokka-vscode)

Create JavaScript Scratchpad with quokka.js in VSCode

Two things that all React developers will agree on are:

1. It's imperative that you define all prop types for a given component when writing production-grade code.
2. Although the process only takes a minute, it can be a pain point of React development, especially when a component takes a large number of props..

In this lesson, we learn how to use the React PropTypes Generate extension to automatically define our prop-types for us without having to write a single line of code!.

### Resources
* [VS Code Marketplace - React PropTypes Extension](https://marketplace.visualstudio.com/items?itemName=suming.react-proptypes-generate)
* [NPM Registry - PropTypes](https://www.npmjs.com/package/prop-types(



Automate Adding PropTypes to Components with VS Code's React PropTypes Generator Extension

You're going to run into a lot of frustration writing Node CLI's unless you get good at debugging. (Even if you use TypeScript!) To understand code execution (particularly code you didn't write), you should be comfortable using the VS Code Debugger for its excellent step-through and watch features. But in case you don't know where the problem is (particularly for heavily nested codebases like React) or if you're looking for performance issues rather than code errors, you'll also want a robust logging solution. The `debug` module is best in class and is built into Oclif.

Debug Node.js CLIs with the VS Code Debugger and the `Debug` module

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.

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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