Learn Canvas, Openapi and Ramda

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

A <canvas> element provides scripts with a resolution-dependent bitmap canvas, which can be used for rendering graphs, game graphics, or other visual images on the fly.

canvas

The `canvas` element exposes a 2d context which allows you to draw lines using the `moveTo`, `lineTo`, and `stroke` api. You can combine this with Mouse events to make a simple drawing app.

Draw with Your Mouse on an HTML Canvas

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

Most.js is a toolkit for reactive programming. It helps you compose asynchronous operations on streams of values and events, e.g. WebSocket messages, DOM events, etc, and on time-varying values, e.g. the "current value" of an <input>, without many of the hazards of side effects and mutable shared state.

most

Unlike RxJS, Most does not include an observable-based AJAX helper utility. However, it can be very convenient to have one, because it allows you to use streams to do things like declaratively handling errors & setting up cancellation. In this lesson, we use currying & functional composition to create an Observable-based wrapper around the fetch API with Ramda & Most.

Create an Observable-Based Fetch Wrapper Using Ramda & Most

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'll take a stateful React component and look at how we can refactor our `setState` calls to use an updater function and then leverage Ramda's `evolve` function to make our updater function a reusable utility that isn't tied to the React API.

Update State in React with Ramda's Evolve

In this lesson, we'll refactor a React component to use Ramda lenses to update our component state. We'll create a lens to focus on the property we want to target and use `over` to apply the existing state value to a utility function and we'll get back a new state that will be reflected in our rendered UI.

Update Component State in React With Ramda Lenses

When doing comparisons inside of functions, you end of relying heavily on the argument passed into the function. Ramda's `converge` allows you to do comparisons in a Point-Free style allowing you more flexibility with composing and constructing functions. This lesson walks through refactoring a function to Point-Free style using Ramda's Converge.

Eliminate Function Arguments (Point-Free Style) with Ramda's Converge

You can really unlock the power of ramda (and functional programming in general) when you combine functions. Counting words in a string may seem like a relatively difficult task, but ramda makes it easy by providing a `countBy` function.  This lesson walks through using the `countBy` to count words in a string.

Count Words in a String with Ramda's countBy and invert

Open API Initiative (OAI) is focused on creating, evolving and promoting a vendor neutral API Description Format based on the Swagger Specification

openapi

Swagger is a project used to describe restful APIs using the OpenAPI Specification. It allows you to document your API so consumers understand the endpoints, parameters, and responses. In this lesson, I'll show you how to install the swagger command line tool, create a new API project using swagger, and introduce you to the swagger API editor.

Create an API with Swagger

There are a lot of great monitoring tools available for Node.js. It is also incredibly easy to build monitoring into your application. In this lesson, we will add precision monitoring to the function calls in the Todo API server, log it to the console, and persist those results to Elasticsearch where they can later be reviewed, graphed, and analyzed.

Build Monitoring into your API Server

In this lesson, I will show you how to update a simple, skeleton React application to work with the Todo API server built with Swagger. Using existing components, you will learn how to display all Todo items, update an existing Todo item, and add a new Todo item.

Connect an existing React application to an API server created with Swagger

The last step before deploying your new API server into production is to load test it. Load testing allows you to better understand the performance characteristics as well as forecast load and capacity. Fortunately, load testing is incredibly easy and I'll show you exactly how to create a load test plan, test the response from the API server to ensure it is responding correctly, and scale your test up to simulate as many users as needed.

Perform Load Tests on an API Server using Apache jmeter

This lesson shows you how to delete a Todo item from Elasticsearch using the Todo ID from the URL parameters according to the specifications defined in your Swagger API docs.

Create a Node.js function for an HTTP DELETE request for a Swagger API

This lesson will teach you how to create the javascript functions defined by the Swagger specification to return all Todo items to the client when requested with an HTTP GET method.

Create a Node.js function for an HTTP GET request for a Swagger API

This lesson shows you how to create the functions defined in the Todo API specification to create new Todo items when received in HTTP POST methods. You will also see the errors you will get when CORS is not properly configured and how to resolve them by installing the NPM CORS package.

Create a Node.js function for an HTTP POST request for a Swagger API

This lesson will teach you how to update a Todo item in Elasticsearch when received from an HTTP PUT method in your API server built with Swagger.

Create a Node.js function for an HTTP PUT request for a Swagger API

In this lesson, you will learn how to create a function in your Node.js Todo API server that gets the ID of the Todo item being requested from the URL and retrieve the Todo item from Elasticsearch.

Create a Node.js function for an HTTP GET request with URL parameters

In this lesson, you will learn how to define an HTTP GET endpoint using Swagger with URL parameters to identify the ID of the Todo item being requested.

Define an HTTP GET Endpoint with URL Parameters for a Node.js Server with Swagger

In this lesson, you will learn how to create an HTTP GET endpoint for a node.js server using Swagger. This endpoint will be used to return all of the Todo items from Elasticsearch to the client.

Define an HTTP GET Endpoint for a Node.js API Server with Swagger

This lesson demonstrates how to access the ID of a Todo item being deleted from the URL parameters when used with an HTTP DELETE method.



Define an HTTP DELETE Endpoint for a Node.js API Server with Swagger

In this lesson, you will learn how to define HTTP POST operations for your Todo API server using Swagger. Creating the endpoint, passing the todo in the request body, and parsing the todo are all demonstrated.

Define an HTTP POST Endpoint for a Node.js Server with Swagger

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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