Learn Grep, Linux and Openapi

Amazon Web Services (AWS) is a powerful expansive cloud infrastructure platform for building modern web applications.

AWS provides services for data, presentation, authentication, security, video encoding, and much more. Offering over 175 fully-featured services from data centers globally, AWS powers a large percentage of the internet.

Now that we are SSH'ed into our EC2 instance we can start modifying the server to actually run the application that we want to to run. Before we do that we want to switch users because you initially SSH you are the root user.

We will start by adding a `ghost-admin` user that we will give sudo permissions to. Once we are logged into that user we will update the server to ensure we are on the latest and greatest versions before installing additional software.


Add and Log In to an AWS EC2 Instance with a sudo User

Node.js is a platform built on Chrome's JavaScript runtime for easily building fast, scalable network applications. Node.js uses an event-driven, non-blocking I/O model that makes it lightweight and efficient, perfect for data-intensive real-time applications that run across distributed devices.

node

Ghost has two hard dependencies which need to be installed before it can run. Those are Nginx and Node.js.

We will start by installing Nginx and set Nginx up to control the traffic coming into the EC2 server. After this, we'll install NVM using the raw install script that they provide on GitHub. With NVM installed we can then install the latest version of Node.js


Install Nginx and Node.js on a AWS EC2 Server

To run Ghost, we first need to install the ghost cli. This can be done with npm now that Node.js is installed on the server.

After installing the CLI we will create a directory `/var/www/ghost` that the actual installation of Ghost will occur in. We'll update the permissions of this file to include the `ghost-admin` user that we are logged in as.


Install Ghost CLI and Create Directory

“a11y” stands for “accessibility.” It is a numeronym, with 11 representing the count of letters between the letter a and the letter y.

Accessibility is broken down into 4 categories of disability: visual, cognitive, auditory, motor. 

Regardless of the category of disability, accessibilities aim in web development is to make applications and websites usable for everyone.

a11y

In this lesson, we'll apply our `jq` skills and write a script that gets a list of dependencies from package.json and greps for usages of every package. If a package is unused, it's marked for removal. 

Use jq and grep to Find Unused Dependencies in a Project

A Unix command used to search files for the occurrence of a string of characters that matches a specified pattern.

grep

When you have a bunch of text to sift through, standard Unix command line utilities like `grep`, `sed`, `awk`, `cut`, `sort`, and `uniq` can help extract the information you want. For example, with a git log for a project, you might want to group commits by day in order to see which days you're most productive, or find out how many commits you made last year. In this lesson, you'll learn how to combine these commands with the pipe operator to extract this data from git logs.

Analyze Git Logs with the Command Line

In this lesson, we’ll use `grep` to find text patterns. We’ll also go over some of the flags that `grep` has that can be combined together to make it more powerful and easier to read.

Search for Text with `grep`

Vim is a highly configurable text editor built to make creating and changing any kind of text very efficient. It is included as "vi" with most UNIX systems and with Apple OS X.

Every programmer starting to work in a *nix environment eventually finds themselves in VIM, the default text editor. Often by mistake! Because VIM lacks the typical polished GUI of normal programs, it can be hard to discover the correct sequence of commands to even exit the program. Once you learn how to exit VIM, then the next task is to also learn how to make VIM do what you want -while- exiting!

In this lesson we cover 7 different ways of exiting VIM, whether you are in EX mode or Command (also known as Escape) mode, whether you are editing a single file or several at once.

Exit Vim

By grouping your searches with parentheses, you can create even more complex searches. Grouping lets you separate out different parts of your regular expression, and apply special characters like +, ?, and * to groups of search terms.

Search with groups using grep

Learn to use the xargs Unix command to combine find and grep. First we can use find to identify which files we want to search; then we can use xargs to tell grep which files to search through.

Search matching files by combining find and grep with xargs

What happens when you want to find things that don't contain a pattern? The -v flag lets you use grep in inverse mode. We'll use grep -v against find's output to exclude files in node_modules.


Find matches excluding a pattern with grep

Learn to use git grep to only search through the tracked files in a git repo. This is especially useful when you want to exclude build artifacts or locally installed dependencies, such as webpack bundles or the node_modules directory. You'll also note that git grep is automatically colorized - we'll see how to get the same coloring effect with grep --color.

Search the contents of a git repository with git grep

By default, grep displays the file path and the line where a match is found. Learn to use the flags -A, -B, -C, and -n to get more context, and view line numbers as well as the lines surrounding a pattern match.


Show context around matches using grep

By using wildcards and glob patterns with the special characters . and *, you can create regular expressions that describe what kind of text you're looking for, instead of just plaintext strings. 

Search for basic patterns using grep

Learn to use grep's extended regular expressions to describe more complex patterns. The `?` and `+` special characters describe optional patterns. The `?` character matches zero or one instance of the preceding term, and the `+` character matches one or more instances of the preceding term. To use these characters, you'll need to either escape them with backslashes or turn on extended regular expressions with the `-E` flag.

Use grep's extended regular expressions to find complex patterns

Learn to use the special anchor characters `^` and `$` to indicate the beginning and end of lines when writing regular expressions for grep. These line anchors are part of basic regular expressions in grep, so you don't need to escape them.

Specify line beginning and end in patterns using grep

You can describe classes of characters using bracket expressions. We'll use expressions such as [a-zA-Z] and [0-9] to search for alphabetic and numeric characters with grep.

Match classes of characters using bracket expressions with grep

Describe optional patterns with grep OR using the vertical bar character `|`. By using the `|` special character, you can write either-or style patterns. In this lesson we'll look for matches on "grey" or "gray". Like the `+` and `?` characters, the `|` character is part of extended regular expressions with grep, so you'll either need to escape it with a backslash, or use the -E flag.

Search for optional patterns with grep OR

Sometimes you'll be looking for a string, but won't know which file it's in; or you'll want to find all usages of it within a directory. Learn how to use grep -r to recursively search all the files in a directory. 

Search directory contents recursively using grep

Learn how to use find to identify filenames matching specified patterns. We'll use find to identify all .jsx files in a directory, or all test files in a directory.

Use find to search for filename patterns

 Learn the basic syntax for using grep to search the contents of a single file or files. It's like CMD+F, but better! You'll be able to quickly see all matching results in a file.

Search the contents of files using grep

CMD+F in your text editor can only get you so far! In this course, we will learn to use grep and find, two powerful command-line utilities, to search through and explore our codebase. Learn to search specific files and folders, using regular expressions to look for -- or ignore! -- particular phrases. We'll cover how to search recursively, get more context on your results, and some simple tricks to make your searches faster and more targeted.


Once you're familiar with grep and find, you'll be able to quickly answer questions such as: Do I use this function anywhere other than my tests? Where does this file get imported? Whether you're working on a new feature, reading a codebase for the first time, or refactoring existing code, grep and find can help you work faster and more effectively.

Use Grep for Fast Search from the Command Line

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.

Search

Search Results