Learn Dart, Gatsby and Mongodb

This course provides a practical introduction to MongoDB, a leading NoSQL document database. You will learn how to set up a local MongoDB environment using Docker and then dive into essential database operations. By the end of this course, you will know how to:

- Create and manage MongoDB collections.
- Insert, filter, update, and delete documents.
- Perform complex queries using logical operators and expressions.
- Manipulate data within arrays and nested fields.
- Rename fields and manage date and time properties.
- Execute multiple operations efficiently using bulk operations.

Effectively manage and interact with MongoDB databases. Being proficient with databases is a crucial skill in modern app development.

Tomasz Ducin

Get Started with MongoDB

Learn MongoDB setup with Docker, manage collections, documents, queries, data manipulation, and bulk operations. Essential for modern app development.

In this lesson, we learn that replacing a document in a MongoDB collection is done using the `replaceOne` method, which does **not** have a _replaceMany_ equivalent.

We demonstrate how to search for a specific document, such as one with the name "Alice Smith," and replace it with a new document containing updated information like "John Doe."

This operation completely removes all existing fields from the original document and replaces them with those in the new document. After running the operation, we verify that only the new data remains in the collection.


Replace Documents in MongoDB Collections using replaceOne

Replace documents in MongoDB collections using replaceOne to completely overwrite existing documents with new data based on specified queries.

In this lesson, we learn that we can store datetime properties in MongoDB documents using the `$currentDate` operator.

We demonstrate how to create a new date property for a document, such as one for "Bob Johnson," by simply applying the operator.

We also show how to update multiple properties at once and set them as either Date or timestamp types.

By using `$currentDate`, we can easily manage and modify date-related information in our MongoDB documents, ensuring accurate time tracking.

Create or Modify Datetime properties using $currentDate in MongoDB

Create or modify datetime properties in MongoDB using $currentDate to set fields as Date or timestamp types for efficient date management

In this lesson, we learn that renaming fields in MongoDB documents can be accomplished with the `$rename` operator.

We demonstrate how to use the `updateOne` method to target a specific document, such as one with the name "Bob Johnson."

By specifying the old field names and their new counterparts, like changing position to title and skills to competence, we can effectively update our document structure.

Rename fields in MongoDB Documents

Rename fields in MongoDB documents using the $rename operator with updateOne or updateMany for efficient data management

In this lesson, we learn that updating arrays within MongoDB documents involves using the `$push` and `$pull` operators.

The `$push` operator allows us to add new items to an array, while combining it with the `$each` operator enables us to add multiple items simultaneously.

We demonstrate how to add "Java" and ".Net" to the skills array.

Conversely, the `$pull` operator removes elements from an array, and we can use the in operator with `$pull` to remove multiple items, such as "Python" and "SQL," based on specified criteria, providing flexibility in array manipulation.

Update Mongo Documents arrays using $push, $pull and $each operations

Update MongoDB arrays with $push and $pull: add single/multiple elements using $each and remove elements using in with $pull.

In this lesson, we learn that updating documents in MongoDB involves using the `updateOne` and `updateMany` methods along with operators like `$set` and `$unset`.

The `$set` operator allows us to modify existing properties or add new ones, even in nested structures, while the `$unset` operator removes specified properties from a document.

We demonstrate how to use these operators to change a document's salary, modify nested personal information, and remove fields like position.

We also show how to combine `$set` and `$unset` in a single operation for efficient data management.


Update Mongo Documents using updateOne, updateMany, $set and $unset operations

Update MongoDB documents with $set and $unset operators: modify, add, and remove properties, including nested fields, in a single operation.

In this lesson, we learn that when querying MongoDB collections, we can control which properties are returned by using projections with the `findOne` method.

We demonstrate how to include or exclude fields by setting them to 1 or 0, respectively. For example, we can choose to include the name and position while excluding the `_id` field.

We also cover how to access nested properties and project specific elements from arrays using the `$slice` operator, allowing for more precise data retrieval based on our needs.

Return a subset of properties of a MongoDB Document (projections, $slice)

Return specific properties from MongoDB documents using projections in the findOne method, including nested fields and array slicing, $slice

In this lesson, we learn that counting documents that meet certain criteria in MongoDB can be done with the countDocuments() method.

We demonstrate how to retrieve the total number of documents when no query is specified and how to filter results using regular expressions.

For example, we can count employees with names containing a lowercase "a" or those with specific skills, using `$regex`.

Additionally, we show how to count documents where age is not equal to a given value, illustrating the versatility of the `countDocuments()` method in various scenarios.


Count documents satisfying a query in MongoDB Collection (countDocuments, $regex, $in)

Count documents in MongoDB collections using countDocuments() with query filters, including regex and specific conditions, using $regex, $in

In this lesson, we learn that searching for a MongoDB document by its ID can be a little tricky in the beginning.

We demonstrate how to use the `_id` field to search for specific documents and emphasize the necessity of wrapping the ID with the `ObjectId` constructor. This step is crucial because failing to do so can result in empty results, underscoring the importance of proper syntax when querying by document ID.


Find Document by id in MongoDB Collections

Find MongoDB documents by their unique _id using the ObjectId constructor without mistakes.

In this lesson, we learn that we can perform advanced filtering in MongoDB by using expressions within our queries. All thanks to `$expr`.

We demonstrate how to compare multiple document properties, such as ensuring age is less than salary using comparison operators (`$lt`, `$lte`, `$gt`, `$in`, `$nin`, etc).

Additionally, we explore filtering based on the length of an array (`$size`) to check for specific skills.


Filtering Documents using Expressions in MongoDB Collections ($expr, $size, $in, $and, etc)

Filter MongoDB documents using expressions $expr with comparison operators $lt, $gt, $eq and array length checks $size

In this lesson, we learn that combining multiple criteria in MongoDB queries can be done using logical operators such as `$and` and `$or`.

We demonstrate how to create queries that filter documents based on various conditions, such as _age_ and _skills_.

Additionally, we explore the use of the `$in` operator for matching any values in an array and the `$all` operator for ensuring all specified values are present.

This lesson provides essential skills for effective use of operators such as: `$in`, `$nin`, `$lt`, `$lte`, `$gt`, `$gte`, `$eq`, `$ne` (and many others) in querying MongoDB collections.



Combine Filtering Documents in MongoDB Collections ($and, $or, $in, $eq, etc)

Combine filtering criteria in MongoDB with logical operators like $and, $or; use $in and $all for array filtering in this lesson on document queries.

In this lesson, we learn that filtering documents from MongoDB collections can be achieved through the `find` and `findOne` methods.

We demonstrate how to use these methods to retrieve documents based on specific criteria, such as names or emails, and how to apply regular expressions for more general searches.

Additionally, we explore filtering by array values and using conditions like less than or greater than for numerical properties.


Filtering Documents in MongoDB Collections (find, findOne)

Learn to filter MongoDB documents using find and findOne methods with query criteria, regex, and array filtering in this lesson on document retrieval.

In this lesson, we learn that inserting documents into a MongoDB collection can be done using the `insertOne` and `insertMany` methods.

We demonstrate how to create new employee objects in JSON format and add them to a collection.

We also show how to view the inserted documents and their automatically generated IDs.

Furthermore, we discuss how to delete documents from a collection using filtering criteria.


Inserting Documents into MongoDB Collections (insertOne, insertMany)

Insert documents into MongoDB collections using the Mongo shell mongosh and MongoDB Compass, using the insertOne and insertMany methods

In this lesson, we learn that creating a MongoDB collection can be done easily through MongoDB Compass or the Mongo shell: `mongosh`.

We cover how to use the GUI to add a new collection and understand that there’s no need for a predefined schema, allowing us to store documents with varying structures.

We also discuss how to remove a collection when it’s no longer needed, demonstrating both methods for flexibility in managing our database.

We also see `find`, `countDocuments` and `drop` MongoDB collection methods in action!


Create or Remove MongoDB Collection

Learn to create and remove MongoDB collections using MongoDB Compass and the Mongo shell in this practical lesson on collection management.

In this lesson, we learn that MongoDB Compass is a graphical user interface for managing MongoDB databases.

We cover how to download and install Compass, create a connection to our local MongoDB instance, and troubleshoot any connection errors.

Additionally, we explore how to navigate the interface to view collections and documents easily.

This lesson provides a practical understanding of using Compass for MongoDB database management.



Access Mongo databases from MongoDB Compass

Learn to access MongoDB databases with MongoDB Compass. Install, connect, and explore data visually, locally and free.

Get started with MongoDB and Docker really quick 🔥 even if you have no backend/fullstack experience.

This lesson guides you through installing Docker Desktop and setting up a MongoDB image.

We explain Docker images and containers, and their roles in providing isolated software environments.

We run a MongoDB image in 2 ways: using the terminal and Docker Desktop

We demonstrates configuring port settings for a container and accessing the MongoDB shell (`mongosh`) to interact with the database.

We learn how to manage Docker containers, configure settings, and run commands inside the container.

Grab the CLI command: 🔥 `docker run --name some-mongo -d mongo:latest` 🔥

Install Docker to run local MongoDB Container

Learn to install Docker Desktop, set up a MongoDB image, and manage containers. Configure ports and access the Mongo shell

Gatsby is a blazing fast static site generator built with React. Gatsby gives you all of the modern JavaScript tools you want; set up and ready to go out of the box.

gatsby

This lesson walks through how to update and query frontmatter data for Gatsby blog posts in order to display featured articles separately from recent articles.

Display Featured Articles through Frontmatter Properties on a Gatsby Blog

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

It's possible to pass configuration parameters to your Gatsby plugin. One use case is just allow the plugin to be used multiple times creating different queries that will be available for your site.

To do this you need to refactor the origin plugin code to accept this parameters and update the `gatsby-config.js` file to set them there.

Use configuration parameters for a Gatsby source plugin

Es posible pasar parámetros que ayuden a configurar tu plugin de Gatsby. Uno de sus caso de uso es permitir que el plugin se utilice multiples veces creando diferentes consultas que estarán disponibles para tu sitio.
Para esto es necesario refactorizar el plugin original para aceptar estos parámetros que son configurados mediante el archivo `gatsby-config.js`

Utilizar parámetros de configuración en un plugin para Gatsby

The plugin that you just implemented, each created node contains an attribute call `artwork_url` that references an URL for an external image. Is possible to directly use that URL to load the images in your site, but the images can big big and take too much time to load. You can optimize this images directly in your plugin to allow the site that use it to drop it directly into `gatsby-image`.


To accomplish the goal of optimizing the external images, your plugin will have to create a new type of node. This new file node will allow the user of your plugin to harness the power of the plugins `gatsby-plugin-sharp` and `gatsby-transformer-sharp` to perform transformations over the image field.


Optimize Remote Images in a Gatsby Source Plugin

En el plugin que has implementado, cada nodo creado contiene un atributo `artwork_url` que hace referencia a la url de una imagen externa. Es posible usar esa URL directamente para cargar las imágenes en tu sitio, pero estas pueden ser muy grandes y tomar mucho tiempo en cargar. Pero es posible optimizar dichas imágenes directamente en el plugin para que el sitio que utilice tu plugin pueda utilizar directamente  `gatsby-image`.


Para lograr optimizar las imágenes externas tu plugin tendrá que crear un nuevo tipo de nodo. Este nuevo nodo tipo archivo permitirá al usuario de tu plugin usar los plugins `gatsby-plugin-sharp` y `gatsby-transformer-sharp` para realizar transformaciones en la imagen.


Optimizar images remotas en un plugin fuente para Gatsby

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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