Learn Greensock, Ramda and Strapi

**Developers aren’t the only ones working on sites.** 

Marketers, copywriters, designers, and other non-coding roles all need to be able to make content changes. If you don't want to be bothered to open a PR every time a typo needs fixed, you *need* a CMS. 

And Strapi is an *amazing* CMS option.  

Strapi helps you quickly build a robust content API. You can host it yourself, it supports both RESTful and GraphQL, handles your authentication, lets you bring team members and it's highly customizable with plugins and extensions. 

You can take those gains even further by adding **Next.js 13 to the stack.** 

Next.js is fast, supports both server and client-side rendering, and features dynamic routes. Once you are generating dynamic pages from your CMS you’ll see just how powerful the combination of Strapi and Next.js is!

**In this course you will do the following:** 

- Building Content Models with Strapi
- Fetching Data from your Headless CMS
- Using React Server Components
- Using the Next.js App directory
- Setting up pagination in Strapi and Next.js
- Parsing and rendering content in your Next.js application
- Connecting external services to your Headless CMS (like Cloudinary for Media Management)
- Deploying your Next.js and Strapi Applications to a production environment
- Setting up and using Strapi Plugins
Transferring data between your Strapi environments

By the end of this course you’ll be building pages and managing your content like a pro!

strapi

Modern CMS Driven Web Applications with Strapi and Next 13

There is a lot of extra functionality that you can import into your Strapi application through Strapi plugins. 

To demonstrate how easy this is, we'll install the `strapi-todo-plugin` and add a To-Do feature to our Strapi application that will help manage our content.

Install and Use a Strapi Plugin

Inviting users to your Strapi application is simple. First, we'll create a role for editors so they can't publish or delete content then we'll add the user and fill out name and email. This gives us a URL that we can send to the new collaborator and they can set their password and log in to the Strapi dashboard.

Invite a New User to Your Strapi App

When a new Strapi version is available, you'll see a notification in your admin panel. You can also check the [Strapi GitHub releases page](https://github.com/strapi/strapi/releases) that is linked in the notification to see what's new in the latest version and take note of the latest version number. 

Once we upgrade the Strapi dependencies and install again, you'll notice the notification is gone and when you check settings the version number has been updated. To update production, you push your changes to GitHub and run `git push heroku HEAD:main`.

Upgrade a Strapi App in Development and Production

There are times that when deploying your Strapi application you'll want the data and settings you developed with to be transferred to your production environment. To do this we can use Strapi's data transfer feature. 

To start the data transfer we will need to log into the production environment and create a transfer token. When the token is created we will just need to run `yarn strapi transfer --to :heroku-url` and paste the transfer token when prompted. You'll notice that all the data is present in your production enviroment as well as the roles you set up in development.

Transfer Data Between Environments with Strapi Data Transfer

Now that we've got a functioning application it's time to deploy. For Strapi, we'll use Heroku to deploy for production. To do so we will need to create a production environment file for `database.js` and `server.js`, then we log into Heroku and created a new app. Once logged in we will set all the environment variables in Heroku. Finally, we pushed our code to Heroku and saw our application running live on the web.

Deploy a Strapi Application to Heroku

cloudinary

We'll learn how to integrate Cloudinary into our Strapi application so that we can upload image assets in our Strapi dashboard and the resulting image will be stored in Cloudinary and ready for use in our application.

We'll create a `config/plugins.js` file and paste in some config code that sets Cloudinary as the provider with `cloud_name`, `api_key`, and `api_secret`.

Then we'll install the Cloudinary provider plugin with `npm install @strapi/provider-upload-cloudinary`, add the middleware security that lets us use Cloudinary, and finally add Cloudinary credentials to our `.env` file.

Install and Add the Cloudinary Provider to a Strapi Application

We'll cover the basics of building a new page in Next.js, specifically an "About Us" page. If you've used Next before, this will feel very similar to creating a new page in the `pages` directory. 

While doing this we will also be introduced to some of the components that come with the template, such as the `PageContainer`, `Main`, `Title`, and `Description` components which you can check out [in more detail here](https://github.com/malgamves/next-13-website-template/tree/main/components).


Create an About Us Page inside the Next.js 13 App Directory

GraphQL is a powerful technology that lets you query the exact data that you need when you need it on your front end. Strapi allows for GraphQL integration through a plugin.

You can install GraphQL with the following command:

```bash
yarn strapi install graphql
```
You'll notice this adds GraphQL in your plugins tab within Strapi. 

When that is installed you can navigate to `/graphql` in your Strapi project and query the data you have. 

Install and Query GraphQL API in Strapi

We have a problem right now in that if you were to try and fetch these posts from the API, you would get a 403 error. And that's because we haven't set up the end user permissions for the API to be able to access this content. So let's go ahead and do that now.

We'll modify the User permissions for the `Post` role and give public access to `find` and `findOne`.

Set up API End User Permissions to Make REST Queries in Strapi

The first thing we want to do is model the content that will be presented on our website. To do this we'll create a Post content type that will have several fields with types including text, rich text, date, and UID. 

After we create the content type you'll see how to manually create posts through the Strapi dashboard.

Create a Collection Type for a Post in Strapi

We'll start with the basics of creating a new Strapi project, naming the project API, and setting up the project to use an SQLite database.

Creating a New Strapi Project for Development with npx

Ultra high-performance, professional-grade HTML5 animation for the modern web

greensock

The `gsap.set()` tween (or animation container) is a zero-duration tween that instantly applies a property to an HTML element. In addition, there is a subtle but important difference between `opacity` and Greensock's `autoAlpha` properties. You will learn how to hide a menu on page load and also how to prevent the element from flashing on page load. This lesson contains Greensock's basic building blocks that are used to create advanced animations with UI elements.

To add the latest Greensock library to your project, go to [Greensock's website](https://greensock.com/). Fun fact: GSAP stands for Greensock Animation Platform, and their current latest version is GSAP 3!

A common programming convention for Greensock I like to follow is, if you're going to animate a property with Greensock, keep it in Greensock, otherwise, if you are not going to animate a property, keep it in your CSS.

Hide a Menu with GSAP3's set() Tween and autoAlpha Property

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

GreenSock supports all of the CSS3 3d properties, so you can freely rotate around X, Y, and Z axises with rotationX, rotationY, and rotationZ. Once that's set up, you can tweak the perspective of the element with transformPerspective.

Spin Elements in 3d with GreenSock

By default, elements use their own perspective, so if you rotate multiple elements on the page the perspective will seem a bit off for each of them. The solution is to set the perspective on the container element, then each child element will follow the parents' perspective.

Control the Shared 3d Perspective of Multiple Elements with GreenSock

Sometimes you want to leave an animation looping without any user interaction. This is done using the "repeat" and "yoyo" properties combined to keep the tween moving back and forth from the start properties to the end properties.

Loop a Tween Forever Using Yoyo and Repeat with GreenSock

Modern development requires modern tooling. Rather than adding GreenSock as a script to an HTML page, we're going to install GreenSock using Yarn and set up our development environment using Parcel. This will allow us to quickly iterate and test out our animation ideas. GreenSock is abbreviated as `gsap` when importing.

Setup GreenSock as a Module with Parcel

Animating an element to a click event is as simple as passing the x and y coordinates of the click event to the properties of the `TweenMax.to` call. To be able to center the element where the mouse clicks, you also need to adjust the special xPercent and yPercent properties so the element is properly offset.

Animate and Center an Element to a Click Event with GreenSock

Rotating an element with GreenSock is as simple as changing the `rotation` property using `TweenMax`. But there's also a hugely convenient trick using the `+=` or `-+` syntax as a string that allows you to adjust based on the previous value of the rotation.

Rotate an Element Based on Previous Values with GreenSock

Animations often have multiple steps. GreenSock makes this simple with the concept of a timeline where you code each point you want your element to move to. This lesson will show you how to set up a timeline as well as pause and resume the timeline animation.

Create Animation Steps with GreenSock's Timeline

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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