Learn Angularjs, Chai and Clojure

Clojure is a dynamic, general-purpose programming language, combining the approachability and interactive development of a scripting language with an efficient and robust infrastructure for multithreaded programming. Clojure is a compiled language, yet remains completely dynamic – every feature supported by Clojure is supported at runtime. Clojure provides easy access to the Java frameworks, with optional type hints and type inference, to ensure that calls to Java can avoid reflection.

clojure

Para iniciar con Clojure es necesario realizar algunas configuraciones e instalar algunas herramientas.
Lo primero es instalar [leiningen](https://leiningen.org) y configurar tu editor de código.
En este caso configuraremos VIM. Para esto instalaremos algunos ]plugins 

* [vim-fireplace](https://github.com/tpope/vim-fireplace)
* [vim-salve](https://github.com/tpope/vim-salve)
* [coc-conjure](https://github.com/jlesquembre/coc-conjure)

Configurar ambiente de desarrollo y VIM para Clojure

Angular is a development platform for creating applications using modern web standards. Angular includes a wealth of essential features such as mobile gestures, animations, filtering, routing, data binding, security, internationalization, and beautiful UI components. It's extremely modular, lightweight, and easy to learn.

angular

When migrating AngularJS (v1.x) applications to Angular you [have different options](https://github.com/angular/ngMigration-Forum/wiki/Migration-Paths-Overview). Using Angular Elements is one of them. In this lesson we learn how to integrate an Angular Element into an AngularJS (v1.x) application. We will leverage some features release in the latest AngularJS 1.7.3 that make it fully compatible with custom elements.

Use Angular components in AngularJS applications with Angular Elements

In this lesson I show what a Clojure keyword is and why we prefer them as keys in maps; and the three ways we can look up a value in a map.

Keywords and Map Lookup in Clojure and ClojureScript

We'll use Lumo, a cross-platform standalone ClojureScript compiler and Node.js environment, to create Hello as a command-line application. 

In this lesson, I’ll show you how to install Lumo through NPM, invoke a function in ClojureScript, and use the println function from the core library.

Compile and Run a ClojureScript Command-line App on Node.js with Lumo

AngularJS is an open-source JavaScript framework, maintained by Google, that assists with running single-page applications. Its goal is to augment web-based applications with model–view–controller (MVC) capability, in an effort to make both development and testing easier.

angularjs

Many times while building an AngularJS application you'll need to manipulate data, and it doesn't make sense to do it with a Controller method. Luckily you have Filters, which are particularly well suited for manipulating text within your HTML views. This handy tool uses a simple syntax to create highly reusable functionality for your apps.

Filters

Chai is a BDD / TDD assertion library for node and the browser that can be delightfully paired with any javascript testing framework.

chai

In this lesson, you will learn how to verify your API server is returning HTTP 400 responses when clients submit incorrect data. Returning HTTP 400 ensures that your clients are notified of the incorrect usage. Testing for them ensures your API returns errors instead of incorrect responses when supplied with incorrect data.

Test for HTTP 400 responses with Mocha and Chai

In this lesson, we will use Chai's request method to test our Node application's API responses.
By the end of this lesson, you will know how to:
- install the prerequisites to use mocha and chai in your application
- test for HTTP status response codes
- test for a string of text on a page
- test for a json response and validate the properties of the object
- write tests that not only verify the response of your application, but the behavior as well


Test Node RESTful API with Mocha and Chai

Mocha is a feature-rich JavaScript test framework running on Node.js and in the browser, making asynchronous testing simple and fun. Mocha tests run serially, allowing for flexible and accurate reporting, while mapping uncaught exceptions to the correct test cases.

mocha

Writing great ES6 style Promises for Node.js is only half the battle. Your great modules must include tests as well to ensure future iterations don't break them. In this lesson, I show you a simple ES6 Promise in Node.js, then walk you through creating tests in Mocha using chai and chai-as-promised to test both resolve and reject methods.

Testing ES6 Promises in Node.js using Mocha and Chai

Manually creating action items every time we need to dispatch an action eventually accumulates to a very tedious process. In this lesson, we will learn how to abstract out the creation of action items so that we consolidate their creation into a single place. This not only creates efficiencies in how we express code but also reduces code volume while simultaneously increasing stability. 

Angular 1.x Redux: Create Reusable Actions with Action Creators

In this lesson, we are going to learn how to enable our store to work with more than one reducer in an application.

Currently, we have set up our store to work with the categories reducer exclusively, but this leaves us with a real problem, what if we want to keep track of the currently selected category within our store so that we can access that data somewhere else in the app? It is safe to say that most applications consist of many data models interacting together, and we need a way to accommodate this within the single state tree in our application.

When we initialize our app store, wouldn't it be convenient if we could take all of our reducers and combine them into one big reducer that our store knows how to work with? Well, this is exactly what the combine reducer method does in Redux. It allows us to combine reducers together and then access them as a named property on our application store.

We currently have a category reducer just hanging out here doing nothing because we have been focusing entirely on the categories reducer. We are going to combine the two reducers so that we can use the current category data within our bookmarks component.

Angular 1.x Redux: Handle Multiple Models by Combining Reducers

Invariably the question that comes up when talking about Redux is how does one handle asynchronous operations in redux. For instance, how do we hand off an operation to redux that requires a remote call to the server? Where exactly does the async part get handled? 

Redux has this concept of middleware that allows us to insert custom logic in the space between dispatching an action, and the moment it reaches the reducer. In this lesson, we are going to set up a few async operations and then use redux thunk middleware to make sure that everything makes it into the reducer properly.

The main key to thunk middleware is that it allows us to return a function instead of an action. This function can encapsulate the async operation and dispatches the appropriate action when the operation is completed.

Angular 1.x Redux: Handle Asynchronous Operations with Middleware

One of the primary advantages of using redux is that reducers are incredibly easy to test. By nature, they have a clearly defined contract of inputs and outputs, and because they are stateless, they require very little instrumentation to test in isolation. 

We will start with a few simple tests for the categories reducer and then move on to testing the CRUD functionality within the bookmarks reducer. 

Angular 1.x Redux: Test Reducers

Up to this point, we have created an effective, yet rudimentary, implementation of Redux by manually creating an application store, reducers and action creators. We have already made significant progress in simplifying state management within our application, but we have in fact only touched the surface of what Redux has to offer.

In this lesson, we are going to introduce ngRedux into our application and let it handle the rest of the heavy lifting as we work through this series. ngRedux is a project that provides angular bindings to redux to streamline integrating it into our application.

Angular 1.x Redux: Integrate ngRedux

In this lesson, we are going to learn how to integrate Redux Devtools into our Angular application.

Redux Devtools is a live-editing time travel environment for Redux. Devtools boasts a list of awesome features but my two favorite ones are that we can inspect every state and action as it happens and we can go back in time by “cancelling” actions.

This is going to be an interesting lesson because, in order for this to work, we are going to need to make something that was written for React work in Angular. For the most part, everything will play side by side with one small trick that we will pull off at the end to force an Angular digest cycle when React manipulates the application store. 

Angular 1.x Redux: Integrate Redux Devtools

One of the biggest mental shifts that developers have to take when embracing redux is shifting from mutable operations to immutable operations. The problem with mutable operations is that they create side effects that involve anything else that is referencing the thing that is being mutated.

With immutable operations within a reducer, a new object is returned for every operation – completely bypassing the possibility that an existing reference is going to be compromised. It is then up to the consumer to decide how they want to get the new data and what to do with it.

In this lesson, we are going to flesh out the CRUD functionality of our bookmarks reducer, doing it first using mutable operations and then refactoring it to use immutable operations.

Angular 1.x Redux: Avoid State Mutation with Immutable Operations

In this lesson, we are going are going to apply everything that we have learned by building out our bookmarks component to be powered by Redux. This will help reinforce all the techniques we have covered by seeing them built out  in a single, contiguous lesson.

We will walk through the steps necessary to phase out the bookmarks model with a Redux implementation that includes building and integrating the equivalent reducers and action creators to integrate into our application.

Angular 1.x Redux: Put It All Together

In this lesson, we are going to learn how to map our Angular component directly to our application store using the connect method on ngRedux. 

In Angular, it is a common technique to bind a controller property directly to the model so that our controllers remain lightweight and operate more as a pass through mechanism than anything else.

Using the connect method, we can accomplish the same effect by not only binding properties directly to the application store, but also binding our controller to our action creators. This allows us to drastically simplify our controllers as we are able to delete a bunch of local controller methods as they become unnecessary because of our mappings.

Angular 1.x Redux: Map State and Dispatchers to Redux

In this lesson, we are going to extend our application store with a subscribe method so that we do not have to manually query our application store every time we dispatch an action.

In our categories controller, we are dispatching a **GET_CATEGORIES** action every time we want to update the categories collection within our application store. For this change to be propagated back into our controller, we need to call **store.getState**. 

Depending on how many times we are calling **store.dispatch**, this could get very cumbersome. We will extend our application by adding a subscribe method so that state changes are automatically pushed into our controller.

Angular 1.x Redux: Subscribe to the Application Store

In this lesson, we are are going to learn how to abstract out state mutations into a reducer. In our current Eggly application, all of the state and business logic for our categories is handled within the categories model. We are going to refactor our code to entirely eliminate the need for our categories model by building out a couple of reducers and seeing how we can interact with them directly. 
The key thing to notice as we build out our reducers is that a reducer itself is completely stateless because it receives current state every single time it needs to calculate and return the new state. With a clearly defined contract of inputs and outputs, this makes testing reducers really easy which is one of the huge benefits of using this approach. 

Angular 1.x Redux: Handle State Mutations with Reducers

In this lesson, we are going to learn how to build an application store so that we can have a single place to store our application state. Reducers are great for updating state because they are stateless, but they are very bad at preserving state, which is vital when you need to reference state in more than one place.

We will build out a simple application store that will be responsible for keeping track of our categories collection that our categories controller can consume.

Angular 1.x Redux: Persist State with an Application Store

Welcome to the series where we are going to learn how to integrate Redux into an existing Angular application.

One of the most challenging aspects of programming is state management. The complexity involved with managing state exponentially increases with each moving piece that we introduce. For every piece of shared, mutable state that we have in our application, there is an opportunity for unintended behavior. For instance, let us say that we have a collection that is shared between two controllers; how do we modify it from one controller with absolute certainty that it isn't going to adversely affect the other?

This is where Redux comes riding in on a white horse and saves the day! By applying Redux to our application, we consolidate all of our states into a single JavaScript object called a store that can only be modified by dispatching an action. To further simplify things, state mutations within our store are handled by pure, stateless functions called reducers. 

Redux was initially created for React, but it is in fact, a really solid programming pattern that we can easily apply to our Angular applications. The beauty of Redux is its simplicity in so much that someone could create an implementation of Redux in less than 10 minutes. This is exactly what we will do in the first couple lessons as we build out our reducers, actions and application store by hand before we introduce ng-redux to handle the rest of our abstractions. 

We will also learn how to combine reducers, introduce middleware for async operations, map state directly to our actions, test reducers and even hook up Redux devtools. By the end of the series, you will have the tools in your hands to tame state management within your angular application while reducing the overall complexity thanks to the simplicity of Redux.

Angular 1.x Redux: Introduction

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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