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AI_LOG.md

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+# AI_LOG.md
+
+# AI Collaboration Log
+
+This document summarizes how AI tools were used during the development of this project, the decisions made, and the lessons learned throughout the implementation.
+
+---
+
+# AI Tech Stack
+
+The following AI tools were used during development:
+
+- ChatGPT (GPT-5.5)
+- GitHub Copilot (VS Code Chat)
+- Google Gemini
+
+Each tool was used for different purposes, including brainstorming, backend implementation, debugging, architecture discussions, and documentation.
+
+---
+
+# Development Journey
+
+## 1. Choosing the Frontend
+
+Initially, I was unsure whether to build the frontend using React or a simple static HTML page.
+
+Since the assignment required an MVP and the frontend only needed to display monitoring results and receive WebSocket updates, I decided to keep it simple and use a static HTML/JavaScript frontend served through Express.
+
+This reduced unnecessary complexity and allowed me to focus more on the backend implementation.
+
+---
+
+## 2. Designing the Project Structure
+
+One of my first prompts to GitHub Copilot was to generate a project directory structure.
+
+The initial suggestion contained many folders, services, configuration files, and abstractions that felt unnecessary for a small MVP.
+
+Instead of following that structure directly, I simplified it significantly and ended up with a much smaller and easier-to-maintain project.
+
+Final structure:
+
+```text
+backend/
+    database/
+    models/
+    routes/
+    utils/
+    app.js
+    web_socket_server.js
+
+frontend/
+    index.html
+    scripts.js
+```
+
+This structure was sufficient for the assignment while remaining easy to understand.
+
+---
+
+# Prompts That Shaped the Project
+
+Examples of prompts used during development include:
+
+- "Generate a simple folder structure for a Node.js URL monitoring application."
+- "Implement a WebSocket server that broadcasts URL health updates."
+- "How should I organize Express routes and controllers?"
+- "How can I dynamically update HTML using WebSockets?"
+- "Generate a Docker Compose configuration for this application."
+- "Create a professional README for this project."
+
+These prompts were used as starting points rather than copied directly into the final implementation.
+
+---
+
+# Course Corrections
+
+AI significantly accelerated development, but many generated solutions required refinement before they became production-ready.
+
+## Simplifying the Architecture
+
+The first suggested architecture was considerably more complex than necessary for the scope of this assignment.
+
+I simplified the project by reducing unnecessary layers and keeping only the components required for the MVP.
+
+---
+
+## WebSocket Communication
+
+While implementing WebSockets, I learned the difference between sending messages to a single client versus broadcasting to all connected clients.
+
+For example:
+
+- `ws.send()` sends data to a single connected client.
+- Iterating through `wss.clients` and calling `client.send()` broadcasts updates to every connected client.
+
+This understanding helped implement real-time monitoring correctly.
+
+---
+
+## Asynchronous Processing
+
+During implementation, I initially used asynchronous iteration in a way that caused the "finished" event to be emitted before all URL checks had completed.
+
+After debugging, I changed the implementation to wait for all monitoring requests before broadcasting the completion event.
+
+---
+
+## File Path Management
+
+Initially I used hardcoded relative file paths.
+
+Through AI discussions and debugging, I learned that using Node.js `path.join()` together with `__dirname` produces more reliable and portable file paths, especially when running inside Docker or from different working directories.
+
+---
+
+## Dynamic UI Updates
+
+Instead of requiring the user to refresh the page after adding a new URL, I implemented:
+
+- writing the URL into `urls.json`
+- broadcasting the update through WebSockets
+- dynamically adding the new row to the table
+
+This allows new URLs to appear immediately without refreshing the browser.
+
+---
+
+# Development Focus
+
+The project intentionally prioritizes backend engineering over frontend styling.
+
+Most development effort was spent on:
+
+- REST API design
+- WebSocket communication
+- Health monitoring logic
+- Monitoring history
+- Dynamic updates
+- Error handling
+- Dockerization
+- Project documentation
+
+The frontend was intentionally kept minimal because the primary objective of the assignment was demonstrating backend architecture and real-time communication.
+
+---
+
+# Key Learnings
+
+During this project I improved my understanding of:
+
+- Express application structure
+- REST API design
+- WebSocket communication
+- Real-time frontend updates
+- Broadcasting messages to multiple clients
+- Asynchronous programming in Node.js
+- Managing JSON-based persistence
+- File path handling using `path.join()`
+- Dockerizing a Node.js application
+- Structuring a maintainable backend project
+
+---
+
+# Reflection
+
+AI greatly accelerated development by helping generate initial implementations, explain unfamiliar concepts, and assist with debugging.
+
+However, every generated solution was reviewed, simplified where necessary, and adapted to match the project requirements. Rather than accepting AI-generated code as-is, I treated it as a collaborative tool to explore alternatives, understand implementation details, and make informed engineering decisions.