Project Summary: VZBZ is a custom Discord bot I engineered to solve key operational and engagement challenges in the Eight or Infinity community. It combines essential server management utilities with a prototype for an advanced, context-aware AI agent. The project showcases my ability to build full-stack applications that integrate AI, manage community dynamics, and streamline user workflows.

As the Eight or Infinity community grew, we faced common scaling challenges:

1. Moderator Burnout: Repetitive tasks like digital “room building” and managing channel hygiene were becoming a significant time sink.

1. Maintaining Culture: Ensuring new members felt welcomed and active participation was recognized became harder to do manually.

1. Fragmented Tooling: We needed a single, cohesive tool to support collaborative projects hosted on the server.

I designed and built VZBZ from the ground up to be a central nervous system for the server, developed in two main stages.

This phase moved beyond simple commands to explore true AI-driven interaction.

• Contextual Grounding (RAG): I connected the bot to my custom LLM gateway and implemented a Retrieval-Augmented Generation (RAG) pattern. The AI is fed our server's guides and announcements, enabling it to answer user questions with accurate, context-specific information.

• Interactive Persona: The Agent was given a distinct personality and backstory. Using my n8n engine, I created a workflow that allows the bot to participate in conversations and even generate relevant images on command, making it feel like a true community member.

I designed the bot's architecture with a rapid local prototyping stage and a clear path to a resilient production deployment.

The current environment is optimized for rapid iteration and feature development.

• Core Application: A TypeScript application running on the Node.js runtime, using the Discord.js library for all core Discord API interactions.

• Execution & Orchestration: Instead of running as a monolithic, always-on process, the bot is invoked on-demand. My n8n engine uses its "Execute Command" node to run the tsx command, which compiles and executes the TypeScript entry script instantly. This event-driven approach is highly efficient, keeping the bot's code modular and enabling real-time debugging without a complex deployment pipeline.

• AI & Automation Backend: Core bot commands are handled locally, while complex, multi-step tasks like AI-powered analysis or image generation are offloaded via webhook to my Personal Automation Engine (n8n). This keeps the primary bot application lightweight and modular.

To evolve VZBZ from a prototype to a 24/7 production service, the roadmap includes two primary options:

1. Containerized Self-Hosting: The application will be containerized using Docker and deployed to a dedicated, low-power device like a Raspberry Pi for a cost-effective, always-on service.

1. Cloud Deployment: For greater scalability, the Docker container can be deployed to a VPS cloud platform like Heroku, Render, Railway, or AWS Lightsail.

The ultimate goal for VZBZ is to evolve from a bot into a personalized companion for community members. The next strategic steps are:

• Long-Term Memory: Implement a vector database to save key details from conversations, allowing the bot to build "relationships" and recall user preferences.

• Proactive Assistance: Develop logic for the bot to proactively offer help or information based on the context of a conversation, rather than just reacting to commands.

This project demonstrates my ability to take a product from concept to functional MVP, making informed design decisions based on both user needs and technical requirements.