AI Home Directory

Welcome to my AI Home Directory - a centralized knowledge base and context repository that serves as the foundation for all my AI-assisted development work.

Overview

This repository functions as my personal "AI home directory" that gets mounted into all development project stacks (containers). It provides critical, consistent context across all projects while being combined with project-specific prompts as needed.

Purpose

After a year of heavy AI utilization and several weeks working with AI CLI agents (Qwen, Gemini, Claude Code, Codex), I've developed a portable and reproducible development environment using Docker. This repository represents the constant, foundational context that every project needs access to.

Repository Structure

AboutMe.md

Personal information and professional background that provides context for my work.

AgentRules.md

Operational guidelines for AI agents, including interaction rules, workflow patterns, and technical guidelines.

StartHere.md

Onboarding guide explaining the purpose of this directory and how it fits into my development workflow.

TSYS.md

Business information about my organizational structure and related entities.

AUDIT1.md

Comprehensive analysis and improvement suggestions for my AI workflow and documentation.

AGENTS.md

Guidelines for AI agents working with this repository, including commit conventions and workflow instructions.

Architecture

This repository was previously a combined collection of my personal "AI databank" and Docker artifacts. The Docker artifacts have now been moved to a dedicated repository, leaving this as the pure "AI home directory" that maintains consistent context across all projects.

Workflow

1. This directory is mounted into all development containers as a consistent knowledge base
2. Combined with project-specific context and prompts as needed
3. Provides continuity and reduces repetitive explanation of context to AI agents
4. Maintains consistent operational patterns across different projects

Benefits

• Consistency: All projects have access to the same foundational knowledge
• Efficiency: Eliminates need to repeatedly explain context to AI agents
• Scalability: Works seamlessly with multiple ongoing projects
• Maintainability: Centralized updates propagate to all projects automatically