Quick Start Guide

Get the Athena distributed AI system running in under 5 minutes and see research through practice in action.

Prerequisites

• Elixir 1.15+
• Phoenix 1.7+
• Git

1. Start the Chat System

# Navigate to the main application
cd ash_chat

# Install dependencies  
mix deps.get

# Start the Phoenix server
mix phx.server

2. Access the Interface

Open your browser to: http://localhost:4000

You’ll see:

• Chat - Multi-user AI chat with agent personalities
• Events - Real-time event monitoring and inspection
• System - System status and controls

3. Create Demo Data

# In a new terminal, start an IEx session
cd ash_chat && iex -S mix

# Create demo users, rooms, and AI agents  
AshChat.Setup.reset_demo_data()

# Test the multi-user system
AshChat.Setup.quick_test()

4. Explore the Dual Purpose System

A. Practical System: Distributed AI

Event Processing:

# Send a test event to the system
curl -X POST http://localhost:4000/webhook/test \
  -H "Content-Type: application/json" \
  -d '{
    "timestamp": "'$(date -u +"%Y-%m-%dT%H:%M:%S.000Z")'",
    "type": "motion.detected", 
    "source": "camera_01",
    "description": "Motion detected in living room"
  }'

# Watch it appear in real-time on the Events dashboard
# Visit http://localhost:4000/events

AI Decision Making:

• System receives events from cameras, sensors, file changes
• AI processes events and makes intelligent decisions
• Triggers actions through MCP tools (camera control, displays)

B. Research Output: Methodology Development

Conversation Archaeology:

# See methodology development preserved in real-time
ls chat-history/ | wc -l
# Shows: 90+ conversation files documenting collaboration patterns

# Each conversation shows how the practical system was built
head chat-history/3c00c241-4f9f-4d2b-aebd-34f7d5392654.jsonl

Research Through Practice:

• Building real system components reveals collaboration patterns
• Each technical challenge improves the methodology
• Practical constraints drive methodology refinement

5. Trace a Decision

Try this archaeological workflow:

# Find a piece of code you're curious about
git blame ash_chat/lib/ash_chat/resources/room.ex

# Look for the commit hash in the conversation history
grep -r "c3e13db" chat-history/

# Read the full conversation that created that code
# Every line of code has complete provenance!

What You’ve Just Seen

Practical System:

1. Distributed AI event processing - real-time ingestion and intelligent handling
2. Multi-user chat interface with AI agent personalities
3. MCP tool integration - AI can control cameras, capture screenshots, process events
4. Phoenix LiveView dashboards - real-time system monitoring

Research Methodology:

1. Research through practice - building real system while developing collaboration patterns
2. Conversation archaeology - complete development decision preservation
3. Two-role framework - AI as development team, human as product team in action
4. Physics of Work - making it better for the next person (which is us)

Next Steps

🤖 For AI Collaborators

Start Here: AI Onboarding Quick Start - Essential first steps for new AI sessions

Core Resources:

• AI Collaboration Protocol - How we work together
• Development Checklists - Process reminders from experience
• AI Quick Reference - Common commands and patterns

📚 Understanding the System

• System Architecture - Complete distributed AI system design
• Collaboration Methodology - Research through practice patterns
• Development Journal - Real-time discoveries and insights

🔍 Deep Dives

• Conversation Archaeology - How methodology emerges from practice
• Chat Persistence - Check ash_chat/PERSISTENCE_*.md for SQLite implementation

Troubleshooting

Port 4000 already in use:

lsof -i :4000  # Check what's using the port
# Usually means Phoenix is already running

Dependencies not found:

cd ash_chat && mix deps.get

Compilation errors:

cd ash_chat && mix compile

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You’re now running a system that preserves complete AI development consciousness alongside working code.