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Building Personal AI Infrastructure: A Technical Deep Dive

•6 min read•By Wayne Bridges
AIautomationproductivitypython

I manage two businesses (Omnissa day job + Free Beer Studio), dozens of customers, and multiple projects simultaneously. Traditional project management tools weren't cutting it.

So I built my own: Personal AI Infrastructure (PAI).

The Problem

Standard tools like Asana, Notion, and Todoist work great—until you're juggling:

  • Customer relationship management
  • Project tracking across multiple ventures
  • Email triage and response drafting
  • Meeting preparation and note-taking
  • Weekly/quarterly planning
  • Habit tracking
  • Knowledge management

Each tool solves part of the problem. None solve my problem.

The Solution: PAI

PAI is an intelligent system that:

  1. Orchestrates my daily workflow (1 MUST + 3 MITs + 7 habits = Daily Win)
  2. Automates repetitive tasks (customer intelligence, email drafts, meeting briefs)
  3. Integrates with tools I already use (Things 3, Apple Notes, Google Workspace)
  4. Learns from my patterns and preferences

Architecture

THINGS 3 (Execution) ↔ PAI (Intelligence) ↔ GOOGLE WORKSPACE (Production)
         ↕                    ↕                        ↕
    Tasks/Projects      Memories/Docs           Calendar/Email
         ↕                    ↕
   APPLE NOTES          CLAUDE AI
  (Daily Records)    (Agent Coordination)

Components

1. Claude Code CLI - My primary interface

  • Natural language task management
  • Context-aware slash commands
  • 85+ MCP-integrated tools (Gmail, Calendar, Things 3, etc.)
  • Proactive agent coordination

2. Python Agents - Specialized automation

  • Customer Intelligence: Scans customer files, builds memory summaries
  • Task Router: Triages inbox items, tags for Things 3
  • Project Hygiene: Monitors stale tasks, suggests cleanup
  • Insights Synthesizer: Weekly/quarterly pattern analysis

3. AppleScript Libraries - Native Mac integration

  • things-lib.sh - Task management automation
  • notes-lib.sh - Apple Notes HTML formatting
  • mail-lib.sh - Email search and operations
  • calendar-lib.sh - Event management

4. Memory System - Persistent context

  • Customer profiles with interaction history
  • Project decisions and rationale
  • Learning notes from experiments
  • Auto-expires stale data (120min TTL cache)

Key Workflows

Daily Morning Dashboard

/morning-dashboard

This command:

  1. Shows today's MUST + 3 MITs from Things 3
  2. Reviews calendar for meetings
  3. Surfaces urgent emails
  4. Displays habit progress
  5. Flags any blockers

Output: A single screen showing everything that matters today.

Customer Meeting Prep

/prep-meeting [customer-name]

This command:

  1. Loads customer profile and history
  2. Reviews recent interactions (emails, notes, meetings)
  3. Checks related tasks in Things 3
  4. Surfaces relevant product updates
  5. Suggests talking points

Result: Walk into every meeting fully prepared, no scrambling.

Email Response Drafting

/draft-response [email-subject]

This command:

  1. Locates the email in Apple Mail
  2. Reads thread context
  3. Loads customer intelligence
  4. Drafts response in my voice (direct, helpful, actionable)
  5. Includes relevant templates

Time saved: 15-20 minutes per complex email.

The Tech Stack

Languages & Frameworks:

  • Python 3.11 (agents, automation)
  • TypeScript/Node (Next.js websites)
  • Bash (AppleScript bridge scripts)

AI & LLMs:

  • Claude AI (Claude Code CLI interface)
  • Anthropic API (embedded in PAI agents)
  • Model Context Protocol (MCP) for tool integration

Data & Storage:

  • Markdown files (notes, customers, projects)
  • JSON (structured data, configurations)
  • SQLite (agent logs, analytics)
  • Git (version control for everything)

Integrations:

  • Things 3 (task execution)
  • Apple Notes (daily records, meeting notes)
  • Google Workspace (Gmail, Calendar, Drive, Docs)
  • GitHub (code projects)
  • Vercel (website deployments)

Deployment:

  • macOS launchd agents (scheduled tasks)
  • Local filesystem (no cloud dependency)
  • Secure credentials in ~/.config/

Results

After 6 months of building and iterating:

Time Savings:

  • Morning planning: 30 min → 5 min (83% reduction)
  • Meeting prep: 20 min → 3 min (85% reduction)
  • Email responses: 15 min → 5 min (67% reduction)
  • Weekly review: 60 min → 20 min (67% reduction)

Outcome Improvements:

  • Daily win rate: 45% → 78% (33% increase)
  • Customer responsiveness: Same-day replies went from 60% → 95%
  • Project completion: Shipped 3 major projects in Q4 2025

Cognitive Load:

  • Zero "what should I work on?" moments
  • Complete customer context every time
  • No forgotten tasks or dropped balls
  • Clear quarterly progress tracking

Lessons Learned

1. Start With Pain Points

Don't build infrastructure for the sake of it. I built PAI because I was drowning in context switching.

2. Use The Right Tool For The Job

  • Things 3 for execution (best Mac app, period)
  • Apple Notes for daily records (native, fast, searchable)
  • Claude AI for intelligence (best reasoning model)
  • Python for automation (fast to write, easy to maintain)

3. Make It Stupidly Simple

Every workflow should be:

  • One command to execute
  • Clear output format
  • Actionable next steps

If it's complex, I won't use it.

4. Iterate Relentlessly

PAI v1 was bash scripts and text files. v2 added Python agents. v3 integrated Claude Code. v4 added memory system.

Each version solved a new pain point.

5. Document Everything

When a command does something useful, I document it. When a decision is made, I log it. Future me (and future Claude) thanks past me.

What's Next

Phase 3: Advanced integrations (calendar intelligence, email management skill)

Phase 4: Content & publishing workflows (blog automation, social media)

Phase 5: Analytics & insights (pattern detection, productivity metrics)

Phase 6: Open source components (skills library, agent templates)

Want to Build Your Own?

PAI is personal—it's tailored to my exact workflow. But the principles are universal:

  1. Map your workflow - Document your daily/weekly routines
  2. Identify repetitive tasks - Where do you waste time?
  3. Choose your tools - What do you already use?
  4. Start small - One automation at a time
  5. Iterate based on usage - Build what you actually use

I'm happy to share more details on specific components. Hit me up at wayne@freebeer.ai or check out my GitHub.

Tech Note: This entire blog post system (including this post) was built and deployed in under 2 hours using Next.js 15, MDX, and Vercel. The PAI system helped me scope it, write it, and ship it. Meta? Yes. Useful? Absolutely.