Chapter 7: Resources & Citations

Access documentation, citations, and community resources

Documentation

macOS System Documentation:

Apple Developer Resources:

From this series:

  • Tutorial #2: Task-Based AI Exposure Analysis (How to measure which tasks to automate)
  • Tutorial #11: Build a $10K MRR SaaS in 30 Days (Apply automation to business)
  • Tutorial #21: Claude Code for Academic Research (Automate research workflows)

External resources:

Research Citations

This tutorial applies research from:

  1. Brynjolfsson, E., et al. (2024). "AI Productivity Measurement and Implementation Costs" MIT Working Paper Key finding: Complementary investments (including automation infrastructure) are 6-12x the cost of AI tools themselves.

  2. Acemoglu, D. & Restrepo, P. (2024). "Task-Based Framework for AI Economics" NBER Working Paper Key finding: Automation value comes from eliminating high-friction, low-value tasks—exactly what this pipeline does.

  3. Felten, E., Raj, M., & Seamans, R. (2024). "Occupational Exposure to AI" Data Repository Key finding: Knowledge workers with highest AI exposure see 2.4x productivity gains when automation infrastructure is in place.

Code Templates

All scripts from this tutorial are available at:

~/automation-projects/claude-pipeline/
├── scripts/
│   ├── wake-utils.sh
│   ├── automation-handler.sh
│   └── setup-automation.sh
├── launchagents/
│   └── com.user.claude-automation.plist
└── logs/
    └── automation.log

Download complete template:

git clone https://github.com/xps-org/claude-automation-templates
cd claude-automation-templates/01-wake-pipeline
./setup-automation.sh

Tools Mentioned

Core System Tools (Built into macOS):

  • launchctl - LaunchAgent manager
  • pmset - Power management
  • caffeinate - Prevent sleep
  • osascript - AppleScript automation

Monitoring Tools:

  • log show - System log viewer
  • fs_usage - File system usage monitoring
  • dtrace - System tracing for debugging

Productivity Tools:

Community Resources

Get help and share your automation:

Share your success:

  • Tag #ClaudeAutomation on Twitter/X
  • Post your pipeline in the community showcase
  • Contribute improvements to the template repo

Benchmarks & Metrics

Expected Performance:

MetricTargetNotes
Setup time30-45 minInitial build and testing
Wake accuracy95%+With AC power and correct settings
Task completion90%+With proper error handling
System overheadLess than 5% CPUDuring caffeinate periods
Log size growth~1MB/monthWith standard logging

My Personal Results:

  • 127 successful automated sessions over 3 months
  • 3 failures (all due to unplugged power)
  • Average time saved: 2.4 hours per week
  • Total automation development time: 8 hours (including this tutorial)
  • ROI: Positive after week 4

Next Steps

Immediate actions: Complete this tutorial's implementation (30 min), test with simple tasks like backup or log rotation (15 min), monitor for one week to ensure stability, and read Blog #3: "The Productivity Paradox I'm Living Through" for context.

Advanced learning path:

  1. Tutorial #12: Building Your AI Sales Agent (Apply automation to business)
  2. Tutorial #17: Programmatic SEO (Automate content generation)
  3. Tutorial #25: Multi-Agent Research Systems (Coordinate multiple AI agents)

Build your portfolio:

  • Create 3 custom automation pipelines
  • Document your results and metrics
  • Share on community forum
  • Help others troubleshoot their implementations