Analysis Directory

This directory contains Tier 2 content - critical analysis of research material under professional evaluation.

Purpose

Bridge between raw research (docs/) and proven practice (main src/ content). Each document represents your professional assessment of potential patterns, tools, or frameworks.

Standards

  • Objective analysis using established risk matrices
  • Client context considerations for all assessments
  • Evidence-based conclusions with supporting data
  • Clear recommendations with actionable next steps

File Naming

analysis-[topic]-[YYYY-MM].md

Examples:

  • analysis-framework-wars-2025-01.md
  • analysis-mcp-integration-2025-01.md
  • analysis-claude-workflows-2025-01.md

Analysis Template

See analysis-template.md for the standard structure to follow.

Current Analysis Documents

🔴 High Priority - User Adoption & Trust

Psychology of Trust in AI Systems

  • Focus: Four-pillar trust framework for AI system design
  • Value: Critical for addressing user acceptance challenges
  • Status: Framework evaluation complete, awaiting field validation
  • Next Steps: Create trust assessment templates for client projects

🔴 High Priority - Technical Implementation

ACE-FCA Context Engineering

  • Focus: Frequent Intentional Compaction for complex codebases
  • Value: Enables AI coding in 300k+ LOC production systems
  • Status: Technical evaluation complete, ready for pilot
  • Next Steps: Test on internal project, measure productivity gains

AI Coding Optimization

Faraaz AI Coding Efficiency Evaluation

  • Vector embeddings and dependency graphs for AI agent optimization
  • Performance metrics and ROI frameworks

Tool-Specific Evaluations

Google Gemini Nano Banana Evaluation

  • On-device AI capabilities assessment
  • Privacy implications and performance trade-offs
  • Cost and vendor lock-in considerations

Distributed Systems Integration

Martin Fowler Distributed Systems Patterns

  • Architectural patterns for AI in distributed systems
  • Enterprise integration strategies
  • Scalability and reliability considerations

Lifecycle

  1. Creation: High-priority research items get analysis documents
  2. Updates: Quarterly review for relevance and accuracy
  3. Promotion: Successful experiments move to main content
  4. Archive: Outdated or rejected analyses move to archive/ folder