docs: add AI productivity research, trust calibration, and exploration workflow
## New Content
### Trust & Verification (ultimate-guide.md)
- Section 1.7 "Trust Calibration: When and How Much to Verify" (~155 lines)
- Research-backed stats (ACM, Veracode, CodeRabbit, Cortex.io)
- Verification spectrum by code type
- Solo vs Team strategies with workflow diagrams
- "Prove It Works" checklist
- New pitfall: "Trust AI output without proportional verification"
- CLAUDE.md size guideline: 4-8KB optimal, >16K degrades coherence
### AI Productivity (learning-with-ai.md)
- Section "The Reality of AI Productivity" (~55 lines)
- Productivity curve phases (Wow Effect → Targeted Gains → Plateau)
- High-gain vs low/negative-gain task categorization
- Team success factors
- Productivity trajectory table by pattern (Dependent/Avoidant/Augmented)
- 5 new sources (GitHub, McKinsey, Stack Overflow, Uplevel, DORA)
### Session Limits (architecture.md)
- "Session Degradation Limits" section
- Turn limits (15-25), token thresholds (80-100K)
- Success rates by scope (1-3 files: ~85%, 8+ files: ~40%)
### Exploration Workflow
- NEW: guide/workflows/exploration-workflow.md
- Anti-anchoring prompts, 3-5 approaches pattern
- iterative-refinement.md: Script Generation Workflow (3-7 iteration pattern)
- anchor-catalog.md: Anti-Anchoring Techniques, Exploration/Iteration Prompts
### Reference Updates
- adoption-approaches.md: Empirical data section
- reference.yaml: New deep_dive entries, updated line numbers
Sources: MetalBear engineering blog, arXiv studies, Addy Osmani (Jan 2026)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
fd17414abb