docs: v3.9.6 - Trust Calibration, Vibe Coding Trap, reference.yaml sync

Added:
- ultimate-guide.md: Trust Calibration section (1.7) with research-backed verification strategies
- ultimate-guide.md: Infrastructure as Code Skills (Anton Babenko's terraform-skill)
- learning-with-ai.md: "The Vibe Coding Trap" anti-pattern (Karpathy, Feb 2025)
- learning-with-ai.md: "Reality of AI Productivity" section with research
- learning-with-ai.md: External resource link to leerob.com/ai (AI fundamentals)
- IDEAS.md: "Vibe Coding Discourse" watching entry

Changed:
- reference.yaml: Complete line number synchronization (~55 entries)
  Guide grew from ~9900 to 11154 lines; all deep_dive references updated
- Version bump: 3.9.5 → 3.9.6

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

guide/learning-with-ai.md

@@ -78,6 +78,19 @@ AI-assisted (right): Problem → Attempt → AI guidance → Understanding → S
 
 The struggle isn't optional. It's where learning happens.
 
+### The "Vibe Coding" Trap
+
+Term coined by [Andrej Karpathy](https://x.com/karpathy/status/1886192184808149383) (Feb 2025, Collins Word of the Year 2025): coding by "fully giving in to the vibes" without understanding the generated code.
+
+**Symptoms:**
+- Accept All without reading diffs
+- Copy-paste errors without understanding root cause
+- Debug by asking AI for random changes until it works
+
+**Karpathy's caveat:** "Not too bad for throwaway weekend projects" — but dangerous for production code you'll need to maintain.
+
+**Antidote:** The UVAL Protocol (§5) forces understanding before acceptance.
+
 ---
 
 ## The Reality of AI Productivity
@@ -928,6 +941,7 @@ See [methodologies.md](./methodologies.md) for:
 
 - [Anthropic Prompt Engineering Guide](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview) — Better prompts = better learning
 - [The Pragmatic Programmer](https://pragprog.com/titles/tpp20/the-pragmatic-programmer-20th-anniversary-edition/) — Timeless principles for deliberate practice
+- [AI for Engineers](https://leerob.com/ai) — AI fundamentals (ML, transformers, tokenization)
 
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