diff --git a/data/inventory.yaml b/data/inventory.yaml
index 048728ab..de37241b 100644
--- a/data/inventory.yaml
+++ b/data/inventory.yaml
@@ -282187,8 +282187,18 @@ https://www.youtube.com/embed/6eBSHbLKuN0:
   video_order: 25
   year: N/A
 https://www.youtube.com/embed/9vM4p9NN0Ts:
-  ai_summary: |
-    This Stanford CS229 technical deep-dive deconstructs the transition from raw autoregressive language models to instruction-tuned assistants, focusing on the systems orchestration required for 2026 AI infrastructure. It explores critical patterns in tokenization (BPE/Sub-word), parameter-efficient fine-tuning (PEFT/LoRA), and the shift from RLHF to Direct Preference Optimization (DPO) to simplify model alignment pipelines. For cloud architects, the lecture provides a foundational framework for optimizing the "Compute-to-Token" ratio and managing memory constraints (KV Cache) in distributed distributed inference environments, while advocating for LLM-as-a-Judge automated evaluation loops for scalable model governance.
+  ai_summary: 'This Stanford CS229 technical deep-dive deconstructs the transition
+    from raw autoregressive language models to instruction-tuned assistants, focusing
+    on the systems orchestration required for 2026 AI infrastructure. It explores
+    critical patterns in tokenization (BPE/Sub-word), parameter-efficient fine-tuning
+    (PEFT/LoRA), and the shift from RLHF to Direct Preference Optimization (DPO) to
+    simplify model alignment pipelines. For cloud architects, the lecture provides
+    a foundational framework for optimizing the "Compute-to-Token" ratio and managing
+    memory constraints (KV Cache) in distributed distributed inference environments,
+    while advocating for LLM-as-a-Judge automated evaluation loops for scalable model
+    governance.
+
+    '
   category: AI and Future Operations
   description: Featured video in the Top Videos & Clips section.
   health_score: 100.0
diff --git a/v2-docs/index.md b/v2-docs/index.md
index 26d2497a..c712f513 100644
--- a/v2-docs/index.md
+++ b/v2-docs/index.md
@@ -21,7 +21,7 @@
     2. **Standard Layer (Mapped)**: Resources identified as candidates for Elite status but pending deep AI analysis.
 
     **Current Inventory Coverage:**
-    - **V1 Base Inventory**: 17982 total resources analyzed.
+    - **V1 Base Inventory**: 17983 total resources analyzed.
     - **V2 Elite Selection**: 14172 candidates identified (78.81% density ratio).
     - **AI Enrichment Coverage**: 2853 / 14172 (20.13%)
     - **GitHub Metadata Coverage**: 1451 / 1763 (82.3%) - *Critical for Maturity Tagging*