feat: integrate Stanford CS229 LLM video and bump to 2.1.1

CHANGELOG.md

@@ -5,6 +5,11 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [[2.1.1]](https://github.com/nubenetes/awesome-kubernetes/releases/tag/v2.1.1) - 2026-05-25
+
+### Added
+- **Stanford CS229 LLM Session**: Integrated the Stanford CS229 lecture on "Building Large Language Models" into the V2 Elite Video Hub. The architectural summary focuses on LLM orchestration, alignment (DPO/RLHF), and systems optimization for 2026 AI infrastructure.
+
 ## [[2.1.0]](https://github.com/nubenetes/awesome-kubernetes/releases/tag/v2.1.0) - 2026-05-25
 
 ### Added

data/inventory.yaml

@@ -282186,6 +282186,22 @@ https://www.youtube.com/embed/6eBSHbLKuN0:
   v1_locations: []
   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.
+  category: AI and Future Operations
+  description: Featured video in the Top Videos & Clips section.
+  health_score: 100.0
+  is_enriched: true
+  is_featured_video: true
+  last_checked: 0.0
+  stars: 0
+  status: online
+  technology: LLM Architecture & Post-Training
+  title: 'Stanford CS229: Building Large Language Models (LLMs)'
+  v1_locations: []
+  video_order: 26
+  year: N/A
 https://www.youtube.com/kubernetescommunity:
   content_hash: 5654ed1b031bb525606fadef419e20044ee5e80a0915535c7ae4e75f0c909d9d
   health_score: 100.0

v2-docs/videos.md

@@ -62,6 +62,16 @@ Welcome to the **Agentic Video Hub**. This section presents a logical, architect
 
     </center>
 
+??? note "🎬 Stanford CS229 | Machine Learning | Building Large Language Models (LLMs) | `LLM Architecture & Post-Training`"
+    !!! info "Architectural 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.
+
+    <center markdown="1">
+
+    <iframe width="720" height="405" src="https://www.youtube.com/embed/9vM4p9NN0Ts" title="Stanford CS229: Building Large Language Models (LLMs)" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen loading="lazy" style="border: 1px solid var(--md-typeset-table-color); border-radius: 8px;"></iframe>
+
+    </center>
+
 ## Architecture and Cloud Strategy
 ??? note "🎬 Kubernetes for SysAdmins | Kelsey Hightower at PuppetConf | Talk & Demo | `Kubernetes`"
     !!! info "Architectural Summary"