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Merge pull request #228 from nubenetes/bot/v2-elite-sync

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V2 Elite: Agentic Optimization Sync (2026)
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Inaki
2026-05-25 16:04:48 +02:00
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14
data/inventory.yaml
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@@ -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

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v2-docs/index.md
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@@ -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*