From 86c3f0b8a5535525de8486848ede3c3b8aaec395 Mon Sep 17 00:00:00 2001 From: inafev <2752419+inafev@users.noreply.github.com> Date: Mon, 25 May 2026 13:58:31 +0000 Subject: [PATCH] feat: sync V2 elite curated edition and README metrics [skip ci] --- README.md | 16 ++++++++-------- data/inventory.yaml | 14 ++++++++++++-- v2-docs/index.md | 2 +- v2-docs/videos.md | 2 +- 4 files changed, 22 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index 4129edce..0b1a2dae 100644 --- a/README.md +++ b/README.md @@ -138,9 +138,9 @@ Additionally, as of May 2026, Nubenetes has reached the **Platinum Operational T | Metric | Value | | :--- | :--- | -| **Total Technical Resources (Links)** | **17982+** | +| **Total Technical Resources (Links)** | **17983+** | | **Specialized MD Pages** | **161** | -| **Total Commits** | **5423+** | +| **Total Commits** | **5427+** | | **Primary AI Engine** | **Google Gemini (Agentic)** | @@ -178,7 +178,7 @@ The growth of Nubenetes reflects the acceleration of the Cloud Native ecosystem. | 6 | 2023 | 30 | 123 | Maintenance & Refinement | | 7 | 2024 | 53 | 218 | Curation Strategy Pivot | | 8 | 2025 | 5 | 20 | Stability & Research Phase | -| 9 | 2026 | 1864 | 7,698 | **Agentic AI Surge** (May 2026 Inception) | +| 9 | 2026 | 1868 | 7,714 | **Agentic AI Surge** (May 2026 Inception) | @@ -194,8 +194,8 @@ xychart-beta title "Nubenetes Annual Growth Metrics (2018–2026)" x-axis ["2018", "2019", "2020", "2021", "2022", "2023", "2024", "2025", "2026"] y-axis "Volume (Commits / Estimated New Refs)" 0 --> 9000 - bar [1445, 586, 8449, 2193, 1660, 123, 218, 20, 7698] - bar [350, 142, 2046, 531, 402, 30, 53, 5, 1864] + bar [1445, 586, 8449, 2193, 1660, 123, 218, 20, 7714] + bar [350, 142, 2046, 531, 402, 30, 53, 5, 1868] ``` @@ -204,7 +204,7 @@ xychart-beta | Month | Commits | Est. New Refs | Status | | :--- | :---: | :---: | :--- | | 2026-04 | 25 | 103 | Active Curation | -| 2026-05 | 1839 | 7,595 | **Agentic Inception (Gemini Era)** | +| 2026-05 | 1843 | 7,611 | **Agentic Inception (Gemini Era)** | ### 2.4. Content Distribution and Semantic Clustering @@ -217,7 +217,7 @@ This chart shows the high-level distribution across the primary domains of Cloud ```mermaid pie title Nubenetes Major Ecosystem Pillars - "Specialized Topics" : 3582 + "Specialized Topics" : 3583 "Kubernetes Ecosystem" : 3500 "Developer Ecosystem" : 3000 "Public/Private Cloud" : 2500 @@ -238,7 +238,7 @@ Reflecting Nubenetes' mission of global access while maintaining technical Engli ```mermaid pie title Linguistic Diversity (Global Access) - "English" : 16183 + "English" : 16184 "Spanish" : 1078 "French" : 179 "Others" : 539 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* diff --git a/v2-docs/videos.md b/v2-docs/videos.md index ab4030ae..9dbb1144 100644 --- a/v2-docs/videos.md +++ b/v2-docs/videos.md @@ -62,7 +62,7 @@ Welcome to the **Agentic Video Hub**. This section presents a logical, architect -??? note "🎬 Stanford CS229 | Machine Learning | Building Large Language Models (LLMs) | `LLM Architecture & Post-Training`" +??? note "🎬 Stanford CS229: 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.