Merge pull request #288 from nubenetes/develop

feat: add Grafana AI Observability video to V2 Video Hub (v2.3.35)

CHANGELOG.md

@@ -5,6 +5,70 @@ 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).
 
+## [[2.3.35]](https://github.com/nubenetes/awesome-kubernetes/releases/tag/v2.3.35) - 2026-05-28
+
+### Added
+- **Grafana Observability Video**: Added the Viktor Farcic video `I Stopped Staring at Dashboards. AI Reads My Grafana Metrics Now.` to the V2 Video Hub under the "AI Agents and MCP" learning path.
+
+## [[2.3.34]](https://github.com/nubenetes/awesome-kubernetes/releases/tag/v2.3.34) - 2026-05-28
+
+### Added
+- **Link Provenance Tracking**: Added `addition_method` metadata attribute to `data/inventory.yaml` to differentiate between manually curated and automatically ingested links.
+- **Database Migration**: Migrated all 18,004 existing database entries to have `addition_method: manual` by default.
+- **Workflow & Optimizer Integration**: Programmed `src/agentic_curator.py` to mark automatically ingested resources as `automatic`, and updated `src/v2_optimizer.py` to default new V1 Markdown source resources to `manual`.
+
+### Changed
+- **Documentation & Memory Systems**: Added documentation for `addition_method` in `README.md`, `GEMINI.md`, and `src/memory/health_learning.json`.
+
+## [[2.3.32]](https://github.com/nubenetes/awesome-kubernetes/releases/tag/v2.3.32) - 2026-05-28
+
+### Changed
+- **Arsys Logo Dark Mode Contrast**: Changed the Arsys logo fill color to a constant corporate electric sky blue (`#00a2e8`) inside the SVG, ensuring maximum contrast on both white (V1) and slate/black (V2) backgrounds.
+- **Removed Logo CSS Inversion**: Removed the dark mode CSS filter invert rule in `extra.css` to preserve branding color integrity.
+
+## [[2.3.31]](https://github.com/nubenetes/awesome-kubernetes/releases/tag/v2.3.31) - 2026-05-28
+
+### Changed
+- **Arsys Logo Dark Mode Adaptation**: Embedded light/dark prefers-color-scheme styles inside the Arsys SVG logo and added a CSS invert filter for the slate theme in `extra.css` to fix visibility in dark mode.
+
+## [[2.3.30]](https://github.com/nubenetes/awesome-kubernetes/releases/tag/v2.3.30) - 2026-05-28
+
+### Fixed
+- **Wikimedia Commons URL Correction**: Replaced the broken HTML error page (downloaded due to invalid hash pathing) with a valid vector SVG file by using the correct MD5 filename-derived directory structure (`8/88/Arsys_logo.svg`) on upload.wikimedia.org.
+
+## [[2.3.29]](https://github.com/nubenetes/awesome-kubernetes/releases/tag/v2.3.29) - 2026-05-28
+
+### Added
+- **Arsys Channel Integration**: Registered `https://www.youtube.com/@arsys` in `data/inventory.yaml` under `cloud_providers` (V2) and ordered to append at the end of V1 flat sequence.
+
+### Fixed
+- **Video Hub Heading Level Fix**: Fixed a markdown linter error (MD001) in the generated video hub index by changing the heading increment from h3 to h2 (`## Learning Dimensions`).
+
+## [[2.3.28]](https://github.com/nubenetes/awesome-kubernetes/releases/tag/v2.3.28) - 2026-05-28
+
+### Changed
+- **Mandate 23 Documentation**: Updated `GEMINI.md` and `health_learning.json` to formally document the database-driven dual layout design of YouTube mosaics.
+
+## [[2.3.27]](https://github.com/nubenetes/awesome-kubernetes/releases/tag/v2.3.27) - 2026-05-28
+
+### Added
+- **Workflow Automation for Mosaic**: Configured the V2 Publisher workflow to execute `src/reorganize_mosaic.py` automatically on each run.
+
+## [[2.3.26]](https://github.com/nubenetes/awesome-kubernetes/releases/tag/v2.3.26) - 2026-05-28
+
+### Fixed
+- **Video Hub Index Heading Level Correction**: Adjusted generator code to prevent heading hierarchy lint failures.
+
+## [[2.3.25]](https://github.com/nubenetes/awesome-kubernetes/releases/tag/v2.3.25) - 2026-05-28
+
+### Added
+- **Database Migration for YouTube Mosaic**: Migrated YouTube channel mosaic configuration from the deprecated `youtube_channels_mosaic.yaml` to the unified monolithic `data/inventory.yaml` and refactored the layout generator to dynamically read from the database.
+
+## [[2.3.24]](https://github.com/nubenetes/awesome-kubernetes/releases/tag/v2.3.24) - 2026-05-28
+
+### Changed
+- **Restored V1 Flat YouTube Mosaic Layout**: Restored the flat list layout (11 channels per row, sorted by `order_v1`) on the V1 homepage, while preserving the advanced categorized V2 dashboard.
+
 ## [[2.3.23]](https://github.com/nubenetes/awesome-kubernetes/releases/tag/v2.3.23) - 2026-05-28
 
 ### Changed

GEMINI.md

@@ -55,6 +55,7 @@ This file contains the accumulated instructions and long-term vision for the aut
         *   `source_provenance`: Identifies the origin of the discovery (Twitter, RSS, Manual).
         *   `social_preview_url`: OpenGraph/Social images to enrich the V2 visual experience.
         *   `mentions_count`: Tracks resource popularity/rediscovery frequency.
+        *   `addition_method`: Tracks the resource addition origin ('manual' or 'automatic') to facilitate scaling metrics.
     - **Persistence (MANDATORY)**: Every AI agent and workflow MUST load this file at startup, update it, and INJECT the modified YAML into the final PR payload if any change is detected. Discarding the database during a workflow run is a CRITICAL FAILURE.
     - **Exhaustive Initialization**: The system supports a `FORCE_FULL_CHECK` environment variable to bypass all caches (e.g., 21-day health cache) and force a full re-validation and re-enrichment of the entire 17k+ link archive.
     - **No Trusted Bypassing**: All domains, including high-trust ones (GitHub, Google, AWS), MUST be verified for link validity. Trusted status only grants a lower priority for aggressive scraper rotation, not a bypass for existence checks.

README.md

@@ -140,7 +140,7 @@ Additionally, as of May 2026, Nubenetes has reached the **Platinum Operational T
 | :--- | :--- |
 | **Total Technical Resources (Links)** | **18004+** |
 | **Specialized MD Pages** | **161** |
-| **Total Commits** | **5630+** |
+| **Total Commits** | **5637+** |
 | **Primary AI Engine** | **Google Gemini (Agentic)** |
 <!-- HEART_STATS_END -->
 
@@ -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 | 2071 | 8,553 | **Agentic AI Surge** (May 2026 Inception) |
+| 9 | 2026 | 2078 | 8,582 | **Agentic AI Surge** (May 2026 Inception) |
 <!-- ANNUAL_GROWTH_END -->
 
 <!-- ANNUAL_CHART_START -->
@@ -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, 8553]
-    bar [350, 142, 2046, 531, 402, 30, 53, 5, 2071]
+    bar [1445, 586, 8449, 2193, 1660, 123, 218, 20, 8582]
+    bar [350, 142, 2046, 531, 402, 30, 53, 5, 2078]
 ```
 <!-- ANNUAL_CHART_END -->
 
@@ -204,7 +204,7 @@ xychart-beta
 | Month | Commits | Est. New Refs | Status |
 | :--- | :---: | :---: | :--- |
 | 2026-04 | 25 | 103 | Active Curation |
-| 2026-05 | 2046 | 8,449 | **Agentic Inception (Gemini Era)** |
+| 2026-05 | 2053 | 8,478 | **Agentic Inception (Gemini Era)** |
 <!-- MONTHLY_SURGE_END -->
 
 ### 2.4. Content Distribution and Semantic Clustering
@@ -447,6 +447,7 @@ To embrace the diverse global Cloud Native community while maintaining internati
     *   `hierarchy`: Persistent, **recursive technical classification** (list of up to 10 levels) for O'Reilly-style grouping.
     *   `content_hash` / `health_score`: Advanced fields for content drift detection and reliability tracking.
     *   `source_provenance` / `social_preview_url`: Data for origin tracing and V2 visual enrichment.
+    *   `addition_method`: Origin type of the resource addition ('manual' or 'automatic') to support growth and scaling metrics.
 - **Separation of Concerns (Data vs. UI)**:
     *   **The Database (Source of Truth)**: Holds raw data, enabling future features like language-based filtering or statistics without re-processing links.
     *   **The Portal (Visual Rendering)**: The `V2VisionEngine` dynamically converts the metadata into visual UI tags (e.g., `[SPANISH CONTENT]`, `[ARCHITECT LEVEL]`).

src/agentic_curator.py

@@ -165,7 +165,8 @@ async def evaluate_extracted_assets(raw_assets: List[Dict]) -> Dict[str, Dict]:
                             "reputation_status": "Vetted" if not data.get("reputation_penalty") else "Suspicious",
                             "reputation_summary": data.get("reputation_summary", ""),
                             "source_provenance": d["asset"].get("source_type", "Social"), "social_preview_url": d["rich_meta"].get("og_image", ""),
-                            "category": primary_cat, "status": "online", "last_checked": datetime.now().timestamp(), **d["gh_meta"]
+                            "category": primary_cat, "status": "online", "last_checked": datetime.now().timestamp(),
+                            "addition_method": "automatic", **d["gh_meta"]
                         }
                         if "youtube.com" in url or "youtu.be" in url:
                             title_desc = f"{data['title']} {data['desc']}".lower()

src/memory/health_learning.json

@@ -8,5 +8,13 @@
       "src/reorganize_mosaic.py",
       "src/v2_optimizer.py"
     ]
+  },
+  "addition_method_tracking": {
+    "field": "addition_method",
+    "values": ["manual", "automatic"],
+    "rules": {
+      "manual": "Assigned to all pre-existing entries and any new entries discovered by the V2 Optimizer from V1 Markdown source files (assumed manually added).",
+      "automatic": "Assigned to resources ingested automatically via curation workflows from X/Twitter, RSS, or GitHub trending."
+    }
   }
 }

src/v2_optimizer.py

@@ -392,6 +392,8 @@ class V2VisionEngine:
                             norm_url = normalize_url(item["url"])
                             self.inventory[norm_url] = {k:v for k,v in item.items() if k not in ["url", "title", "original_file", "is_special", "aliases"]}
                             self.inventory[norm_url]["title"] = item["title"]
+                            if "addition_method" not in self.inventory[norm_url]:
+                                self.inventory[norm_url]["addition_method"] = "manual"
 
                 except Exception:
                     for l in batch: analyst_results.append(l)
@@ -501,6 +503,8 @@ class V2VisionEngine:
                 # Persist to inventory
                 self.inventory[norm_url] = {k:v for k,v in item.items() if k not in ["url", "title", "original_file", "is_special", "aliases"]}
                 self.inventory[norm_url]["title"] = item["title"]
+                if "addition_method" not in self.inventory[norm_url]:
+                    self.inventory[norm_url]["addition_method"] = "manual"
                 
                 if p_id not in project_registry or item.get("stars", 0) > project_registry[p_id].get("stars", 0):
                     if p_id in project_registry and project_registry[p_id].get("is_special"): item["is_special"] = True

v2-docs/videos/ai-agents.md

@@ -6,11 +6,12 @@ Welcome to the **AI Agents and MCP** section of the V2 Video Hub. Explore curate
 
 1. [Red Hat OpenShift](#red-hat-openshift)
 2. [Generative AI and Large Language Models](#generative-ai-and-large-language-models)
-3. [Red Hat OpenShift AI](#red-hat-openshift-ai)
-4. [Claude Code](#claude-code)
-5. [Neural Networks](#neural-networks)
-6. [LLM Architecture and Post-Training](#llm-architecture-and-post-training)
-7. [Agentic DevOps](#agentic-devops)
+3. [Grafana Assistant Claude Code Grafana MCP Server](#grafana-assistant-claude-code-grafana-mcp-server)
+4. [Red Hat OpenShift AI](#red-hat-openshift-ai)
+5. [Claude Code](#claude-code)
+6. [Neural Networks](#neural-networks)
+7. [LLM Architecture and Post-Training](#llm-architecture-and-post-training)
+8. [Agentic DevOps](#agentic-devops)
 
 ## Red Hat OpenShift
 
@@ -36,6 +37,18 @@ Welcome to the **AI Agents and MCP** section of the V2 Video Hub. Explore curate
 
     </center>
 
+## Grafana Assistant Claude Code Grafana MCP Server
+
+??? note "🎬 I Stopped Staring at Dashboards. AI Reads My Grafana Metrics Now."
+    !!! info "Architectural Summary"
+        This video explores how AI agents can read Grafana metrics, logs, and traces directly using Grafana Assistant and Claude Code wired up to the Grafana MCP server. It demonstrates querying Prometheus, Loki, and Tempo, generating custom dashboards from natural language, and analyzing runtime data from the command line. This workflow bridges the gap between analysis and remediation, allowing agents to verify fixes and make data-grounded decisions.
+
+    <center markdown="1">
+
+    <iframe width="720" height="405" src="https://www.youtube.com/embed/HI6KleJAZPY" title="I Stopped Staring at Dashboards. AI Reads My Grafana Metrics Now." 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>
+
 ## Red Hat OpenShift AI
 
 ??? note "🎬 Red Hat OpenShift AI overview"