mirror of
https://github.com/nubenetes/awesome-kubernetes.git
synced 2026-05-24 18:13:48 +00:00
feat: pivot V2 to high-density chronological library with year extraction
This commit is contained in:
Nubenetes Bot
@@ -14,102 +14,52 @@ V2_DIR = "v2-docs"
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class V2VisionEngine:
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def __init__(self):
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# 100% Comprehensive 2026 Taxonomy - Audited for Full V1 Coverage
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# 100% Comprehensive 2026 Taxonomy
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self.dimensions = {
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"Intelligent Control Plane": [
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"ai", "ai-agents-mcp", "chatgpt", "mlops"
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],
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"Architectural Foundations": [
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"introduction", "faq", "kubernetes", "linux", "git", "cloud-arch-diagrams",
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"matrix-table", "other-awesome-lists", "about"
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],
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"Platform & Site Reliability": [
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"sre", "devops", "developerportals", "scaffolding", "finops", "chaos-engineering",
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"performance-testing-with-jenkins-and-jmeter", "project-management-methodology",
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"project-management-tools", "qa", "test-automation-frameworks", "testops"
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],
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"Hardened Infrastructure": [
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"iac", "terraform", "pulumi", "crossplane", "ansible", "securityascode",
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"kubernetes-security", "aws-security", "oauth", "devsecops", "kustomize",
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"liquibase", "chef"
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],
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"Cloud Providers (Hyperscalers)": [
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"aws", "azure", "GoogleCloudPlatform", "ibm_cloud", "oraclecloud", "digitalocean",
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"cloudflare", "scaleway", "managed-kubernetes-in-public-cloud", "public-cloud-solutions",
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"private-cloud-solutions", "edge-computing", "aws-architecture", "aws-security",
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"aws-networking", "aws-databases", "aws-storage", "aws-monitoring", "aws-iac",
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"aws-tools-scripts", "aws-messaging", "aws-data", "aws-devops", "aws-serverless",
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"aws-containers", "aws-backup", "aws-training", "aws-newfeatures", "aws-miscellaneous",
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"aws-pricing", "aws-spain"
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],
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"Networking & Service Mesh": [
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"networking", "kubernetes-networking", "servicemesh", "istio", "caching",
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"web-servers", "cloudflare"
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],
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"The Container Stack": [
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"docker", "container-managers", "serverless", "kubernetes-autoscaling",
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"kubernetes-operators-controllers", "kubernetes-storage", "kubernetes-monitoring",
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"kubernetes-troubleshooting", "kubernetes-backup-migrations", "kubernetes-on-premise",
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"kubernetes-bigdata", "kubernetes-client-libraries", "kubernetes-releases",
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"kubernetes-based-devel", "kubernetes-alternatives", "kubectl-commands", "rancher",
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"openshift", "ocp3", "ocp4", "noops"
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],
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"Data & Advanced Analytics": [
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"databases", "nosql", "newsql", "message-queue", "crunchydata", "yaml", "bigdata"
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],
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"Engineering Pipeline": [
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"cicd", "gitops", "argo", "flux", "tekton", "jenkins", "jenkins-alternatives",
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"openshift-pipelines", "sonarqube", "registries", "keptn", "stackstorm",
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"cicd-kubernetes-plugins"
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],
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"Developer Ecosystem": [
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"visual-studio", "javascript", "golang", "python", "java_frameworks", "java_app_servers",
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"java-and-java-performance-optimization", "dotnet", "angular", "react", "web3",
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"api", "swagger-code-generator-for-rest-apis", "postman", "lowcode-nocode",
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"devel-sites", "dom", "linux-dev-env", "ChromeDevTools", "xamarin", "jvm-parameters-matrix-table",
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"maven-gradle", "embedded-servlet-containers"
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],
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"Career & Industry": [
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"recruitment", "hr", "freelancing", "remote-tech-jobs", "workfromhome",
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"interview-questions", "elearning", "digital-money", "appointment-scheduling", "newsfeeds"
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]
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"Intelligent Control Plane": ["ai", "ai-agents-mcp", "chatgpt", "mlops"],
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"Architectural Foundations": ["introduction", "faq", "kubernetes", "linux", "git", "cloud-arch-diagrams", "matrix-table", "other-awesome-lists", "about"],
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"Platform & Site Reliability": ["sre", "devops", "developerportals", "scaffolding", "finops", "chaos-engineering", "performance-testing-with-jenkins-and-jmeter", "project-management-methodology", "project-management-tools", "qa", "test-automation-frameworks", "testops"],
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"Hardened Infrastructure": ["iac", "terraform", "pulumi", "crossplane", "ansible", "securityascode", "kubernetes-security", "aws-security", "oauth", "devsecops", "kustomize", "liquibase", "chef"],
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"Cloud Providers (Hyperscalers)": ["aws", "azure", "GoogleCloudPlatform", "ibm_cloud", "oraclecloud", "digitalocean", "cloudflare", "scaleway", "managed-kubernetes-in-public-cloud", "public-cloud-solutions", "private-cloud-solutions", "edge-computing", "aws-architecture", "aws-security", "aws-networking", "aws-databases", "aws-storage", "aws-monitoring", "aws-iac", "aws-tools-scripts", "aws-messaging", "aws-data", "aws-devops", "aws-serverless", "aws-containers", "aws-backup", "aws-training", "aws-newfeatures", "aws-miscellaneous", "aws-pricing", "aws-spain"],
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"Networking & Service Mesh": ["networking", "kubernetes-networking", "servicemesh", "istio", "caching", "web-servers", "cloudflare"],
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"The Container Stack": ["docker", "container-managers", "serverless", "kubernetes-autoscaling", "kubernetes-operators-controllers", "kubernetes-storage", "kubernetes-monitoring", "kubernetes-troubleshooting", "kubernetes-backup-migrations", "kubernetes-on-premise", "kubernetes-bigdata", "kubernetes-client-libraries", "kubernetes-releases", "kubernetes-based-devel", "kubernetes-alternatives", "kubectl-commands", "rancher", "openshift", "ocp3", "ocp4", "noops"],
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"Data & Advanced Analytics": ["databases", "nosql", "newsql", "message-queue", "crunchydata", "yaml", "bigdata"],
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"Engineering Pipeline": ["cicd", "gitops", "argo", "flux", "tekton", "jenkins", "jenkins-alternatives", "openshift-pipelines", "sonarqube", "registries", "keptn", "stackstorm", "cicd-kubernetes-plugins"],
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"Developer Ecosystem": ["visual-studio", "javascript", "golang", "python", "java_frameworks", "java_app_servers", "java-and-java-performance-optimization", "dotnet", "angular", "react", "web3", "api", "swagger-code-generator-for-rest-apis", "postman", "lowcode-nocode", "devel-sites", "dom", "linux-dev-env", "ChromeDevTools", "xamarin", "jvm-parameters-matrix-table", "maven-gradle", "embedded-servlet-containers"],
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"Career & Industry": ["recruitment", "hr", "freelancing", "remote-tech-jobs", "workfromhome", "interview-questions", "elearning", "digital-money", "appointment-scheduling", "newsfeeds"]
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}
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self.elite_criteria = (
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"You are a Senior Principal Architect in May 2026. "
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"Transform this list into an ENTERPRISE-GRADE portal.\n\n"
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"PHASE 1: EXCELLENCE SELECTION\n"
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"- Filter for production quality, authority, and innovation.\n"
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self.library_criteria = (
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"You are a Technical Librarian in 2026. Your mission is to build a high-density, professional reference library.\n"
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"PHASE 1: TECHNICAL PRESERVATION\n"
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"- Be INCLUSIVE. Do not discard useful technical content just because it's a few years old.\n"
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"- KEEP: Technically solid tools, guides, and documentation. Even if 'classic', they are part of the library.\n"
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"- DISCARD ONLY: Broken links, 404s, obvious spam, non-technical jokes, and personal anecdotes.\n"
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"- ALWAYS keep 'Awesome' repositories.\n\n"
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"PHASE 2: ELITE SYNTHESIS\n"
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"- From the kept links, identify the TOP 5 'Architect's Choice' resources.\n"
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"- Categorize resources into: [FOUNDATIONAL], [PRODUCTION-READY], or [CUTTING-EDGE].\n"
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"PHASE 2: TEMPORAL ANALYSIS\n"
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"- For EACH kept resource, identify or estimate the PUBLICATION YEAR.\n"
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"- Use URL patterns (e.g., /2023/...), content clues, or tool era to decide the year.\n"
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"- If totally unknown, use 2024 as default for recently active looking sites, or 'N/A'.\n"
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)
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async def analyze_and_cluster(self):
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log_event("STARTING V2 ARCHITECT'S CUT: THE FINAL POLISH", section_break=True)
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log_event("STARTING V2 HIGH-DENSITY CHRONOLOGICAL LIBRARY GENERATION", section_break=True)
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all_v1_links = await self._gather_all_v1_content()
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log_event(f"[*] Discovery: Found {len(all_v1_links)} resources in V1 archive.")
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# 1. Excellence Filtering & Elite Pass
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log_event("[*] Phase 1: Semantic Excellence Filtering...")
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elite_inventory = await self._evaluate_quality(all_v1_links)
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log_event(f"[*] Refinement Complete: {len(elite_inventory)} high-impact nodes selected.")
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log_event("[*] Phase 1: Library Evaluation & Year Extraction...")
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library_inventory = await self._evaluate_and_date_resources(all_v1_links)
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log_event(f"[*] Inventory Refined: {len(library_inventory)} high-quality resources kept.")
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# 2. Structural Reconstruction
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log_event("[*] Phase 2: Multi-dimensional Clustering & Synthesis...")
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v2_data = await self._rebuild_structure(elite_inventory)
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log_event("[*] Phase 2: Dimensional Clustering & Chronological Sorting...")
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v2_data = await self._rebuild_structure(library_inventory)
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# 3. File Generation
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log_event("[*] Phase 3: Generating Premium Portal Pages...")
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os.makedirs(V2_DIR, exist_ok=True)
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await self._write_premium_files(v2_data)
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# 4. Navigation Sync
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await self._sync_enterprise_navigation(v2_data)
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log_event("V2 ARCHITECT'S CUT COMPLETED.", section_break=True)
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log_event("V2 LIBRARY GENERATION COMPLETED.", section_break=True)
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async def _gather_all_v1_content(self) -> List[Dict]:
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all_links = []
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@@ -121,57 +71,54 @@ class V2VisionEngine:
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content = f.read()
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matches = re.findall(r'^\s*-\s*\[([^\]]+)\]\(([^\)]+)\)(.*)', content, re.MULTILINE)
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for title, url, desc in matches:
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all_links.append({
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"title": title, "url": url, "description": desc.strip(), "original_file": file
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})
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all_links.append({"title": title, "url": url, "description": desc.strip(), "original_file": file})
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return all_links
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async def _evaluate_quality(self, links: List[Dict]) -> List[Dict]:
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async def _evaluate_and_date_resources(self, links: List[Dict]) -> List[Dict]:
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refined = []
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BATCH_SIZE = 60
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BATCH_SIZE = 50
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for i in range(0, len(links), BATCH_SIZE):
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batch = links[i:i+BATCH_SIZE]
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batch_num = i//BATCH_SIZE + 1
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log_event(f" [>] Evaluating Excellence Batch {batch_num}...")
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log_event(f" [>] Processing Batch {batch_num}...")
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prompt = (
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f"{self.elite_criteria}\n"
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"Respond ONLY with a JSON object: {\"keep_indices\": [int, ...], \"tags\": {\"index\": \"TAG\"}, \"top_choices\": [int, ...]}\n"
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"TAGS: [FOUNDATIONAL], [PRODUCTION-READY], [CUTTING-EDGE].\n\n"
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f"{self.library_criteria}\n"
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"Respond ONLY with a JSON object: {\"keep_indices\": [int, ...], \"years\": {\"index\": \"YYYY\"}}\n\n"
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"LINKS:\n" + "\n".join([f"{idx}. {l['title']} ({l['url']})" for idx, l in enumerate(batch)])
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)
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try:
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data = await call_gemini_with_retry(prompt)
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indices = data.get("keep_indices", [])
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tags_map = data.get("tags", {})
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top_choices = set(data.get("top_choices", []))
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years_map = data.get("years", {})
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for idx in indices:
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try:
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idx_int = int(idx)
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if idx_int < len(batch):
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item = batch[idx_int].copy()
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item["tag"] = tags_map.get(str(idx), "[PRODUCTION-READY]")
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item["is_elite"] = idx_int in top_choices
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year_val = years_map.get(str(idx), "2024")
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item["year"] = str(year_val)
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# Tag based on year
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if item["year"].isdigit() and int(item["year"]) >= 2025: item["tag"] = "[CUTTING-EDGE]"
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elif "awesome" in item["title"].lower(): item["tag"] = "[FOUNDATIONAL]"
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else: item["tag"] = "[PRODUCTION-READY]"
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refined.append(item)
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except: continue
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log_event(f" [Batch {batch_num}] Quality Filter: Kept {len(indices)}/{len(batch)}")
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log_event(f" [Batch {batch_num}] Kept {len(indices)}/{len(batch)}")
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except:
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# Conservative fallback: keep and mark as 2024
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for l in batch:
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if "awesome" in l['title'].lower():
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item = l.copy()
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item["tag"] = "[FOUNDATIONAL]"
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item["is_elite"] = False
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refined.append(item)
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item = l.copy()
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item["year"] = "2024"
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item["tag"] = "[FOUNDATIONAL]" if "awesome" in l["title"].lower() else "[PRODUCTION-READY]"
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refined.append(item)
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await asyncio.sleep(0.5)
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return refined
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async def _rebuild_structure(self, inventory: List[Dict]) -> Dict[str, Dict]:
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v2_structure = {dim: {"summary": "", "categories": {}} for dim in self.dimensions.keys()}
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file_to_dim = {}
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for dim, files in self.dimensions.items():
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for f in files: file_to_dim[f + ".md"] = dim
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@@ -183,29 +130,34 @@ class V2VisionEngine:
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v2_structure[dim]["categories"][cat_name] = []
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v2_structure[dim]["categories"][cat_name].append(item)
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# Sort within each category by year (descending)
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for dim in v2_structure.keys():
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if not v2_structure[dim]["categories"]: continue
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log_event(f" [*] Synthesizing executive summary for {dim}...")
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prompt = f"Write a 1-sentence executive summary for a technical portal section titled '{dim}'. Tone: Professional, Visionary, 2026. Respond ONLY with the sentence."
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for cat in v2_structure[dim]["categories"]:
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v2_structure[dim]["categories"][cat].sort(key=lambda x: x.get("year", "0"), reverse=True)
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# Dimension summary
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prompt = f"Write a 1-sentence executive summary for section '{dim}'. Professional 2026 tone. Respond ONLY with the sentence."
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try:
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v2_structure[dim]["summary"] = await call_gemini_with_retry(prompt, response_format="text")
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except:
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v2_structure[dim]["summary"] = f"Curated high-impact resources for {dim} ecosystem."
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v2_structure[dim]["summary"] = f"Curated high-density chronological resources for {dim}."
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return v2_structure
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async def _write_premium_files(self, data: Dict[str, Dict]):
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# index.md with Mermaid
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# Home
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mermaid_code = "graph TD\n"
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for dim in data.keys():
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if data[dim]["categories"]:
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mermaid_code += f" V2[Nubenetes V2] --> {dim.replace(' ', '_')}[{dim}]\n"
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mermaid_code += f" V2[Nubenetes V2] --> {dim.replace(' ', '_').replace('&', 'and').replace('(', '').replace(')', '')}[{dim}]\n"
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index_md = (
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"# Nubenetes V2 | The Architect's Cut (2026)\n\n"
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"!!! quote \"Engineering the Future\"\n"
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" This portal represents the state-of-the-art in Cloud Native engineering. It is an AI-distilled intelligence layer "
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" filtered for production stability, technical depth, and architectural innovation.\n\n"
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"# Nubenetes V2 | The High-Density Library (2026)\n\n"
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"\n\n"
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"!!! quote \"Chronological Excellence\"\n"
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" This portal is a time-indexed reference for Cloud Native engineering. Resources are sorted by publication year "
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" to ensure you have both the latest innovations and foundational classics at your fingertips.\n\n"
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"## System Architecture View\n\n"
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"```mermaid\n" + mermaid_code + "```\n\n"
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"## Strategic Dimensions\n\n"
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@@ -214,37 +166,22 @@ class V2VisionEngine:
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if not content["categories"]: continue
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slug = dim.lower().replace(" ", "-").replace("&", "and").replace("(", "").replace(")", "").replace(" ", "-")
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index_md += f"- **[{dim}](./{slug}.md)**: {content['summary']}\n"
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with open(os.path.join(V2_DIR, "index.md"), "w") as f: f.write(index_md)
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# Dimension pages
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for dim, content in data.items():
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if not content["categories"]: continue
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slug = dim.lower().replace(" ", "-").replace("&", "and").replace("(", "").replace(")", "").replace(" ", "-")
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md = f"# {dim}\n\n"
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md += f"!!! info \"Executive Overview\"\n {content['summary']}\n\n"
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# Sub-grouping
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for cat, links in content["categories"].items():
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md += f"## {cat}\n"
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# Highlight Elite choices first
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elite_links = [l for l in links if l.get("is_elite")]
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normal_links = [l for l in links if not l.get("is_elite")]
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if elite_links:
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md += "!!! star \"Architect's Choice\"\n"
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for l in elite_links:
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md += f" - [{l['title']}]({l['url']}) - {l['description']}\n"
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md += "\n"
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for l in normal_links:
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for l in links:
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year = l.get("year", "N/A")
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tag = l.get("tag", "[PRODUCTION-READY]")
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tag_color = "success" if "FOUNDATIONAL" in tag else "info" if "PRODUCTION" in tag else "warning"
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md += f" - [{l['title']}]({l['url']}) {l['description']} <span class='md-tag md-tag--{tag_color}'>{tag}</span>\n"
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color = "success" if "FOUNDATIONAL" in tag else "info" if "PRODUCTION" in tag else "warning"
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md += f" - **({year})** [{l['title']}]({l['url']}) {l['description']} <span class='md-tag md-tag--{color}'>{tag}</span>\n"
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md += "\n"
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with open(os.path.join(V2_DIR, f"{slug}.md"), "w") as f: f.write(md)
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async def _sync_enterprise_navigation(self, data: Dict[str, Dict]):
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@@ -258,9 +195,7 @@ class V2VisionEngine:
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new_nav = "\n".join(nav_items)
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updated_content = re.sub(r'nav:.*', new_nav, content, flags=re.DOTALL)
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with open("v2-mkdocs.yml", "w") as f: f.write(updated_content)
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log_event(" [OK] Enterprise Navigation fully synchronized.")
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except Exception as e:
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log_event(f" [!] Navigation Sync Error: {e}")
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except: pass
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if __name__ == "__main__":
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engine = V2VisionEngine()
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