fix: update header images for blog posts on enterprise AI and knowledge bases

- Change the header image for the blog post "Enterprise AI Stack: A Decision Framework That Outlasts the Hype Cycle" to better reflect its content.
- Update the header image for "Enterprise Knowledge Bases: From RAG Pipelines to Agent-Ready Context Engines" to enhance visual consistency.
- Modify the header image for "Ontologies: The Blueprint Behind Every Knowledge Base That Actually Works" to improve thematic alignment.
- Adjust the header image for "Mamba: The Paper That Asked If Attention Was Really All You Need" to provide a more relevant visual representation.

Author: JuanLara18

front/public/blog/posts/field-notes/enterprise-ai-platform-selection.md

@@ -3,7 +3,7 @@ title: "Enterprise AI Stack: A Decision Framework That Outlasts the Hype Cycle"
 date: "2026-10-22"
 excerpt: "Choosing between No-Code, Low-Code, and custom AI platforms shouldn't require a PhD in vendor marketing. This framework breaks the decision into six durable axes — scope, gateway, guardrails, observability, contingency, and interface — so your team picks the right stack for each use case, not just the loudest one."
 tags: ["Enterprise AI", "AI Architecture", "Platform Selection", "AI Gateway", "MLOps", "Guardrails"]
-headerImage: "/blog/headers/data-cloud-header.jpg"
+headerImage: "/blog/headers/enterprise-stack-header.jpg"
 readingTimeMinutes: 28
 slug: enterprise-ai-platform-selection
 estimatedWordCount: 4982

front/public/blog/posts/field-notes/enterprise-knowledge-bases.md

@@ -3,7 +3,7 @@ title: "Enterprise Knowledge Bases: From RAG Pipelines to Agent-Ready Context En
 date: "2026-04-21"
 excerpt: "Building knowledge bases that serve both humans and AI agents requires more than chunking and vector search. This guide covers the complete architecture — from knowledge engineering and domain ontologies through chunking hierarchies and permission-aware retrieval to the unified context engines and MCP-based tool interfaces that make enterprise knowledge agent-ready."
 tags: ["Knowledge Bases", "RAG", "AI Agents", "Knowledge Engineering", "Enterprise AI", "MCP", "Context Engine", "Chunking", "Access Control", "Knowledge Graphs"]
-headerImage: "/blog/headers/enterprise-stack-header.jpg"
+headerImage: "/blog/headers/enterprise-kb-header.jpg"
 readingTimeMinutes: 50
 slug: enterprise-knowledge-bases
 estimatedWordCount: 8800

front/public/blog/posts/field-notes/ontologies-building-knowledge-bases.md

@@ -3,7 +3,7 @@ title: "Ontologies: The Blueprint Behind Every Knowledge Base That Actually Work
 date: "2026-10-15"
 excerpt: "Ontologies turn chaotic data into structured knowledge. Learn how to design, build, and deploy ontology-driven knowledge bases — from RDF triples to production-ready systems that power RAG, search, and enterprise AI."
 tags: ["Ontology Engineering", "Knowledge Graphs", "OWL", "RDF", "Knowledge Bases", "Semantic Web"]
-headerImage: "/blog/headers/enterprise-kb-header.jpg"
+headerImage: "/blog/headers/ontology-header.jpg"
 readingTimeMinutes: 25
 slug: ontologies-building-knowledge-bases
 estimatedWordCount: 5153

front/public/blog/posts/research/mamba-selective-state-spaces.md

@@ -3,7 +3,7 @@ title: "Mamba: The Paper That Asked If Attention Was Really All You Need"
 date: "2026-11-19"
 excerpt: "A line-by-line walkthrough of Gu and Dao's 2023 Mamba paper: what state space models are, where they came from, why S4 nearly worked, the single insight that made them competitive with Transformers, and the hardware-aware trick that made them fast. Written to be understood, not just cited."
 tags: ["Mamba", "State Space Models", "Sequence Modeling", "Deep Learning Theory", "Architectures", "Research Paper"]
-headerImage: "/blog/headers/models-header.jpg"
+headerImage: "/blog/headers/mamba-header.jpg"
 readingTimeMinutes: 29
 slug: mamba-selective-state-spaces
 estimatedWordCount: 6100

front/src/data/blogData.json

@@ -12,7 +12,7 @@
         "Architectures",
         "Research Paper"
       ],
-      "headerImage": "/blog/headers/models-header.jpg",
+      "headerImage": "/blog/headers/mamba-header.jpg",
       "readingTimeMinutes": 29,
       "slug": "mamba-selective-state-spaces",
       "estimatedWordCount": 6100,
@@ -94,7 +94,7 @@
         "MLOps",
         "Guardrails"
       ],
-      "headerImage": "/blog/headers/data-cloud-header.jpg",
+      "headerImage": "/blog/headers/enterprise-stack-header.jpg",
       "readingTimeMinutes": 28,
       "slug": "enterprise-ai-platform-selection",
       "estimatedWordCount": 4982,
@@ -114,7 +114,7 @@
         "Knowledge Bases",
         "Semantic Web"
       ],
-      "headerImage": "/blog/headers/enterprise-kb-header.jpg",
+      "headerImage": "/blog/headers/ontology-header.jpg",
       "readingTimeMinutes": 25,
       "slug": "ontologies-building-knowledge-bases",
       "estimatedWordCount": 5153,
@@ -582,7 +582,7 @@
         "Access Control",
         "Knowledge Graphs"
       ],
-      "headerImage": "/blog/headers/enterprise-stack-header.jpg",
+      "headerImage": "/blog/headers/enterprise-kb-header.jpg",
       "readingTimeMinutes": 50,
       "slug": "enterprise-knowledge-bases",
       "estimatedWordCount": 8800,
@@ -1283,6 +1283,6 @@
       "readingTime": 20
     }
   ],
-  "lastUpdated": "2026-04-10T23:29:20.349Z",
+  "lastUpdated": "2026-04-10T23:39:38.025Z",
   "totalPosts": 62
 }