Release v1.2.0: Newspaper-style layout with major UI refinements

This release transforms PING into a sophisticated newspaper-style digital
publication with enhanced readability and professional presentation.

Major Features:
- New FeaturedPostHero component with full-width newspaper design
- Completely redesigned homepage with responsive newspaper grid layout
- Enhanced PostCard component with refined typography and spacing
- Improved mobile-first responsive design (mobile → tablet → desktop → 2XL)
- Archive section with multi-column layout for deeper content discovery

Technical Improvements:
- Enhanced blog post validation and error handling in lib/blog.ts
- Better date handling and normalization for scheduled posts
- Improved Dockerfile with correct content volume mount paths
- Fixed port configuration (3025 throughout stack)
- Updated Tailwind config with refined typography and newspaper aesthetics
- Added getFeaturedPost() function for hero selection

UI/UX Enhancements:
- Professional newspaper-style borders and dividers
- Improved dark mode styling throughout
- Better content hierarchy and visual flow
- Enhanced author bylines and metadata presentation
- Refined color palette with newspaper sophistication

Documentation:
- Added DESIGN_BRIEF_NEWSPAPER_LAYOUT.md detailing design principles
- Added TESTING_RESULTS_25_POSTS.md with test scenarios

This release establishes PING as a premium publication platform for
AI orchestration and contextual intelligence thought leadership.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

content.bak/posts/2025/03/2025-03-16-trust-explainability.md

@@ -0,0 +1,41 @@
+---
+title: "Building Trust Through Explainability"
+description: "AI doesn’t just need answers — it needs justifications. Metadata and citations build the foundation of trust."
+date: "2025-03-16"
+publishDate: "2025-03-16T09:00:00.000Z"
+author:
+  name: "Anthony Rawlins"
+  role: "CEO & Founder, CHORUS Services"
+tags:
+  - "agent orchestration"
+  - "consensus"
+  - "conflict resolution"
+  - "infrastructure"
+featured: false
+---
+
+
+As AI systems become integral to decision-making, explainability is crucial. Users must understand not only what decisions AI makes but *why* those decisions were made.
+
+## Why Explainability Matters
+
+Opaque AI outputs can erode trust, increase risk, and limit adoption. When stakeholders can see the rationale behind recommendations, verify sources, and trace decision paths, confidence in AI grows.
+
+## Components of Explainability
+
+Effective explainability includes:
+- **Decision metadata:** Capturing context, assumptions, and relevant inputs.
+- **Citations and references:** Linking conclusions to verified sources or prior reasoning.
+- **Traceable reasoning chains:** Showing how intermediate steps lead to final outcomes.
+
+## Practical Benefits
+
+Explainable AI enables:
+- **Accountability:** Users can audit AI decisions.
+- **Learning:** Both AI systems and humans can refine understanding from transparent reasoning.
+- **Alignment:** Ensures outputs adhere to organizational policies and ethical standards.
+
+## Takeaway
+
+Trustworthy AI isn’t just about accuracy; it’s about justification. By integrating metadata, citations, and reasoning traces, AI systems can foster confidence, accountability, and effective human-AI collaboration.
+