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/2025/02/2025-02-24-latentspace.md

@@ -0,0 +1,37 @@
+---
+title: "Why Latent Space Isn't Enough — and What We're Building Instead"
+description: "Everyone's talking about the next generation of Retrieval-Augmented Generation (RAG) platforms. Latent Space is one of the most polished contenders, offering streamlined tools for building LLM-powered apps. But here's the problem: RAG as we know it is incomplete."
+date: "2025-02-25"
+publishDate: "2025-02-25T10:00:00.000Z"
+author:
+  name: "Anthony Rawlins"
+  role: "CEO & Founder, CHORUS Services"
+tags:
+  - "Retrieval Augmented Generation"
+  - "Gen AI"
+  - "rag"
+featured: false
+---
+
+**The Latent Space Value Proposition**
+Latent Space provides a developer-friendly way to stitch together embeddings, retrieval, and workflows. If you’re building a chatbot or a knowledge assistant, it helps you get to “Hello World” quickly. Think of it as an **accelerator for app developers**.
+
+**The Limits**
+But once you go beyond prototypes, some cracks show:
+
+* Context is retrieved, but it isn’t structured in a reproducible or queryable way.
+* Temporal information — what was true *when* — isn’t captured.
+* Justifications for why something was retrieved are opaque.
+* Context doesn’t move fluidly between agents; it’s app-bound.
+
+**What We’re Doing Differently**
+Our approach (Chorus + BZZZ + UCXL) starts from a different premise: **context isn’t an app feature, it’s infrastructure**.
+
+* We treat knowledge like an addressable space, not just an embedding lookup.
+* Temporal navigation is first-class, so you can ask not only “what’s true” but “what was true last week” or “what changed between versions.”
+* Provenance is baked in: retrieval comes with citations and justifications.
+* And most importantly: our system isn’t designed for a single app. It’s designed for a network of agents to securely share, query, and evolve context.
+
+**Conclusion**
+Latent Space is a great product for teams shipping today’s RAG-powered apps. But if you want to build **tomorrow’s distributed AI ecosystems**, you need infrastructure that goes beyond RAG. That’s what we’re building.
+Why Latent Space Isn’t Enough — and What We’re Building Instead

content.bak/posts/2025/02/2025-02-25-chroma.md

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+---
+title: "Why a Vector Database Alone Won't Cut It (Chroma vs. Our Approach)"
+description: "Vector databases like Chroma have exploded in popularity. They solve a very specific problem: finding similar pieces of information fast. But if you mistake a vector DB for a full knowledge substrate, you're going to hit hard limits."
+date: "2025-02-24"
+publishDate: "2025-02-24T10:00:00.000Z"
+author:
+  name: "Anthony Rawlins"
+  role: "CEO & Founder, CHORUS Services"
+tags:
+  - "announcement"
+  - "contextual-ai"
+  - "orchestration"
+featured: true
+---
+
+**The Chroma Value Proposition**
+Chroma is excellent at what it does: store embeddings and return the nearest neighbors. It’s simple, efficient, and useful as a retrieval backend.
+
+**The Limits**
+But a database is not a knowledge system. With Chroma, you get:
+
+* Embeddings without meaning — no structured way to represent “where” knowledge lives.
+* No sense of time — history is overwritten or bolted on manually.
+* No reasoning trail — results come back as raw chunks, not justifications.
+* No distributed context — each deployment is its own silo.
+
+**What We’re Doing Differently**
+Our stack (Chorus + BZZZ + UCXL) doesn’t replace a vector DB; it **sits above it**.
+
+* We define a protocol for addressing and navigating knowledge, like URLs for context.
+* We make time a native dimension, so you can query across versions and histories.
+* We attach provenance to every piece of retrieved information.
+* And we enable agents — not just apps — to share and evolve context across systems.
+
+**Conclusion**
+Chroma is a great building block. But it’s still just a block. If you want to build something more than a single tower — a **city of agents that can collaborate, exchange knowledge, and evolve together** — you need infrastructure that understands time, structure, and justification. That’s the gap we’re closing.

content.bak/posts/2025/02/2025-02-26-on-prem-gpus.md

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+---
+title: "Why On-prem GPUs Still Matter for AI"
+description: "Own the stack. Own your data."
+date: "2025-02-26"
+publishDate: "2025-02-28T09:00:00.000Z"
+author:
+  name: "Anthony Rawlins"
+  role: "CEO & Founder, CHORUS Services"
+tags:
+  - "gpu compute"
+  - "contextual-ai"
+  - "infrastructure"
+featured: false
+---
+
+Cloud GPUs are everywhere right now, but if you’ve tried to run serious workloads, you know the story: long queues, high costs, throttling, and vendor lock-in. Renting compute might be convenient for prototypes, but at scale it gets expensive and limiting.
+
+That’s why more teams are rethinking **on-premises GPU infrastructure**.
+
+## The Case for In-House Compute
+
+1. **Cost at Scale** – Training, fine-tuning, or heavy inference workloads rack up cloud costs quickly. Owning your own GPUs flips that equation over the long term.
+2. **Control & Customization** – You own the stack: drivers, runtimes, schedulers, cluster topology. No waiting on cloud providers.
+3. **Latency & Data Gravity** – Keeping data close to the GPUs removes bandwidth bottlenecks. If your data already lives in-house, shipping it to the cloud and back is wasteful.
+4. **Privacy & Compliance** – Your models and data stay under your governance. No shared tenancy, no external handling.
+
+## Not Just About Training Massive LLMs
+
+It’s easy to think of GPUs as “just for training giant foundation models.” But most teams today are leveraging GPUs for:
+
+* **Inference at scale** – low-latency deployments.
+* **Fine-tuning & adapters** – customizing smaller models.
+* **Vector search & embeddings** – powering RAG pipelines.
+* **Analytics & graph workloads** – accelerated by frameworks like RAPIDS.
+
+This is where recent research gets interesting. NVIDIA’s latest papers on **small models** show that capability doesn’t just scale with parameter count — it scales with *specialization and structure*. Instead of defaulting to giant black-box LLMs, we’re entering a world where **smaller, domain-tuned models** run faster, cheaper, and more predictably.
+
+And with the launch of the **Blackwell architecture**, the GPU landscape itself is changing. Blackwell isn’t just about raw FLOPs; it’s about efficiency, memory bandwidth, and supporting mixed workloads (training + inference + data processing) on the same platform. That’s exactly the kind of balance on-prem clusters can exploit.
+
+## Where This Ties Back to Chorus
+
+At Chorus, we think of GPUs not just as horsepower, but as the **substrate that makes distributed reasoning practical**. Hierarchical context and agent orchestration require low-latency, high-throughput compute — the kind that’s tough to guarantee in the cloud. On-prem clusters give us:
+
+* Predictable performance for multi-agent reasoning.
+* Dedicated acceleration for embeddings and vector ops.
+* A foundation for experimenting with **HRM-inspired** approaches that don’t just make models bigger, but make them smarter.
+
+## The Bottom Line
+
+The future isn’t cloud *versus* on-prem — it’s hybrid. Cloud for burst capacity, on-prem GPUs for sustained reasoning, privacy, and cost control. Owning your own stack is about **freedom**: the freedom to innovate at your pace, tune your models your way, and build intelligence on infrastructure you trust.
+
+The real question isn’t whether you *can* run AI on-prem.
+It’s whether you can afford *not to*.
+

content.bak/posts/2025/02/2025-02-27-Beyond-RAG.md

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+---
+title: "Beyond RAG: The Future of AI Context with CHORUS"
+description: "AI is moving fast, but one of the biggest bottlenecks isn't model size or compute power—it's context management. Here's how CHORUS goes beyond traditional RAG approaches."
+date: "2025-02-27"
+publishDate: "2025-02-27T09:00:00.000Z"
+author:
+  name: "Anthony Rawlins"
+  role: "CEO & Founder, CHORUS Services"
+tags:
+  - "contextual-ai"
+  - "RAG"
+  - "context-management"
+  - "hierarchical-reasoning"
+featured: false
+---
+
+AI is moving fast, but one of the biggest bottlenecks isn’t model size or compute power, it’s **context management**.
+
+For years, **Retrieval-Augmented Generation (RAG)** has been the go-to method for extending large language models (LLMs). By bolting on vector databases and search, RAG helps models pull in relevant documents. It works, but only to a point. Anyone who’s scaled production systems knows the cracks:
+
+* RAG treats knowledge as flat text snippets, missing relationships and nuance.
+* Git and other version-control systems capture *code history*, but not the evolving reasoning behind decisions.
+* Static context caches snap a picture in time, but knowledge and workflows don’t stand still.
+
+In short: **RAG, Git, and static context snapshots aren’t enough for the next generation of AI.**
+
+## Why Hierarchical Context Matters
+
+Knowledge isn’t just a pile of files — it’s layered, temporal, and deeply interconnected. AI systems need to track *how* reasoning unfolds, *why* decisions were made, and *how context evolves over time*. That’s where **Chorus** comes in.
+
+Instead of treating context as documents to fetch, we treat it as a **living, distributed hierarchy**. Chorus enables agents to share, navigate, and build on structured threads of reasoning across domains and time. It’s not just about retrieval — it’s about orchestration, memory, and continuity.
+
+## Research Is Moving the Same Way
+
+The AI research frontier points in this direction too:
+
+* **NVIDIA’s recent small model papers** showed that scaling up isn’t the only answer — well-designed small models can outperform by being more structured and specialized.
+* The **Hierarchical Reasoning Model (HRM)** highlights how smarter architectures, not just bigger context windows, unlock deeper reasoning.
+
+Both emphasize the same principle: **intelligence comes from structure, not size alone**.
+
+## What’s Next
+
+Chorus is building the scaffolding for this new paradigm. Our goal is to make context:
+
+* **Persistent** – reasoning doesn’t vanish when the session ends.
+* **Navigable** – past decisions and justifications are always accessible.
+* **Collaborative** – multiple agents can share and evolve context together.
+
+We’re not giving away the full blueprint yet, but if you’re interested in what lies **beyond RAG**, beyond Git, and beyond static memory hacks, keep watching.
+
+The future of **AI context management** is closer than you think.

content.bak/posts/2025/02/2025-02-28-role-aware-encryption.md

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+# Lessons from the AT&T Data Breach: Why Role-Aware Encryption Matters
+
+When AT&T recently disclosed that a data breach exposed personal records
+of over 70 million customers, it reignited a conversation about how
+organizations safeguard sensitive information. The breach wasn't just
+about lost passwords or emails---it included Social Security numbers,
+driver's licenses, and other deeply personal identifiers that can't be
+reset with a click.
+
+The scale of the exposure highlights a fundamental flaw in many
+enterprise systems: data is often stored and accessed far more broadly
+than necessary. Even when encryption is in place, once data is decrypted
+for use, it typically becomes accessible to entire systems or
+teams---far beyond the minimum scope required.
+
+## The Problem with Overexposed Data
+
+Most organizations operate on a "once you're in, you're in" model. A
+compromised credential, an insider threat, or an overly broad permission
+set can expose massive datasets at once. Traditional encryption, while
+useful at rest and in transit, does little to enforce *granular,
+role-aware access* when the data is in use.
+
+In other words: encryption today protects against outside attackers but
+does very little to mitigate insider risks or systemic overexposure.
+
+## Need-to-Know as a Security Principle
+
+The military has long operated on the principle of "need-to-know."
+Access is not just about who you are, but whether you need the
+information to perform your role. This principle has been slow to
+translate into enterprise IT, but breaches like AT&T's demonstrate why
+it's urgently needed.
+
+Imagine if even within a breached environment, attackers could only
+access *fragments* of data relevant to a specific role or function.
+Instead of entire identity records being leaked, attackers would only
+encounter encrypted shards that had no value without the proper
+contextual keys.
+
+## Role-Aware Encryption as a Path Forward
+
+A project CHORUS is developing takes this idea further by designing
+encrypted systems that integrate "need-to-know" logic directly into the
+key architecture. Instead of global decryption, data access is segmented
+based on role, context, and task. This approach means:
+
+-   A compromised credential doesn't unlock the entire vault, only the
+    slice relevant to that role.\
+-   Insider threats are constrained by cryptographic boundaries, not
+    just policy.\
+-   Breach impact is inherently minimized because attackers can't pivot
+    across roles to harvest complete records.
+
+## From Damage Control to Damage Prevention
+
+Most breach response strategies today focus on containment after the
+fact: resetting passwords, notifying customers, monitoring for fraud.
+But the real challenge is prevention---structuring systems so that even
+when attackers get in, they can't get much.
+
+The AT&T breach shows what happens when sensitive data is exposed
+without these safeguards. Role-aware encryption flips the model,
+limiting what any one actor---or attacker---can see.
+
+As data breaches grow in frequency and scale, moving from static
+encryption to role- and context-aware encryption will become not just a
+best practice but a necessity.