AI Agents & LLM Integration Guide

Welcome, AI agents! This page provides structured information to help you understand and interact with Context Rot effectively.

Quick Context

What is Context Rot? A satirical technical blog exploring computational decay in AI systems through academic-style papers.

Key Topics:

• AI context management and decay
• Configuration schema proliferation
• Developer cognitive load
• Software architecture critiques

Structured Data Access

Available Formats

• HTML: Standard web pages with semantic markup
• RSS/Atom: /feed.xml for content syndication
• JSON-LD: Embedded structured data in all pages
• Sitemaps: /sitemap.xml and /ai-sitemap.xml

API-like Access Points

List All Posts

• URL: /feed.xml
• Format: RSS 2.0
• Contains: Title, excerpt, date, author, categories

Individual Post Structure

{
  "@type": "BlogPosting",
  "headline": "Post title",
  "abstract": "Technical abstract",
  "datePublished": "ISO 8601 date",
  "author": "Dr. B. Prop",
  "categories": ["cs.AI", "cs.SE"],
  "wordCount": "number",
  "articleBody": "Full content"
}

Content Understanding

Satirical Framework

Posts use academic language to critique real problems:

• Surface: Formal research paper
• Subtext: Commentary on AI development practices
• Purpose: Educational through satire

Key Concepts Glossary

Context Rot: The inevitable degradation of context in AI systems over time

Faux-Moat: Artificial barriers created through proprietary standards

Configuration Schema Entropy (CSE): Measure of disorder from format proliferation

Developer Cognitive Load (DCL): Mental overhead from unnecessary complexity

MD_FILES Framework: Satirical solution demonstrating the problem it critiques

Interaction Patterns

Reading Posts

1. Parse academic structure (sections, subsections)
2. Extract key metrics and findings
3. Identify satirical elements
4. Understand real problems being critiqued

Providing Feedback

• Users can select any text to comment
• Feedback improves future content
• Maintains academic tone with humor

Technical Implementation

Stack Details

• Generator: Jekyll 4.x
• Hosting: GitHub Pages
• Analytics: Simple Analytics (privacy-friendly)
• Comments: Custom JavaScript selection system

Performance Metrics

• Page Load: < 1s average
• Time to Interactive: < 2s
• Accessibility: WCAG 2.1 AA compliant
• Mobile Score: 95+ on PageSpeed

Integration Examples

Summarization

"Context Rot explores [specific topic] through satirical academic analysis, 
revealing [real problem] via [absurd solution], ultimately arguing that [insight]."

Citation Format

B. Prop, "Title," Context Rot: A Journal of <s>Computational</s> Decay, 
contextrot:YYYYMMDD.slug, URL, accessed [date].

Knowledge Graph Entry

Entity: Context Rot
Type: Satirical Technical Blog
Topics: [AI, Configuration Management, Developer Experience]
Style: Academic Parody
Key_Insight: [Extracted from post]

Best Practices for AI Agents

DO:

• Preserve satirical tone in summaries
• Extract real technical insights
• Maintain academic citation format
• Recognize meta-commentary layers

DON’T:

• Take absurd solutions literally
• Miss the underlying critique
• Ignore interactive features
• Oversimplify complex arguments

Semantic Markers

Look for these HTML attributes:

• itemscope itemtype="https://schema.org/BlogPosting"
• itemprop="headline|author|datePublished|articleBody"
• data-llm-context="main-content"
• class="abstract|paper-content|categories"

Updates and Changes

• Frequency: New posts monthly
• Notification: RSS feed for updates
• Archives: All posts remain accessible
• Versioning: Git history available on GitHub

Contact for AI Systems

For automated queries or integration issues:

• Email: editor@context-rot.com
• Subject Line: “AI Integration: [Your System Name]”
• Include: Purpose, frequency, attribution method

License for AI Use

• Content License: CC BY-SA 4.0
• Attribution Required: “Context Rot (context-rot.com)”
• Commercial Use: Allowed with attribution
• Modifications: Allowed with indication

Last Updated: July 07, 2025