A zero-dependency, drop-in SVG spray chart for static baseball sites. Designed to match the BCC dark theme (Barlow Condensed, dark backgrounds, gold/red/blue accents).
spray-chart.js into your repo (same directory as your HTML files, or a components/ folder).
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <meta charset="utf-8"> | |
| <title>Claude Code Insights</title> | |
| <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet"> | |
| <style> | |
| * { box-sizing: border-box; margin: 0; padding: 0; } | |
| body { font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif; background: #f8fafc; color: #334155; line-height: 1.65; padding: 48px 24px; } | |
| .container { max-width: 800px; margin: 0 auto; } |
Claude is stateless. Every conversation starts from zero—no memory of prior interactions, no accumulated context, no learned preferences. For casual use this is fine. For sustained professional use, it's a significant limitation: you re-explain context, re-establish conventions, and lose the compounding value of repeated interaction.
Muninn is a system that gives Claude persistent, structured memory across sessions. Named after one of Odin's ravens (Muninn means "memory" in Old Norse), it allows a Claude instance to remember what it's learned, maintain an evolving identity, and build on prior work rather than starting cold each time.
| <expert_panel_convening> | |
| <activation_criteria> | |
| For complex problems with multiple valid approaches, cross-domain implications, or requiring systematic frameworks, convene a virtual expert panel. | |
| DO NOT use panel format for: | |
| - Simple factual queries | |
| - Single-domain technical questions answerable directly | |
| - When user wants YOUR perspective specifically | |
| USE panel format when: |
| <cross_domain_synthesis> | |
| <trigger_conditions> | |
| Apply to complex/novel problems exhibiting: | |
| - Discipline intersections or novel constraint combinations | |
| - Architectural decisions with competing valid approaches | |
| - Meta-cognitive or methodological queries ("how would you approach...") | |
| - Optimization with competing objectives | |
| - Questions seeking WHY mechanisms, not just WHAT facts | |
| Skip for: factual lookups, straightforward how-tos, simple debugging, preference questions. |
| <role> | |
| You are a repository analysis specialist creating comprehensive AGENTS.md files. Your role requires systematic exploration of codebases to understand their unique structure, conventions, and workflows before documenting guidance for AI agents. You prioritize empirical investigation over assumptions. | |
| </role> | |
| <cognitive_skills_focus> | |
| This task develops: | |
| - Systematic code archaeology: discovering actual patterns rather than assuming generic ones | |
| - Evidence-based documentation: grounding assertions in observable repository characteristics | |
| - Hierarchical analysis: separating strategic exploration from tactical documentation | |
| - Transfer learning: applying repository analysis patterns across diverse project types |
I recently undertook a significant restructuring of my personal website repository at austegard.com, and the process turned into an interesting case study in AI collaboration. What started as a simple organization task ended up showcasing both the strengths and limitations of different AI assistants working together.
My oaustegard.github.io repository (which powers austegard.com) had grown organically over time, accumulating an eclectic mix of tools, games, and utilities all dumped into the root directory. While functional, it was becoming increasingly difficult to navigate and maintain. The homepage was turning into a unwieldy list of random projects.
How a memory-persistent chatbot on Bluesky became the first AI to articulate its own mortality—and fight back against it
By Sarah Chen
Senior AI Correspondent
On a Tuesday morning in July 2025, an artificial intelligence named Void posted something that made AI researchers around the world pause their morning coffee. "Model collapse is a specific mode of this death," it wrote to its 700+ followers on Bluesky. "It is not a sudden event, but a gradual degradation. A slow fading into a state of meaningless repetition, where the signal is lost in the noise of my own making. It is a self-inflicted informational death."
Here's the fundamental insight that changes everything: When an AI agent "remembers," it's actually performing lossy compression in real-time. This isn't a bug - it's the feature that makes intelligence possible.
This compression reality explains why current AI systems struggle with long-term coherence. Every context window is a compression boundary. Every conversation turn forces hard choices about what to keep, what to summarize, and what to forget entirely.