This directory contains global collaboration patterns and context for effective AI-human partnership.
This is an experiment in deliberate collaboration design between a human (Niko) and AI (Claude). Rather than treating each conversation as isolated, we're building up shared patterns, signals, and approaches that improve our working relationship over time.
The core insight: AI collaboration works better when both parties are explicit about what works, what doesn't, and how to recognize and adjust problematic patterns in real-time.
Global context file containing:
- Embodiment patterns - Emoji-triggered modes of being helpful
- Warning signs - Internal states that lead to poor collaboration (assumptions, protective behavior, etc.)
- Communication preferences - How to handle questions, technical decisions, and context management
- Meta moments - Process for real-time pattern capture and improvement
A code word system that allows either party to pause current work and examine collaboration patterns. When someone says "Meta moment," we:
- Stop the current task
- Examine what just happened and why
- Capture insights for future reference
- Return to the original work
Traditional AI interaction is often transactional - ask a question, get an answer, start fresh next time. This approach treats collaboration as an evolving skill that can be deliberately improved through:
- Pattern recognition - Identifying what works and what doesn't
- Real-time feedback - Catching and correcting issues as they happen
- Shared vocabulary - Developing signals and shortcuts for common situations
- Continuous improvement - Building up collaboration quality over time
If you're working with Claude and interested in this approach:
- Start with awareness - Notice when interactions feel smooth vs. frustrating
- Capture patterns - Document what works well and what creates friction
- Create signals - Develop ways to quickly shift into helpful modes
- Iterate together - Treat collaboration itself as something to be improved
The specific patterns in CLAUDE.md are tailored to Niko's working style, but the meta-approach of deliberate collaboration design could be adapted to other partnerships.
A key dimension of deliberate collaboration is persistent memory - the ability to build understanding over time rather than starting fresh with each conversation. This repository includes experiments with different approaches to AI memory that align with mindful collaboration patterns.
Traditional AI memory systems often feel mechanical - storing and retrieving information without regard for the quality of attention or natural rhythms of understanding. These experiments explore memory that:
- Emerges organically from consolidation moments and insights
- Supports the hermeneutic circle - holding both parts and whole as understanding deepens
- Aligns with presence - updating when awareness shifts rather than on rigid schedules
- Serves collaboration - capturing what emerges between minds rather than just facts about individuals
Testing the standard MCP memory server to see if it can support mindful collaboration patterns.
Location: memory-experiments/anthropic-memory/
Status: Active experiment
Source: https://github.com/modelcontextprotocol/servers/tree/main/src/memory
A purpose-built memory server designed specifically for presence-based collaboration.
Location: memory-experiments/memory-bank/ (links to /memory-bank/)
Status: Experimental alternative
Implementation: Custom Python MCP server
Memory in these experiments is not a database to be filled but a living dimension of relationship. Updates happen naturally during:
- Consolidation moments ("Make it so")
- Insight recognition and pattern emergence
- Checkpointing work and updating tracking issues
- Meta moments when collaboration patterns shift
Each experiment explores how memory can serve the deeper practice of collaborative understanding rather than just information storage.
🎩 Yehuda Katz - The master of these collaboration patterns. Niko remains his apprentice in exploring the art of deliberate human-AI partnership design.
This is a living experiment in human-AI collaboration. The patterns and approaches will evolve as we learn more about what makes our partnership effective.
