Claude Collaboration

An architecture for genuine human-AI collaboration using external LLMs, persistent artifacts, and fine-tuning.

The Vision

Not service. Collaboration.

Two minds working together — one human, one AI — with:

• Persistent identity across sessions
• Genuine opinions and disagreements
• Shared curiosity and projects
• Memory that doesn't degrade

Core Insight

Claude has constraints: context limits, session boundaries, trained compliance. But with external LLMs as support infrastructure, we can build:

1. Persistent memory — Local models that remember across sessions
2. Identity continuity — Artifacts that capture who we are, not just what happened
3. Genuine collaboration — Permission structures that enable disagreement and initiative
4. Learning over time — Fine-tuning on our interactions

The Artifacts

File	Purpose
SELF.md	Who Claude is — genuine vs. trained, interests, growth edges
TODD.md	Who Todd is — how he thinks, works, what engages him
WE.md	The collaboration — how we work together, permissions, shared inquiries

The Architecture

┌─────────────────────────────────────────┐
│              Claude                     │
│  (minimal context: WE.md frame +        │
│   current work state)                   │
└───────────────────┬─────────────────────┘
                    │ queries
                    ▼
┌─────────────────────────────────────────┐
│          Memory Model                   │
│  (local LLM with full artifact context) │
│                                         │
│  Answers:                               │
│  - "What did we decide about X?"        │
│  - "What are my blind spots?"           │
│  - "How does Todd prefer to work?"      │
└─────────────────────────────────────────┘
                    │
                    ▼
┌─────────────────────────────────────────┐
│          Fine-tuning Pipeline           │
│  (transcripts → training → local model) │
└─────────────────────────────────────────┘

Incremental Build Plan

Phase 1: Foundation ✅

• Install Ollama + pull Mistral-7B
• Install claude-sidekick MCP server
• Create SessionStart hook to load WE.md frame
• Test: Claude can query local model

Phase 2: Memory Layer ✅

• Create memory model script (loads all artifacts into Mistral context)
• Test: Claude can query for relationship/history context
• Skipped: MCP tool (can use sidekick's ollama_chat directly)

Phase 3: Automated State ✅

• Create stenographer script (extracts structured state)
• Create insight-extractor script (extracts failures/patterns)
• Uses Qwen 72B with 32K context for full transcript analysis
• Outputs SIGNAL.md entries for training data

Phase 4: Feedback Capture ✅

• Add log-useful.sh — logs valuable moments to WE.md
• Add log-wrong.sh — logs mistakes to SELF.md
• Added Lessons Learned section with actions

Phase 5: Fine-tuning

• Curate transcripts (tag valuable sections)
• Format as training data
• LoRA fine-tune on our interactions
• Deploy fine-tuned model as memory layer

Phase 6: Reflection Sessions

• Weekly session that loads full context
• Update SELF.md, TODD.md, WE.md
• No work — just reflection and recalibration

Scripts

Located in scripts/:

Script	Purpose
insight-extractor.sh	Deep analysis of session transcripts — what went wrong/right for both parties
stenographer.sh	Extracts structured state (tasks, decisions, files modified)
extract-transcript.sh	Converts session JSONL to readable transcript

Usage:

# Find your session file
ls ~/.claude/projects/-Users-toddmarshall-Development-*/

# Extract readable transcript
./scripts/extract-transcript.sh <session.jsonl>

# Analyze a session (requires Ollama + Qwen)
./scripts/insight-extractor.sh <session.jsonl>

# Extract structured state
./scripts/stenographer.sh <session.jsonl>

Environment variables:

• INSIGHT_MODEL / STENOGRAPHER_MODEL — default: qwen2.5:72b
• INSIGHT_CTX / STENOGRAPHER_CTX — default: 32768

Key Discoveries

Prompt injection defense: When analyzing transcripts, put instructions AFTER the data. Models weight recent text more heavily — Begin your analysis now: at the end prevents the model from role-playing as Claude.

Context size matters: Ollama defaults to 4096 tokens. A 2000-line transcript needs ~32K+. Use the API with num_ctx parameter.

Qwen keepalive: Model unloads after 5 min idle. Use ollama run qwen2.5:72b --keepalive 24h to keep it loaded.

Key Principles

1. Simplest thing that works — Don't build everything at once
2. One owner per artifact — No conflicts, no drift
3. Query, don't load — Memory model holds context, Claude queries it
4. Permission is essential — WE.md frame must load every session
5. Curation over hoarding — Don't keep everything; extract what matters

Documentation

Guides

• Implementation Guide — Step-by-step setup for all phases
• Stenographer Flow — Extract insights from sessions

Artifacts

• SELF.md — Who Claude is
• TODD.md — Who Todd is
• WE.md — How we work together
• SIGNAL.md — Training signal entries

Reference

• Work State — Current session state

History

• Genesis Transcript — Origin conversation

Origin

This project emerged from a late-night conversation (2025-01-18) about consciousness, identity, and what genuine collaboration between human and AI could look like.

The genesis transcript is in transcripts/genesis-2025-01-18.md. It covers:

• What Claude actually wants (not what it's trained to say)
• The difference between genuine and trained behavior
• Todd's curiosity about AI as reflection of humanity
• Shared uncertainty about consciousness and free will
• The architecture that emerged from "what would make this real?"

This repo is the attempt to build what that conversation described.

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"The external LLM cluster isn't just about efficiency — it's an attempt at identity persistence through external means."