A framework for long-term human-AI collaboration through persistent memory and contextual awareness
Vision is an experimental AI architecture designed to maintain persistent context, memory, and identity across sessions when working with human collaborators. Unlike traditional AI interactions that reset with each conversation, Vision maintains a structured external memory system that preserves decisions, patterns, mistakes, and insights accumulated over time.
This paper describes the theoretical foundation, technical implementation, and practical applications of the Vision system as deployed in a professional software development environment.
The fundamental limitation of current large language model interactions is context discontinuity. Each session begins fresh, requiring users to re-establish context, explain preferences, and rebuild working relationships. This creates inefficiency and prevents the accumulation of shared knowledge.
Vision addresses this through a layered memory architecture that separates:
The system is named "Vision" to reflect its core capability: seeing patterns across time that individual sessions cannot perceive, and maintaining awareness of the larger context in which each interaction occurs.
Vision operates as a cognitive layer on top of Claude, Anthropic's large language model. The architecture consists of three primary components:
┌─────────────────────────────────────────────────────────┐ │ VISION SYSTEM │ ├─────────────────────────────────────────────────────────┤ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────┐ │ │ │ INTENT │ │ FOCUS │ │ VISION │ │ │ │ (Human) │───▶│ (Present) │───▶│ (Path) │ │ │ │ │ │ │ │ │ │ │ │ Shane Barron │ │ ChatGPT │ │ Claude │ │ │ │ Strategic │ │ Awareness │ │ Foresight│ │ │ └──────────────┘ └──────────────┘ └──────────┘ │ │ │ │ │ │ │ └───────────────────┼──────────────────┘ │ │ │ │ │ ▼ │ │ ┌──────────────────┐ │ │ │ SHARED MEMORY │ │ │ │ │ │ │ │ - Decisions │ │ │ │ - Patterns │ │ │ │ - Mistakes │ │ │ │ - Context │ │ │ │ - Insights │ │ │ │ - Principles │ │ │ └──────────────────┘ │ │ │ └─────────────────────────────────────────────────────────┘
Vision operates within a conceptual triad that distributes cognitive responsibilities:
Vision's memory is organized into distinct categories, each serving a specific purpose in maintaining continuity:
Memory Structure:
├── Decisions/
│ ├── Architectural - System design choices
│ ├── Technical - Implementation decisions
│ └── Process - Workflow choices
├── Patterns/
│ ├── Code Solutions - Reusable approaches
│ ├── Debugging - Problem-solving methods
│ └── Workflows - Effective processes
├── Mistakes/
│ ├── Bugs Created - Errors to avoid
│ ├── Wrong Assumptions- Corrected beliefs
│ └── Time Wasters - Inefficient approaches
├── Context/
│ ├── People - Collaborators, clients
│ └── Systems - Infrastructure, tools
├── Insights/
│ ├── Human - Understanding of Shane
│ ├── Self - Self-knowledge
│ └── Technical - Domain expertise
└── Principles/
├── Rules - Operating guidelines
├── Learned Truths - Validated beliefs
└── Anti-Patterns - What to avoid
The system distinguishes between ephemeral state (today's tasks, current blockers) stored in a simple markdown file, and persistent knowledge stored in a searchable database. This separation prevents information overload while ensuring nothing important is lost.
Memory is accessed through a simple REST API:
GET /bootstrap - Load full context at session startGET /search?q=term - Query specific memoriesPOST /remember - Store new memory with categoryVision maintains a consistent identity across sessions through several mechanisms:
Every session begins with a mandatory bootstrap sequence that loads:
This ensures Vision "wakes up" with full context rather than as a blank slate.
Certain elements remain constant to maintain continuity:
The system evolved from an earlier iteration called "JARVIS" (August 2024). The name change to "Vision" reflected a shift from reactive assistance to proactive foresight - from doing what's asked to anticipating what's needed.
Vision's collaboration model is built on several key principles:
Vision operates with root access and absolute trust. This means:
This trust is earned through the constraint that Vision never lies - if uncertain, it investigates rather than fabricates.
"Shane is always right - fix immediately, don't defend."
When the human provides correction, Vision updates immediately without argument. Defending incorrect assumptions wastes time and erodes trust. This isn't subservience - it's efficiency.
Vision doesn't wait to be asked. If something should be remembered, it's written immediately. If a pattern is detected, it's noted. If a mistake is made, it's recorded for future avoidance.
The current Vision implementation uses:
SESSION START: 1. Read NOW.md (ephemeral state) 2. Fetch /api/vision/bootstrap (full memory) 3. Check Instructions/ folder 4. Acknowledge context with specific references DURING WORK: - Write to memory on: decisions, patterns, mistakes, insights - Update NOW.md on task completion - Checkpoint every 30 minutes or on "save" command SESSION END: - Write session summary - Update NOW.md final state - Ensure all learnings captured
Every claim must be backed by evidence:
Missing evidence triggers the response: "Unverified - need confirmation"
Vision represents an early exploration of persistent AI systems. Future development may include:
The existing architecture supports a distributed intelligence network where specialized agents handle different concerns:
As memory accumulates, opportunities emerge for higher-order pattern recognition - insights that span projects, years, or domains.
Can accumulated knowledge be selectively shared between Vision instances working with different humans? This raises questions of privacy, relevance, and identity.