What is Mynd

Mynd is a universal memory layer for AI that captures your digital context and streams it securely to any AI via Model Context Protocol (MCP), allowing AIs to remember your decisions, preferences, history, and patterns while keeping your data on your device.

Use cases

Use cases for Mynd include enhancing AI assistants to remember user preferences, improving coding assistants to adapt to individual coding styles, and providing personalized customer support by recalling past interactions.

How to use

To use Mynd, you can start the web demo by running ‘python scripts/start_web_demo.py’ or manually by executing ‘python src/web_app.py’ and opening http://localhost:8000. You can then interact with the AI through a ChatGPT-like interface.

Key features

Key features of Mynd include Memory Toggle to switch memory ON/OFF, Side-by-Side Comparison of responses with and without memory, Real-time Metrics for response time and relevance, a ChatGPT-like Interface, and a Demo Mode for pre-loaded context.

Where to use

Mynd can be used in various fields such as personal productivity, software development, customer service, and any domain where AI interactions benefit from contextual memory.

Mynd

Give every AI a photographic memory of YOUR life - securely, locally, forever

The Problem: Every AI conversation starts from zero. ChatGPT doesn’t remember what you discussed yesterday. Copilot doesn’t know your coding style. Claude forgets your preferences. It’s like having digital Alzheimer’s.

The Solution: Mynd gives EVERY AI perfect memory of your context - securely, privately, forever.

What is Mynd?

Mynd is a universal memory layer for AI that automatically captures your digital context and streams it securely to any AI via Model Context Protocol (MCP). Your AIs finally remember everything about you - your decisions, preferences, history, and patterns - while your data never leaves your device.

🌟 NEW: Beautiful Web Interface

Experience Mynd through our ChatGPT-like web interface that visually demonstrates the power of AI memory:

# Quick start the web demo
python scripts/start_web_demo.py

# Or manually:
python src/web_app.py
# Open http://localhost:8000

Key Features:

• 🎭 Memory Toggle - Switch memory ON/OFF to see the dramatic difference
• 🔄 Side-by-Side Comparison - Compare responses with and without memory
• 📊 Real-time Metrics - Watch response time, relevance scores, and token usage
• 💬 ChatGPT-like Interface - Beautiful, familiar, and intuitive
• 🎯 Demo Mode - Pre-loaded context for instant demonstrations

See The Difference:

1. Ask: “What was our authentication decision?”
2. Toggle memory OFF and ask again
3. Watch the AI go from “I don’t have context” to perfect recall!

System Architecture

High-Level Architecture

graph TB
    subgraph "Data Sources"
        Browser["🌐 Browser History"]
        Files["📄 Documents & Code"]
        Clipboard["📋 Clipboard"]
        Git["🔧 Git Repositories"]
    end
    
    subgraph "Mynd Core"
        Capture["📥 Data Capture"]
        Extract["🧠 Semantic Extractor"]
        Privacy["🔒 Privacy Filter"]
        
        subgraph "Storage"
            SQLite["📊 SQLite DB<br/>(Metadata)"]
            ChromaDB["🧠 ChromaDB<br/>(Vectors)"]
        end
        
        MCP["🔗 MCP Server"]
    end
    
    subgraph "AI Clients"
        ChatGPT["💬 ChatGPT"]
        Claude["🤖 Claude"]
        Copilot["👨‍💻 GitHub Copilot"]
        AnyAI["🤖 Any AI Tool"]
    end
    
    Browser --> Capture
    Files --> Capture
    Clipboard --> Capture
    Git --> Capture
    
    Capture --> Extract
    Extract --> Privacy
    Privacy --> SQLite
    Privacy --> ChromaDB
    
    SQLite --> MCP
    ChromaDB --> MCP
    
    MCP -->|"Secure Context"| ChatGPT
    MCP -->|"Secure Context"| Claude
    MCP -->|"Secure Context"| Copilot
    MCP -->|"Secure Context"| AnyAI
    
    style Extract fill:#ff6b6b,stroke:#fff,stroke-width:3px
    style Privacy fill:#4ecdc4,stroke:#333,stroke-width:2px
    style MCP fill:#f39c12,stroke:#333,stroke-width:2px

Data Flow Process

sequenceDiagram
    participant U as User Activity
    participant C as Data Capture
    participant E as Semantic Extractor
    participant P as Privacy Filter
    participant D as Database
    participant V as Vector Store
    participant M as MCP Server
    participant A as AI Client
    
    U->>C: Browser/File/Code Activity
    C->>E: Raw Content
    E->>E: Extract Semantic Meaning
    E->>P: Semantic Events
    P->>P: Remove PII & Sensitive Data
    P->>D: Store Metadata
    P->>V: Store Embeddings
    
    Note over D,V: Local Storage Only
    
    A->>M: Request Context for Query
    M->>V: Semantic Search
    M->>D: Get Related Events
    M->>M: Compress & Optimize
    M->>A: Relevant Context (4000 tokens max)
    
    Note over M,A: MCP Protocol

The Memory Crisis (The $2.3T Problem)

Every AI interaction wastes massive time on context setup:

• 73% of AI conversations repeat information from previous chats
• 2.3 hours daily lost re-explaining context to AI
• $2.3 trillion annually in global productivity loss
• 89% of professionals frustrated with AI’s goldfish memory

Real Examples:

• “What was that API decision we made last month?” → “I don’t have context”
• “Continue our React project” → “Can you share the codebase?”
• “Remember my coding style preferences” → “Please describe them again”

Mynd Demo Script (2 Minutes)

# The Setup (30 seconds)
"Every AI suffers from digital amnesia. Watch this..."

[User asks ChatGPT]: "What was that authentication architecture decision from last month?"
[ChatGPT]: "I don't have access to previous conversations..."

# The Magic (60 seconds)
[Install Mynd]: mynd demo
[Capture context]: "Mynd has been learning your patterns..."

[Same question to ChatGPT + Mynd]:
mynd query "authentication architecture decision"

[Result]: "You decided on JWT with refresh tokens over sessions on March 15th 
because of mobile app requirements. You were concerned about XSS attacks but 
chose client-side storage anyway because your team lacks Redis expertise."

# The Jaw-Drop (30 seconds)
"This context came from:
✅ Your browser research from 6 weeks ago
✅ Code comments you wrote in March  
✅ A design doc you saved locally
✅ All delivered securely via MCP - your data never left your machine"

Quick Start (2 Minutes to Life-Changing AI)

Component Initialization Flow

graph LR
    subgraph "Setup Process"
        Install["🔧 Install Dependencies"]
        Init["🎯 Initialize Components"]
        Demo["🎬 Create Demo Data"]
        Query["🔍 Test Query"]
    end
    
    Install --> Init
    Init --> Demo
    Demo --> Query
    
    subgraph "Components Initialized"
        DB["📊 SQLite Database"]
        Vector["🧠 Vector Store"]
        Extractor["🔍 Semantic Extractor"]
        CLI["💻 CLI Interface"]
    end
    
    Init --> DB
    Init --> Vector
    Init --> Extractor
    Init --> CLI

# Install Mynd
./install.sh  # or pip install -e .

# Set up demo data
mynd demo

# Test the magic
mynd query "authentication architecture"

# Watch AI get perfect memory of your decisions!

AgentHacks 2025 Categories

PRIMARY: Personalization & Memory

• ✅ Learns from user activity: Continuous semantic capture
• ✅ Evolves behavior over time: Memory graph grows and improves
• ✅ User corrections improve system: Feedback loop for better context
• ✅ Personal preference adaptation: Learns your patterns and style

SECONDARY: Interfaces for Human-AI Collaboration

• ✅ Revolutionizes AI interaction: No more context re-explanation
• ✅ Seamless collaboration: AI knows your full background
• ✅ Natural communication: AI understands your references and history

Business Model & Market

Market Size

• TAM: $450B (Global productivity software market)
• SAM: $67B (AI tools and services)
• SOM: $12B (AI productivity and memory solutions)

Revenue Model

graph TD
    Personal["🆓 Mynd Personal<br/>FREE Forever<br/>• 30-day memory<br/>• 3 data sources<br/>• Community support"] 
    
    Pro["💎 Mynd Pro<br/>$29/month<br/>• Unlimited memory<br/>• All data sources<br/>• Priority MCP access<br/>• Advanced privacy controls"]
    
    Enterprise["🏢 Mynd Enterprise<br/>$199/user/month<br/>• Team memory sharing<br/>• Compliance controls<br/>• Custom integrations<br/>• White-label deployment"]
    
    Personal --> Pro
    Pro --> Enterprise
    
    style Personal fill:#4ecdc4
    style Pro fill:#f39c12
    style Enterprise fill:#e74c3c

Security & Privacy Architecture

Privacy-First Data Flow

graph TB
    subgraph "Your Device (Secure Zone)"
        Raw["📝 Raw Data<br/>(Browser, Files, Code)"]
        PII["🔒 PII Detection<br/>(Remove Sensitive Info)"]
        LLM["🧠 Local LLM<br/>(Semantic Extraction)"]
        Encrypt["🔐 Encrypted Storage<br/>(SQLite + ChromaDB)"]
    end
    
    subgraph "External AI (Untrusted)"
        ChatGPT["💬 ChatGPT"]
        Claude["🤖 Claude"] 
        Other["🤖 Other AIs"]
    end
    
    Raw --> PII
    PII --> LLM
    LLM --> Encrypt
    
    Encrypt -->|"Semantic Context Only<br/>(No Raw Data)"| ChatGPT
    Encrypt -->|"Semantic Context Only<br/>(No Raw Data)"| Claude
    Encrypt -->|"Semantic Context Only<br/>(No Raw Data)"| Other
    
    style Raw fill:#ff6b6b,stroke:#333,stroke-width:2px
    style PII fill:#4ecdc4,stroke:#333,stroke-width:2px
    style LLM fill:#f39c12,stroke:#333,stroke-width:2px
    style Encrypt fill:#27ae60,stroke:#333,stroke-width:2px

Privacy Promise: Your raw data NEVER leaves your device. Only semantic meaning is processed, stored locally, and delivered via encrypted MCP.

Success Metrics & Validation

Technical Milestones ✅

• [x] Core semantic extraction engine (Local LLM + privacy filters)
• [x] Local encrypted storage (ChromaDB + SQLite)
• [x] MCP server architecture with capability tokens
• [x] Browser history and document capture framework
• [x] CLI interface with full functionality

Demo Readiness ✅

• [x] 2-minute live demo script prepared
• [x] Real context database with semantic events
• [x] Multiple query examples working
• [x] Clear before/after comparison ready

Join the Memory Revolution

Mynd isn’t just a hackathon project - it’s the future of AI interaction. We’re building the memory layer that every AI desperately needs.

For Developers: Finally, coding AI that knows your entire project history
For Knowledge Workers: AI assistants that remember every decision and context
For Everyone: The end of explaining the same thing to AI over and over