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.
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.
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- 🎭 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
- Ask: "What was our authentication decision?"
- Toggle memory OFF and ask again
- Watch the AI go from "I don't have context" to perfect recall!
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
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
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"
# 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"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!- ✅ 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
- ✅ Revolutionizes AI interaction: No more context re-explanation
- ✅ Seamless collaboration: AI knows your full background
- ✅ Natural communication: AI understands your references and history
- TAM: $450B (Global productivity software market)
- SAM: $67B (AI tools and services)
- SOM: $12B (AI productivity and memory solutions)
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
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.
- Core semantic extraction engine (Local LLM + privacy filters)
- Local encrypted storage (ChromaDB + SQLite)
- MCP server architecture with capability tokens
- Browser history and document capture framework
- CLI interface with full functionality
- 2-minute live demo script prepared
- Real context database with semantic events
- Multiple query examples working
- Clear before/after comparison ready
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