YSenseAI™ v4.5-Beta: Ethical AI Training Data Platform

Build your personal wisdom library while contributing to ethical AI development.

YSenseAI is an open-source platform that enables individuals to share their cultural wisdom, personal reflections, and lived experiences in a way that respects their sovereignty, provides fair compensation, and protects cultural heritage. Built on the Z-Protocol v2.0 framework, it ensures transparent attribution, granular consent management, and ethical AI training data practices.

🌟 Overview

The Problem

AI models are trained on vast amounts of data scraped from the internet, often without:

Consent from content creators
Attribution to original sources
Compensation for contributors
Cultural sensitivity for traditional knowledge
Quality metrics for training optimization

This creates ethical, legal, and technical challenges for AI development.

The Solution

YSenseAI provides a consent-first, attribution-native platform where:

Users control their data through granular consent management (Z-Protocol v2.0)
Creators are compensated with 15-30% revenue sharing based on content tier
Cultural heritage is protected with community attribution and benefit sharing
Quality is optimized with 6 training metrics for AI model performance
Attribution is cryptographic with SHA-256 fingerprinting and DID assignment

Core Innovation

Story-First AI-Native UX: Users write freely, AI extracts structured wisdom layers, then collaborates with users to distill essence through dialogue. This creates:

Rich training data (5 perception layers + 3-word distillation)
User self-discovery (reflective conversation with AI)
Personal knowledge library (exportable in JSON/MD/CSV)
Training-optimized format (Alpaca, ShareGPT, JSONL)

✨ Key Features

For Users

📝 Story Canvas: Write freely about meaningful moments
🧠 AI Analysis: Automatic extraction of 5 perception layers (Narrative, Somatic, Attention, Synesthetic, Temporal)
💬 Collaborative Distillation: Chat with AI to refine 3-word essence
🛡️ Z-Protocol Consent: Choose content tier and AI training consent
📚 Personal Library: Search, view, and manage all submissions
📊 Quality Metrics: See training optimization scores
💰 Revenue Tracking: Estimate earnings from AI training
📥 Export Features: Download in JSON (for personal LLM), Markdown, CSV

For Developers

🔒 Attribution Engine: Cryptographic signing with SHA-256 + DID
📈 Quality Metrics: 6 training optimization signals
- Context efficiency (300-800 token sweet spot)
- Reasoning depth (5-layer CoT)
- Cultural specificity (>70% unique markers)
- Emotional richness (>15 descriptors/100 words)
- Attention density (3+ details/layer)
- Compression quality (>80% essence preserved)
🤝 Consent Management: 8 consent types with audit trail
💾 Database Schema: SQLite with GDPR-compliant deletion (72 hours)
🔌 AI Integration: Claude (Anthropic) + Qwen (Alibaba Cloud)
📦 Export Pipeline: JSONL, Alpaca, ShareGPT, CSV, Dataset Card

For AI Labs

✅ Legally-safe data: All content has explicit AI training consent
✅ Attributed data: Cryptographic provenance for every submission
✅ High-quality data: Training-optimized with 6 quality metrics
✅ Culturally-rich data: 5-layer perception analysis
✅ Ethically-sourced data: Z-Protocol v2.0 compliant
✅ Training-ready format: Alpaca, ShareGPT, JSONL

🏗️ Architecture

System Components

┌─────────────────────────────────────────────────────────────┐
│                      YSenseAI Platform                       │
├─────────────────────────────────────────────────────────────┤
│                                                               │
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐  │
│  │   Frontend   │    │   Backend    │    │   Database   │  │
│  │  (Streamlit) │───▶│   (Python)   │───▶│   (SQLite)   │  │
│  └──────────────┘    └──────────────┘    └──────────────┘  │
│         │                    │                    │          │
│         │                    │                    │          │
│         ▼                    ▼                    ▼          │
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐  │
│  │ Story Canvas │    │ AI Agents    │    │ Consent Mgmt │  │
│  │ 5-Layer UI   │    │ Claude+Qwen  │    │ Z-Protocol   │  │
│  │ Collab Chat  │    │ Distillation │    │ 8 Types      │  │
│  └──────────────┘    └──────────────┘    └──────────────┘  │
│         │                    │                    │          │
│         │                    │                    │          │
│         ▼                    ▼                    ▼          │
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐  │
│  │ Personal Lib │    │ Attribution  │    │ Quality      │  │
│  │ Search+Export│    │ SHA-256+DID  │    │ 6 Metrics    │  │
│  │ JSON/MD/CSV  │    │ Crypto Sign  │    │ Training Opt │  │
│  └──────────────┘    └──────────────┘    └──────────────┘  │
│                                                               │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
                    ┌──────────────────┐
                    │  Export Pipeline  │
                    │  JSONL, Alpaca,   │
                    │  ShareGPT, CSV    │
                    └──────────────────┘

Technology Stack

Frontend:

Streamlit 1.28+ (Python web framework)
Custom CSS for sanctuary aesthetic
Responsive design (mobile-friendly)

Backend:

Python 3.11+
SQLite (production-ready with WAL mode)
Anthropic Claude API (AI analysis)
Alibaba Cloud Qwen API (quality metrics)

Attribution:

SHA-256 cryptographic hashing
DID (Decentralized Identifier) system
JSON-LD for metadata
Blockchain-ready architecture

Consent:

Z-Protocol v2.0 framework
5-tier classification system
8 consent types
GDPR-compliant (72-hour deletion)

🛡️ Z-Protocol v2.0

YSenseAI is built on the Z-Protocol v2.0, a comprehensive ethical framework for AI attribution and cultural protection.

Five-Tier Classification

Tier	Description	Revenue Share	Protection Level
PUBLIC	General reflections, non-sensitive	15%	Standard
PERSONAL	Individual experiences, family	20%	Enhanced
CULTURAL	Traditional knowledge, practices	25% + fund	Community attribution
SACRED	Religious, spiritual content	30% + fund	Restricted access
THERAPEUTIC	Mental health, trauma	25% + fund	Medical ethics

Eight Consent Types

Account Creation (Required)
Wisdom Storage (Required for submissions)
AI Training Contribution (Optional, Compensated 15-30%)
Community Sharing (Optional)
Cultural Knowledge Sharing (Optional, for Cultural/Sacred tiers)
Revenue Sharing (Automatic with AI training consent)
Research Participation (Optional, Anonymized)
Anonymized Analytics (Optional)

Key Principles

Consent-First: No data use without explicit consent
Transparent Attribution: Cryptographic provenance for all content
Fair Compensation: 15-30% revenue sharing based on tier
Cultural Protection: Community approval for Cultural/Sacred content
User Sovereignty: Full control over data (export, edit, delete)
GDPR Compliant: All user rights implemented

Learn More: Z-Protocol v2.0 Complete Framework

🚀 Installation

Prerequisites

Python 3.11 or higher
pip (Python package manager)
Git
(Optional) Virtual environment tool (venv, conda)

Quick Install

# Clone the repository
git clone https://github.com/creator35lwb-web/YSense-AI-Attribution-Infrastructure.git
cd YSense-AI-Attribution-Infrastructure

# Create virtual environment
python3.11 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements_production.txt

# Set up environment variables
cp .env.example .env
# Edit .env with your API keys (see Configuration section)

# Initialize database
python -c "from v45_beta.database.schema import Database; Database()"

# Run the application
streamlit run v45_beta/app_legal_protected.py

Docker Install

# Build Docker image
docker build -t ysenseai:v4.5-beta .

# Run container
docker run -p 8501:8501 \
  -e ANTHROPIC_API_KEY=your_key \
  -e QWEN_API_KEY=your_key \
  ysenseai:v4.5-beta

Configuration

Create a .env file with your API keys:

# Anthropic Claude API (required)
ANTHROPIC_API_KEY=sk-ant-api03-...

# Alibaba Cloud Qwen API (required)
QWEN_API_KEY=sk-...
QWEN_BASE_URL=https://dashscope-intl.aliyuncs.com/compatible-mode/v1

# Database (optional, defaults to SQLite)
DATABASE_PATH=./ysense_production.db

# Security (optional, auto-generated if not set)
JWT_SECRET=your_secret_key_here

Get API Keys:

Claude: https://console.anthropic.com/
Qwen: https://www.alibabacloud.com/help/en/model-studio/get-api-key

🎯 Quick Start

1. Register an Account

# Start the application
streamlit run v45_beta/app_legal_protected.py

Navigate to http://localhost:8501 and:

Click "Register" tab
Read and accept Privacy Policy and Terms of Service
Acknowledge beta status
Create your account

2. Submit Your First Wisdom

Click "✨ Submit Wisdom" in sidebar
Write a story about a meaningful moment
Click "Analyze Story" - AI extracts 5 perception layers
Chat with AI to refine the 3-word distillation
Choose Z-Protocol tier (Public/Personal/Cultural/Sacred/Therapeutic)
Opt-in to AI training (optional, 15-30% revenue share)
Click "💾 Save Wisdom"

3. View Your Library

Click "📚 My Library" in sidebar
Search your submissions
View quality scores
Export individual wisdom (JSON/MD)

4. Export Your Data

Go to "⚙️ Settings"
Click "Export All Data"
Choose format (JSON for personal LLM, Markdown for notes, CSV for analysis)
Download your complete wisdom library

📖 Documentation

For Users

User Guide - Complete walkthrough
Privacy Policy - GDPR-compliant privacy notice
Terms of Service - Beta program terms
FAQ - Frequently asked questions

For Developers

Architecture - System design and components
API Reference - Internal API documentation
Database Schema - SQLite schema and relationships
Contributing Guide - How to contribute
Code of Conduct - Community guidelines

For Researchers

Z-Protocol v2.0 Specification - Complete framework
Quality Metrics - Training optimization signals
Attribution Engine - Cryptographic provenance
Export Formats - JSONL, Alpaca, ShareGPT

💡 Use Cases

1. Personal Knowledge Management

Build your own wisdom library and export for personal LLM training:

# Export your wisdom for personal LLM
import json

with open('my_wisdom.json', 'r') as f:
    wisdom = json.load(f)

# Use with Llama, Mistral, or any open-source model
# Fine-tune on your personal reflections

2. Cultural Heritage Preservation

Share traditional knowledge with community attribution:

Submit cultural stories with CULTURAL tier
Community approval required before AI training
Benefit sharing with cultural communities (25% + fund)
Anti-appropriation protections

3. Mental Health Research

Contribute anonymized therapeutic content:

Submit mental health journeys with THERAPEUTIC tier
Medical ethics oversight
Research fund contribution (25% + fund)
Professional validation required

4. AI Training Dataset Creation

Export training-ready datasets for AI labs:

# Export all training-ready submissions
python v45_beta/exports/export_pipeline.py \
  --format alpaca \
  --min-quality 0.7 \
  --output datasets/ysense_training_data.jsonl

5. Academic Research

Study ethical AI data practices:

Analyze consent patterns
Study quality metrics
Research attribution systems
Investigate revenue sharing models

🗺️ Roadmap

v4.5-Beta (November 2025)

Story-first AI-native UX
Collaborative 3-word distillation
Z-Protocol v2.0 integration
GDPR-compliant privacy
Attribution engine (SHA-256 + DID)
Quality metrics (6 signals)
Export pipeline (JSONL, Alpaca, ShareGPT)
HuggingFace Wisdom Canvas demo deployed

VerifiMind PEAS v0.5.0 Foundation (Current - March 2026)

The validation backbone for YSenseAI — VerifiMind PEAS reached its Foundation milestone:

SessionContext tracing for per-run debugging
Error handling v2 with structured responses
Health endpoint v2 with richer diagnostics
BYOK hardening (retry logic, graceful degradation)
205 automated tests, 55.1% coverage
Z-Protocol Approved (9.2/10)
Fully self-hosted on GCP Cloud Run
Landing page live at verifimind.io

v5.0 (Q2-Q3 2026)

Blockchain integration (Ethereum/Polygon)
Smart contracts for revenue distribution
IPFS/Arweave storage
Mobile app (iOS/Android)
Multi-language support (10+ languages)
Community features (sharing, comments)

v6.0 (Q4 2026)

VerifiMind-PEAS API (attribution as a service)
Platform partnerships (integrate with other platforms)
AI lab marketplace (sell datasets directly)
Advanced analytics dashboard
Automated quality validation
Cultural community governance

Long-Term Vision

Industry Standard: Z-Protocol adopted by 100+ platforms
Ecosystem: 10,000+ users contributing ethical AI training data
Revenue: $1M+ distributed to content creators
Impact: Change how AI models are trained globally

🤝 Contributing

We welcome contributions from developers, researchers, and community members!

Ways to Contribute

Code: Fix bugs, add features, improve performance
Documentation: Write guides, tutorials, translations
Research: Study ethical AI practices, publish findings
Community: Help users, answer questions, organize events
Feedback: Report bugs, suggest features, share use cases

Getting Started

Read CONTRIBUTING.md
Check open issues3. Join our Discord (coming soon)
Fork the repository and submit a pull request

Development Setup

# Clone your fork
git clone https://github.com/creator35lwb-web/YSense-AI-Attribution-Infrastructure.git
cd YSense-AI-Attribution-Infrastructure

# Create feature branch
git checkout -b feature/your-feature-name

# Install dev dependencies
pip install -r requirements_dev.txt

# Run tests
pytest tests/

# Submit pull request
git push origin feature/your-feature-name

📝 Citation

If you use YSenseAI in your research, please cite:

@software{ysenseai_v45_beta,
  author = {Alton (creator35lwb)},
  title = {YSenseAI: Ethical AI Training Data Platform},
  version = {4.5-beta},
  year = {2025},
  month = {11},
  url = {https://github.com/creator35lwb-web/YSense-AI-Attribution-Infrastructure},
  doi = {10.5281/zenodo.17737995}
}

@misc{zprotocol_v2,
  author = {Alton (creator35lwb)},
  title = {Z-Protocol v2.0: Ethical Framework for AI Attribution and Cultural Protection},
  year = {2025},
  month = {11},
  url = {https://doi.org/10.5281/zenodo.17072168},
  doi = {10.5281/zenodo.17072168}
}

See CITATION.cff for machine-readable citation metadata.

📄 License

Code License

YSenseAI platform code is licensed under the MIT License.

MIT License

Copyright (c) 2025 Alton (creator35lwb)

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

[Full license text in LICENSE file]

Z-Protocol License

Z-Protocol v2.0 framework is licensed under Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

This means:

✅ You can use Z-Protocol in your projects
✅ You can modify and adapt it
✅ You must give appropriate credit
✅ You must share adaptations under the same license

See LICENSE-ZPROTOCOL for details.

Third-Party Licenses

Streamlit: Apache 2.0
Anthropic Claude: Commercial API (requires key)
Alibaba Cloud Qwen: Commercial API (requires key)

See THIRD_PARTY_LICENSES.md for complete list.

🙏 Acknowledgments

Core Team

Founder & Human Orchestrator: Alton (creator35lwb)
Z-Protocol Design: Community-driven ethical framework
Platform Development: Open-source contributors

FLYWHEEL TEAM (Multi-Agent Development)

CSO R (Manus AI): Strategy, public materials, landing page
CTO RNA (Claude Code): Implementation, testing, deployment
COO AY (Antigravity): Metrics validation, operational reports
L (Human Orchestrator): Direction, synthesis, final decisions

AI Partners

Anthropic: Claude API for AI analysis and distillation
Alibaba Cloud: Qwen API for quality metrics

Community

Beta testers who provided invaluable feedback
Open-source contributors who improved the platform
Cultural communities who shaped the Z-Protocol

Inspiration

Creative Commons: For showing how open licensing can change the world
GDPR: For setting the standard for user privacy rights
Indigenous Data Sovereignty: For teaching us about cultural protection

📞 Contact

Website: https://ysenseai.org
VerifiMind PEAS: https://verifimind.io
HuggingFace: https://huggingface.co/spaces/YSenseAI/wisdom-canvas
Email: alton@ysenseai.org or creator35lwb@gmail.com
Privacy: alton@ysenseai.org or creator35lwb@gmail.com
Legal: alton@ysenseai.org or creator35lwb@gmail.com
GitHub: https://github.com/creator35lwb-web/YSense-AI-Attribution-Infrastructure
Discord: Not yet available (planned for v5.0)
Twitter: https://x.com/creator35lwb

⭐ Star History

If you find YSenseAI useful, please star the repository to show your support!

Built with ❤️ for ethical AI development

Name		Name	Last commit message	Last commit date
Latest commit History 42 Commits
api/specifications		api/specifications
backend		backend
docs		docs
prototype		prototype
v45_beta		v45_beta
website		website
wisdom-drops/2025/september/WD-001-echo-black-box		wisdom-drops/2025/september/WD-001-echo-black-box
.env.example		.env.example
.gitignore		.gitignore
.zenodo.json		.zenodo.json
CHANGELOG.md		CHANGELOG.md
CITATION.cff		CITATION.cff
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
LICENSE-ZPROTOCOL		LICENSE-ZPROTOCOL
LICENSE.txt		LICENSE.txt
PATENT_NOTICE.md		PATENT_NOTICE.md
PUBLICATION_GUIDE.md		PUBLICATION_GUIDE.md
README.md		README.md
RELEASE_NOTES_v0.3.0.md		RELEASE_NOTES_v0.3.0.md
RELEASE_NOTES_v4.5-beta.md		RELEASE_NOTES_v4.5-beta.md
SOCIAL_MEDIA_ANNOUNCEMENTS.md		SOCIAL_MEDIA_ANNOUNCEMENTS.md
attribution_engine.py		attribution_engine.py

Folders and files

Latest commit

History

Repository files navigation

YSenseAI™ v4.5-Beta: Ethical AI Training Data Platform

📚 Table of Contents

🌟 Overview

The Problem

The Solution

Core Innovation

✨ Key Features

For Users

For Developers

For AI Labs

🏗️ Architecture

System Components

Technology Stack

🛡️ Z-Protocol v2.0

Five-Tier Classification

Eight Consent Types

Key Principles

🚀 Installation

Prerequisites

Quick Install

Docker Install

Configuration

🎯 Quick Start

1. Register an Account

2. Submit Your First Wisdom

3. View Your Library

4. Export Your Data

📖 Documentation

For Users

For Developers

For Researchers

💡 Use Cases

1. Personal Knowledge Management

2. Cultural Heritage Preservation

3. Mental Health Research

4. AI Training Dataset Creation

5. Academic Research

🗺️ Roadmap

v4.5-Beta (November 2025)

VerifiMind PEAS v0.5.0 Foundation (Current - March 2026)

v5.0 (Q2-Q3 2026)

v6.0 (Q4 2026)

Long-Term Vision

🤝 Contributing

Ways to Contribute

Getting Started

Development Setup

📝 Citation

📄 License

Code License

Z-Protocol License

Third-Party Licenses

🙏 Acknowledgments

Core Team

FLYWHEEL TEAM (Multi-Agent Development)

AI Partners

Community

Inspiration

📞 Contact

⭐ Star History

About

Resources

License

Licenses found

Code of conduct

Contributing

Uh oh!

Stars

Watchers

Forks

Releases 4

Packages 0

Uh oh!

Contributors

Uh oh!

Languages

Packages