README - skenai/WILL GitHub Wiki

version: 2.1.0 date: 2025-03-15 type: research-doc status: public tags: [william, research, theoretical, validation, readme] related: [Research-Disclaimer, WILLPOWER-Interface] changelog:

  • version: 2.1.0 date: 2025-03-15 changes:
    • "MAJOR: Enhanced research clarity"
    • "MAJOR: Strengthened theoretical foundation"
    • "MAJOR: Added research validation requirements" references:
    • "Research-Disclaimer"
  • version: 2.0.0 date: 2025-03-04 changes:
    • "MAJOR: Switch to YAML frontmatter"
    • "MAJOR: Enhanced metadata structure" references: []
  • version: 1.0.0 date: 2025-03-03 changes:
    • "MAJOR: Initial documentation" references: []

WILL - Web3 Intelligence & Learning Layer Research Project

IMPORTANT RESEARCH NOTICE: WILL (Web3 Intelligence & Learning Layer) represents a theoretical research project under active development. All features, metrics, and capabilities discussed in this documentation are research objectives that require extensive testing and validation. All intelligence systems, learning mechanisms, and integration methods are proposed models pending practical implementation.

Research Overview

WILL investigates theoretical approaches for facilitating meaningful interactions and knowledge sharing in the Web3 ecosystem through experimental AI systems. As a core research component of the SKENAI DAO, WILL represents our investigation into next-generation decentralized intelligence.

Research Vision

WILL's research aims to explore theoretical bridges between traditional AI systems and decentralized governance, investigating approaches for creating a more intelligent, transparent, and user-centric Web3 ecosystem. All proposed solutions require thorough validation.

Research Roadmap

Our research roadmap is divided into several experimental phases:

Phase 1: Foundation Research (Q1 2025)

  • Social intelligence research
  • DAO interaction studies
  • API infrastructure experiments
  • Implementation validation

Phase 2: Enhancement Research (Q2 2025)

  • Learning mechanism studies
  • Cross-chain research
  • Governance feature analysis
  • Implementation validation

Phase 3: Scaling Research (Q3-Q4 2025)

  • Community development studies
  • Security research framework
  • Integration experiments
  • Implementation validation

Research Documentation

  • /docs - Research documentation

    • Architecture research
    • Integration studies
    • API experiments

  • /examples - Research implementations

  • /interfaces - Experimental APIs

  • /community - Research guidelines

Technical Research Overview

WILL investigates a modular research architecture that explores:

  • Web3 integration studies
  • AI processing research
  • Security validation methods
  • Community research models

Research Contributions

We welcome research contributions from the community! Please see our Research Contributing Guidelines for more information.

Research License

This research project is licensed under the MIT License - see the LICENSE file for details.

Related Research

  • SKENAI DAO - Governance research framework
  • Additional research integrations (Under development)

Contact Information

  • Research Team: [research]
  • Development: [dev]
  • Documentation: [docs]
  • Support: [support]

Note: This repository contains the research interface and documentation for WILL. All features require thorough validation before practical implementation.

A Note to Our Family

While maintaining our rigorous research foundation, we recognize that William's strength comes from bringing people together. As a family-focused business, we:

  • Value research integrity
  • Share verified insights
  • Support each other's growth
  • Build trust through honesty
  • Win through excellence

Remember: While we operate as a family business, our foundation is built on rigorous research and validation. Every feature and capability represents ongoing research that requires thorough testing before practical implementation.

SPAN-VERGE Integration

As of Version 3.0.0 (Genesis Epoch), this component is fully integrated with the SPAN-VERGE epochal transition system:

  • Epochal Transitions: Supports automated state transitions via VERGE
  • Multi-Agent Collaboration: Integrates with ARCHIE, HORATIO, CHANDLER, WILL
  • SPAN Addressing: Full SPAN addressing support for resource identification
  • Historical Accuracy: Automatically maintained through WILL learning environment

SPAN Address: span://v1/skenai-main/will/wiki/README

Last updated: 2025-07-25 (SPAN-VERGE Era)