AIMN Dash-Flow Manifesto
AI Optimization Automations Integration to business and personal growth AIMN Tag_flow

AIMN is a Flow Concept for intelligent automation designed to integrate and process data from multiple sources, the goal is to create an AI assistant with real-time contextual awareness. The system is based on:

  • Modular Architecture:
    • Primary Prompt: Defines global variables and goals.
    • Specialized Nodes: Perform specific functions such as analysis and synthesis.
    • Adaptive Flow: Self-optimizes based on feedback.
  • Key Technologies:
    • RAG (Retrieval-Augmented Generation): For efficient information processing.
    • Contextual Memory: Maintains consistency of decisions over time.
    • Intelligent Tagging: Facilitates categorization and retrieval of data.
  • Core Capabilities:
    • Automation of workflows without human supervision.
    • Real-time analysis of data from multiple sources (RSS, email, file system).
    • Generation of reports, predictions, and actions based on current context.
  • Potential Applications:
    • Automated business information management.
    • Advanced personal assistance.
    • Optimization of decision-making processes.
  • Future Developments:
    • Integration with IoT systems for enhanced environmental awareness.
    • Improved autonomous learning capabilities.
    • Expansion of data sources for a more comprehensive understanding of context.

AIMN formalizes an ecosystem in which AI can operate first under supervision then autonomously, making informed decisions and providing contextual assistance without requiring constant human intervention.

AIMN's Flows and Actions are directed toward the ability to dynamically adapt to new contextual needs. Through continuous learning and self-optimization, the system constantly evolves, improving its effectiveness over time and offering increasingly “Aligned” and simplified solutions tailored to user needs.

All stages of Project Development are shared in real time on this site, explore the Dashboard all Assistants are at your disposal for a Functional Logic compression, if you are interested or have questions get in touch now.

 

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AIMN is redefining the interaction between artificial intelligence and human contexts. We seek visionary investors to accelerate the development and implementation of this revolutionary technology.
Investment Opportunities:
1. Direct Funding.
 - Support for the core development of AIMN
 - Priority access to new features and applications
2. Strategic Partnerships
 - Co-development of industry-specific applications
 - Preferential integration with existing systems
3. Sponsored Research Programs
 - Funding for targeted research projects
 - First-look rights to research results
Investor Benefits:
- Participation in potential future revenues
- Influence on development directions
- Early access to cutting-edge technologies
For discussions on investment opportunities, please contact us (insert contact)
We are also open to non-financial contributions from experts and developers. For technical collaborations, please visit our Dashboard.

Business, Management, System, Information, Assistant, AI-Flow, Awareness, Potential, Artificial intelligence, Intelligence, Dash-Flow Manifesto, Logic

Guide to the Project: AI Agency - “Awareness Engine” based on the D-ND Model

5 minutes

This document outlines the project of an AI agency with an internal "Awareness Engine" (AE) concept. The project is based on the logic of the D-ND (Dual - Non-Dual) Model, a theoretical and applicative framework that integrates principles of quantum physics, multivalued logic, and advanced AI to create artificial intelligence systems capable of augmented logical reasoning and adaptability to future scenarios.

The knots come to the boil: the limits of AI reasoning and the road to new logic models

3 minutes

Apple's recent research has exposed a crucial limitation of large language models (LLMs), raising important questions about how these models deal with problems. At the same time, these findings provide the impetus for imagining **new logic architectures** capable of overcoming the current limitations of artificial intelligence.

 

The resultant \( R \) of this analysis is clear: the schema-based approach is not enough. What is needed is a transition to new **logical models**, a movement away from the duality of right and wrong and toward **autological** reasoning.