PiRC-AI: Attention-Based Token Economy with AI Verification and End-to-End Implementation#99
PiRC-AI: Attention-Based Token Economy with AI Verification and End-to-End Implementation#99Clawue884 wants to merge 62 commits intoPiNetwork:mainfrom
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Added project overview and core innovations for PiRC-AI.
Added architecture overview for PiRC-AI attention economy model.
Added sections on Token Model, Simulation, and Risks & Challenges.
Introduced the Attention Triad framework detailing its components: Contribution, Verification, and Monetization. This framework aims to address value extraction imbalances in digital platforms by ensuring fair rewards for user attention.
Implement core reward calculation and normalization methods.
Implemented a model training script that generates a dataset and trains a Random Forest classifier for AI verification.
Implement a simulation that generates user data and calculates rewards based on predictions.
Added a relayer script to process user data and mint rewards based on verification scores.
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What do you think about adding price credibility through governance? For example: 100 retailers are offering a Samsung phone for $1000. If one retailer tries to exploit the situation by raising the price to $1005 or $1050, or even higher than the price in the entire ecosystem, the price is lowered by AI robots until it returns to a normal price. This ends the exploitation and monopoly by greedy retailers, providing security for consumers and protecting retailers from price manipulation that would deliberately drive down the product's price. What are your thoughts on this? |
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This is a very interesting idea, especially in the context of protecting users from price manipulation. However, I think it's important to carefully design how AI interacts with pricing to avoid over-centralization or unintended market distortion. Instead of directly forcing price corrections, a more robust approach could be:
This way, we preserve market freedom while still protecting against manipulation. This could actually integrate very well with the PiRC-AI model, especially within the "Attention Verification" and "Monetization" layers. Great idea — it just needs to be implemented as a guidance system rather than a control system. |
Implement RPC call functionality to interact with the API.
Set up Express server with health and latest ledger endpoints.
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Great work — the architecture and Price Credibility Oracle design is promising. |
🥧 PiRC Sovereign Monetary System (Standards 101-260)Welcome to the official repository of the PiRC Sovereign Monetary System. This repository houses the complete, fully operational codebase for a sophisticated 7-Layer Real World Asset (RWA) and digital economy ecosystem, deployed on the Pi Network and Stellar Testnets. 👤 Lead Architects
🏗️ System Architecture: The 7-Layer MatrixThis ecosystem is built upon a modular 7-layer colored token architecture. Over 50 distinct smart contracts have been deployed, synchronized, and bound together to operate with absolute 1:1 state parity.
⚙️ Core Technical CapabilitiesOur infrastructure extends far beyond standard token creation. It represents a living, breathing digital economy:
🌐 Live Network VerificationThis repository is not theoretical. The smart contracts are actively running and generating blocks. Developers and community members can track the live interactions of the 7-Layer Matrix using standard block explorers:
(Note: Live transaction hashes and contract IDs are dynamically tracked in our 🚀 Getting Started for DevelopersTo interact with the PiRC matrix or build dApps on top of our 7-layer standard:
⚖️ License & Open SourceThis infrastructure is built to empower the next generation of sovereign digital economies. Licensed under the MIT License. Architected and synchronized by Ze0ro99 & Clawue884. ⚡🥧 |










Overview
This pull request introduces PiRC-AI, an extended implementation of the Pi Request for Comment (PiRC), proposing an attention-based token economic model designed for the AI era.
As automation reduces the role of traditional labor, this proposal explores a new paradigm where verified human attention becomes a core economic resource.
Key Contributions
1. Attention-Centered Token Model
Introduces a reward mechanism based on:
R = A × Q × V
Where:
2. Attention Triad Framework
Defines three distinct layers:
This highlights the contribution gap in current digital platforms.
3. AI-Based Verification Layer
Implements a machine learning model to:
4. End-to-End Prototype Implementation
Includes a working system:
User → Dashboard → AI Oracle → Reward Engine → Token Mint → UI Update
Components:
5. Simulation & Tokenomics Validation
Provides tools to evaluate:
Purpose
This PR is intended as:
Disclaimer
This is an independent contribution and not affiliated with the Pi Core Team.
Discussion
Feedback is highly appreciated, especially on: