Terminology
Essential Terms for the CLONES Ecosystem
Understanding the CLONES ecosystem requires familiarity with key terminology. This comprehensive guide defines all important concepts, roles, and mechanisms within the platform.
🤖 Core Technology Concepts
CLONES The platform enabling anyone to create, own, and trade training data for Computer Use Agents
Computer Use Agents (CUAs) AI systems that don't just think and write, but actually control your computer to complete tasks. They click, type, navigate, and execute workflows exactly like humans would.
Demonstration A screen recording of a human performing a computer task, capturing all interactions (clicks, keystrokes, navigation) to serve as AI training data.
Dataset A collection of related demonstrations compiled, processed, and validated for training Computer Use Agents on specific skills or workflows.
🏗️ Platform Architecture
The Forge The data generation engine where humans record demonstrations and create AI training datasets.
Data Marketplace On-chain exchange where dataset tokens can be discovered & traded with threshold-gated IP access.
Meta-Datasets Premium curated bundles combining the best demonstrations across multiple datasets creating enterprise-grade training packages.
👥 Ecosystem Participants
Factory Creator Individual or business who funds data collection by creating Factories and defining specific skills to capture as training datasets.
Revenue Sources => Dataset token launches, volume fees (0.25%), threshold-gated IP control, meta-dataset allocations
Farmer Contributors who record screen demonstrations while performing tasks, earning crypto rewards based on quality and utility.
Ambassador Community members who onboard new participants, earning referral commissions (1-5%) on all referred activity through the on-chain referral program.
🏭 Key Platform Components
Factory A funded workspace where creators define skills and fund reward pools to incentivize farmers to record specific demonstrations.
Factory Pool The reward mechanism funded with $CLONES/$USDC/$ETH tokens, distributed to farmers based on demonstration quality.
The Playground Premium section featuring high-value campaigns funded by CLONES Project and strategic partners with exclusive access requirements.
📊 Quality & Validation
Quality Score AI-powered assessment (0-100%) of each demonstration's clarity, accuracy, and training value.
Quality Threshold Minimum score (50%+) required for payment, ensuring only valuable data contributes to AI training.
CLONES Quality Agent Automated system evaluating demonstrations across execution quality, training utility, and workflow completeness.
🪙 Token Utility
$CLONES Token Native utility token powering the ecosystem—used for staking, governance, burns, and unlocking enhanced features.
Staking Tiers System unlocking enhanced features, governance voting weight, and priority access based on stake percentage (0.1%-0.5% supply).
Token Burns Deflationary mechanism permanently removing $CLONES through dataset launches, premium features, and spotlight positioning.
Fair Launch Public token distribution
🔄 Economic Mechanisms
Threshold-Gated IP Access Access control where holding above creator-set token amounts unlocks commercial IP rights with automatic on-chain enforcement.
Bonding Curve Launches Automated market maker system for dataset token launches providing immediate price discovery (like virtuals.io)
Decentralized Referral Program On-chain system where ambassadors earn commissions on all referred activity, creating exponential growth incentives.
Network Effects Each new participant makes the platform more valuable through increased data quality, dataset variety, and ecosystem growth.
🎯 Strategic Concepts
Data Moat Competitive advantage from the largest web3 repository of human computer interaction data.
Liquid Data Markets Tokenized markets for data-as-IP where participants trade stakes in datasets with threshold-gated commercial access.
🌐 Ecosystem Vision
AI Training Data Economy Economic model where human expertise becomes valuable digital assets through systematic capture and monetization.
Democratized Data Creation Making AI training data creation accessible to everyone, not just corporations with massive resources.
Human-Centric AI Training Training AI systems on real human workflows rather than synthetic approaches, ensuring practical applicability.
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