Meta-Datasets
The Blue-Chip Tier of AI Training Data
Meta-datasets represent the protocol's premium offerings — carefully curated bundles of high-performing datasets within complementary domains, creating comprehensive training packages with private allocation access.
The Evolution: Transform individual datasets into enterprise-grade intelligence products while creating a premium tier above standard dataset tokens.
The Curation Standard
Protocol-Driven Excellence
Instead of random dataset combinations, meta-datasets are built from proven demonstration quality — only datasets containing the highest-graded demonstrations and most valuable training sequences make the cut.
Selection Criteria
Demonstration Excellence: Highest-quality demonstrations with superior AI training value
Domain Coherence: Related workflows from complementary specialized datasets
Training Utility: Only datasets with proven learning effectiveness and clear outcomes
Coverage Completeness: Comprehensive workflow coverage within each vertical
Example Meta-Dataset Themes
"Business Automation Elite" — Excel + Python + Email + Calendar workflows
"Creative AI Master" — Design + Video + AI Prompting + Marketing workflows
"Developer Intelligence" — Coding + Architecture + DevOps + Management workflows
"Financial Operations Suite" — Trading + Analysis + Compliance + Reporting workflows
Private Allocation Model
Blue-Chip Launch Mechanics
Meta-datasets skip public bonding curves entirely, operating as exclusive private allocations followed by direct Uniswap V3 deployment.
Private Contribution System (1B Token Supply)
Liquidity Pool — 50% Allocation (500M Tokens)
Purpose: Ensures trading functionality and burn-to-download access
Mechanism: Auto-seeded for immediate Uniswap V3 deployment
Result: Deep liquidity enabling threshold-based IP access
$CLONES Stakers — 25% Allocation (250M Tokens)
Access Method: Private contribution at fixed price during 48-hour window
Distribution: Among qualified stakers (typically 100+ participants)
Individual Allocation: ~2.5M tokens per staker (0.25% of supply)
Price Determination: Based on floor price of included datasets
Dataset Creators — 20% Allocation (200M Tokens)
Access Method: Direct airdrop (no payment required)
Distribution Formula: 20% × (Dataset Contribution % in Meta-Bundle)
Example: Excel (40% of meta) = 80M tokens, Python (35%) = 70M tokens
Burn-to-Download IP Holders — 5% Allocation (50M Tokens)
Access Method: Direct airdrop (no payment required)
Distribution: Proportional to historical burn-to-download amounts
Qualification: Must have burned threshold amount for IP access
Holder Cap System: All participants are capped at burn-to-download threshold amounts to prevent market manipulation and ensure sustainable tokenomics.
Dynamic Allocation: Unused allocations automatically flow to the liquidity pool
Launch Process & Mechanics
Phase 1: Curation & Announcement
Protocol Selection: Algorithm identifies high-performing dataset combinations
Community Validation: $CLONES stakers vote on meta-dataset themes
Allocation Calculation: Automatic distribution based on contribution formulas
48-Hour Notice: Exclusive launch window announced to qualified participants
Phase 2: Private Contribution Round
Staker Access: Qualified stakers can contribute within their allocation limits
Fixed Pricing: No bonding curve speculation, transparent price discovery
Automatic Airdrops: Creators and IP holders receive tokens instantly
Full Subscription: Round continues until all allocations are filled
Phase 3: Direct Uniswap V3 Deployment
Immediate Launch: Direct Uniswap V3 deployment with pre-seeded liquidity
Deep Liquidity: 500M+ tokens deployed to liquidity pool & available for threshold access
Exclusive Initial Supply: Only qualified ecosystem participants hold tokens at launch
Strategic Positioning
Tier Differentiation:
Individual Datasets: Public launches, broad access
Meta-Datasets: Private allocations, curated quality, premium positioning
Network Effects:
Quality Competition: Creators compete for meta-dataset inclusion
Ecosystem Elevation: Curation standards improve overall data quality
Value Concentration: Top performers capture additional value through bundling
Meta-datasets create a premium tier where proven excellence receives enhanced recognition and reward, while maintaining market functionality through balanced allocation and holder cap protections.
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