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Overview

The Subnet 42 scoring system evaluates miner performance by analyzing telemetry data collected from their TEE (Trusted Execution Environment) workers. This scoring mechanism is designed to reward miners that successfully process X/Twitter data collection tasks and web scraping, while penalizing those with errors or failed operations.

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How telemetry data is collected and processed

Telemetry Data Sources: Each registered TEE worker periodically reports telemetry data that includes:

X/Twitter Analytics

  • Tweet collection statistics
  • Profile data retrieval metrics
  • API usage and rate limit tracking

Web Scraping Metrics

  • Success and failure counts for web scraping operations
  • Performance tracking across different target sites

Error Monitoring

  • Authentication failures
  • Rate limit exceeded events
  • Network and connectivity issues
  • Other operational errors

Timing Data

  • Operation start/end timestamps
  • Processing duration metrics
  • Interval between data collections

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Delta-based Performance Tracking

Snapshot Analysis

  • System stores multiple telemetry snapshots
  • Scores calculated from oldest to newest changes
  • Recent activity and improvements rewarded

TEE Restart Handling

  • Detects negative delta values
  • Resets telemetry for fresh start
  • Ensures non-negative scoring deltas

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Scoring Algorithm

1

Telemetry Data Collection

For each node, the system:
  • Retrieves telemetry snapshots
  • Calculates deltas between records
  • Handles restart scenarios
  • Normalizes values for scoring
2

Key Performance Metrics

The scoring system evaluates:
  • Web Success: Successful web scraping operations
  • Tweet Collection: Successfully retrieved tweets
  • Profile Data: Successfully retrieved Twitter profiles
3

Kurtosis Weighting

A custom kurtosis function weights top performers more heavily in the final scoring calculations.
def apply_kurtosis_custom(
  x,
  top_percentile=90,
  reward_factor=0.4,
  steepness=2.0,
  center_sensitivity=0.5,
  boost_factor=0.2
):

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Function Details

Weighting Logic

  • Applies higher weights to top 90th percentile nodes
  • Uses configurable curve parameters
  • Balances rewards for adequate performance

Metric Normalization

  • Scales values to 0-1 range
  • Provides minimal non-zero scores
  • Handles statistical outliers
1

Score Combination

The final score incorporates:
  • Web scraping success rate
  • Tweet collection metrics
  • Profile retrieval performance
Nodes with balanced performance across metrics receive higher scores, while uneven performance results in moderate scoring.
2

Validation Process

The system performs these checks:
  • Minimal scores for low-activity nodes
  • Score normalization across all nodes
  • Zero scores for invalid/disconnected nodes
3

Weight Application

The validated scores are then:
  • Converted to blockchain weights
  • Used to determine TAO rewards
  • Applied through the Bittensor network

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Weight Conversion & Updates

Weight Calculation

  • Scores converted to blockchain weights
  • TAO rewards based on final weights
  • Regular update intervals

Update Process

  • Minimum interval between updates
  • Up to 3 retry attempts
  • Score reports sent to miners

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Performance Optimization Guide

1

Maintain High Uptime

Keep your TEE worker running continuously to avoid service interruptions and restarts
2

Reduce Errors

Minimize authentication failures, rate limits, and other operational errors
3

Optimize Success Rates

Focus on achieving consistent success with X and web scraping operations
4

Balance Metrics

Aim for strong performance across all metrics rather than excelling in just one area
5

Monitor Performance

Regularly check telemetry data to identify and address potential issues early

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Technical Architecture

Core Components

  • WeightsManager: Handles weight calculations
  • NodeManager: Manages miner connections
  • TelemetryStorage: Handles data persistence
  • ScoringFunctions: Implements scoring logic

Weight Calculation

The calculate_weights method in WeightsManager:
  • Processes telemetry data
  • Normalizes metrics
  • Applies kurtosis weighting
  • Generates final scores

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Scoring Process

1

Process Telemetry Data

Analyzes changes in miner performance metrics over time using delta-based calculations
2

Extract & Normalize

Standardizes raw metrics into comparable values across different data types
3

Apply Weighting

Uses kurtosis weighting to balance consistency with peak performance
4

Calculate Scores

Combines weighted metrics into comprehensive performance scores
5

Generate Weights

Converts final scores into network weight allocations for rewards

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Conclusion

The Subnet 42 scoring system implements a fair and transparent approach to miner rewards. By combining:
  • Delta-based performance tracking
  • Normalized metric analysis
  • Kurtosis-weighted scoring
The system creates balanced incentives that encourage both consistent reliability and performance excellence in web and social data collection.