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Guided Project II: From Zero to Miner: Hands-On Mining on Bittensor Data Universe (SN13)

In this course, we’ll learn Bittensor’s Data Universe subnet (SN13) from scratch in a structured way: understand how Bittensor subnets work and how miners and validators split responsibilities, master SN13’s core data model (DataEntity, labels, time buckets) and incentive mechanics (scoring, credibility, deduplication). By the end, you’ll have deployed and registered a working SN13 miner, configured scraping for Reddit/X/YouTube, and optimized your earnings through data submission, runtime monitoring, and a full end-to-end project demo.

Language

Solidity

Total Length

3.0h

Part of Learning Track

Build on Bittensor + 1

What You’ll Learn

How Bittensor works and how its incentive mechanisms distribute rewards

The role ofSN13 (Data Universe)as Bittensor’sdecentralised data layer

The core SN13 loop:Miner fetches → local storage → S3 upload → Validator sampling & verification → on-chain settlement → TAO rewards

Key SN13 data concepts and the incentive/scoring logic behind rewards

A practical walkthrough fromcloning the repo to launching a miner

SN13

Miner

…

Syllabus

Unit 1 - Introduction to the SN13 Data Universe Subnet

In this unit, we’ll build a clear mental model of the Bittensor subnet ecosystem: what miners, validators, and users each do, why they can earn TAO, and how these roles work together specifically in SN13 (Data Universe).

Unit 2 - Environment Setup & Deployment

In this unit, we’ll build an SN13 (Data Universe) miner from scratch: set up the system environment and dependencies, install btcli and the required toolchain, and get everything ready for the next steps—wallet registration, pulling the codebase, configuring scraping, and launching your node.

Unit 3 - Miner Configuration & Data Scraping Strategy

In this unit, we’ll turn your SN13 (Data Universe) miner’s scraping approach into a controllable, optimizable system: starting with a clear understanding of the structure and key parameters in scraping_config.json, you’ll learn how to choose data sources and labels across Reddit / X / YouTube, use time and freshness logic to improve scoring efficiency, and manage scraping volume and cost—then continuously iterate your configuration based on signals from the Dashboard.

Unit 4 – Understanding the Scoring System and Optimizing Rewards

In this unit, we’ll fully break down where SN13 (Data Universe) rewards actually come from. Starting with the data value formula, you’ll learn how Dynamic Desirability (changing demand), Credibility, and deduplication/scarcity work together to determine your score and long-term earnings—so you can translate “optimization” into actionable scraping and configuration strategies.

Unit 5 - S3 Storage Configuration & Data Upload

In this unit, we'll cover how miner data flows from your local SQLite database to cloud S3 storage: understand S3's role as the "bridge" between miners and validators, master the path structure and authentication flow, and learn to configure upload parameters to ensure your data can be properly accessed and scored by validators.