Overview
Registration Activity
Neuron registration trends
Neuron registration trends
The alpha price in τ, day by day, and what moved it each way.
Log change over the last 30 days −18.13%: protocol buys 0.00%, root sells −0.44%, users net −18.58%, cross-subnet +0.89%, unattributed +0.0001%.
The chart follows the alpha price in τ. Each period starts at its opening price and steps through the mechanisms below, in a fixed order, to its closing price: a step that rises pushed the price up, one that falls pushed it down, and its height is how far it moved the price. A thin line carries each close into the next period’s open; it is dashed where the two differ. The faint candle behind each period is the market’s own open, high, low and close, for context.
With many periods on screen (90 days, or hours), the steps would be too thin to read, so the chart draws the price as a line instead and colours each period by the mechanism that moved it most. Hover a period for the full step-by-step breakdown.
Not here, because they do not move the spot price: the per-block TAO injection into the pool, and burns.
All figures are log changes, not simple percentage changes, so the parts add up exactly to the total; on big moves the two differ (a 10% log fall is a 9.5% price fall).
2026-11-10 UTC, as of ≈23:32 UTC (block 9,479,148)
The day isn’t over yet, so these figures keep changing. They are in no daily total, average or rank.
Holder & circulating-alpha metrics as of (daily close)
Organizations need large volumes of high-quality annotated data to train and evaluate AI systems, but human annotation is expensive, slow, and difficult to scale. Crowd-sourcing platforms struggle with quality consistency, and hiring specialized annotators for domain-specific work is cost-prohibitive. A decentralized network of independently-operated annotation nodes can process data at scale while maintaining quality through economic incentives: validators establish ground truth on full datasets, miners compete on tag quality, and scoring mechanisms enforce alignment through cosine similarity validation. ReadyAI implements this as a subnet on Bittensor where validators pull raw conversations or documents, tag them comprehensively to establish ground truth, then distribute windowed subsets to independent miners who generate annotations. Miners' submissions are scored against the validator's ground truth, and rewards are distributed based on tag accuracy and relevance—creating a cost-efficient, scalable annotation pipeline.
Auto-generated from the GitHub repository and may not reflect the latest changes.
ReadyAI is a decentralized data structuring and semantic tagging pipeline built on Bittensor that converts raw unstructured data into AI-ready structured data. Validators establish ground truth by tagging full conversations/documents and create data windows for miners to process. Miners receive windows and generate metadata tags and embeddings, which are scored by validators using cosine similarity against the ground truth.
The system supports multiple task types including conversation tagging, webpage metadata generation, survey tagging, named entity extraction, and skill generation.
ReadyAI follows a fractal data mining architecture: validators pull full conversations/documents from the ReadyAI API (or custom data sources), establish ground truth by generating comprehensive semantic tags and embeddings for the full content, then break the content into windows and mask sensitive data. These masked windows are distributed to multiple miners who process them independently using configured LLMs to generate tags and embeddings. Miners return their results to validators, which score each submission against the ground truth using cosine similarity, then upload results to persistent storage.
Validators are rewarded for accurate ground truth establishment and scoring; miners are rewarded based on tag quality. The system supports encrypted endpoint commitments where miner addresses are encrypted on-chain and only validators with the private key can decrypt them to reach miners.
For tagging-style tasks: the validator generates ground truth tags for full source content, breaks it into windows, and sends masked windows to miners. Each miner tag is scored by cosine similarity to the embedding neighborhood of ground truth tags. Final miner score blends: top-3 unique tag mean (55% weight), overall mean tag score (25% weight), median tag score (10% weight), and single top score (10% weight).
Penalties apply if: no tags overlap with ground truth, fewer than minimum unique tags submitted, or all tags score below threshold. For skill_coverage_evaluation tasks: scoring blends section coverage and skill coverage via embedding similarity, with LLM-as-judge verification of test assertions to prevent fabrication.
The sections above were auto-generated from the GitHub repository and may not reflect the latest changes. View repository (github.com, opens in a new tab, external site)
This information was auto-extracted from the GitHub repository and may be incomplete or out of date. Always refer to the subnet's GitHub repository (github.com, opens in a new tab, external site) for the latest instructions.
Miners using OpenAI API need CPU with 8GB RAM and 20GB disk; GPU with 24GB VRAM recommended for production
Not yet verified: Notes
Machine-extracted from the subnet's repository docs; may be wrong or out of date. Check the repository before buying or renting hardware. Open the repository (github.com, opens in a new tab, external site) · Extracted 2 Nov 2026
Clone the repository
git clone https://github.com/afterpartyai/bittensor-conversation-genome-project.git cgp-subnet && cd cgp-subnet
Install Python dependencies
pip install -r requirements.txt
Create environment configuration file
cp env.example .env
Edit .env to add OpenAI API key and other required configurations
nano .env (or your preferred editor)
Run tests to verify setup (optional but recommended)(optional)
python -m pytest -s --disable-warnings tests/test_full_loop.py
Register hotkey on testnet (netuid 138) or mainnet (netuid 33)
btcli s register --netuid 138 --wallet.name <coldkey> --wallet.hotkey <hotkey> --network test --wallet.path ~/.bittensor/wallets
Start miner on testnet
python3 -m neurons.miner --subtensor.network test --netuid 138 --wallet.name <coldkey> --wallet.hotkey <hotkey> --logging.debug --axon.port <port>
Clone the repository
git clone https://github.com/afterpartyai/bittensor-conversation-genome-project.git cgp-subnet && cd cgp-subnet
Install Python dependencies
pip install -r requirements.txt
Create environment configuration file
cp env.example .env
Edit .env to add OpenAI API key, WandB API key, and configure database endpoints
nano .env (or your preferred editor)
Set testnet private key (provided in env.example) or mainnet key (distributed securely)
export COMMITMENT_PRIVATE_KEY=<testnet_key_or_mainnet_key>
Generate validator API key by running generation script (for testnet add 'test' argument)
python scripts/get_validator_api_key.py test
Register hotkey on testnet (netuid 138) or mainnet (netuid 33)
btcli s register --netuid 138 --wallet.name <coldkey> --wallet.hotkey <hotkey> --network test --wallet.path ~/.bittensor/wallets
Start validator on testnet
python3 -m neurons.validator --subtensor.network test --netuid 138 --wallet.name <coldkey> --wallet.hotkey <hotkey> --logging.debug --axon.port <port>
| Day (UTC) | Share |
|---|---|
| 2026-11-09 | 0.01% |
| 2026-11-08 | 0.01% |
| 2026-11-07 | 0.01% |
| 2026-11-06 | 0.01% |
| 2026-11-05 | 0.01% |
| 2026-11-04 | 0.01% |
| 2026-11-03 | 0.01% |
| 2026-11-02 | 0.01% |
| 2026-11-01 | 0.01% |
| 2026-10-31 | 0.01% |
| 2026-10-30 | 0.01% |
| 2026-10-29 | 0.01% |
| 2026-10-28 | 0.01% |
| 2026-10-27 | 0.02% |
| 2026-10-26 | 0.02% |
| 2026-10-25 | 0.02% |
| 2026-10-24 | 0.02% |
| 2026-10-23 | 0.02% |
| 2026-10-22 | 0.03% |
| 2026-10-21 | 0.02% |
| 2026-10-20 | 0.02% |
| 2026-10-19 | 0.03% |
| 2026-10-18 | 0.03% |
| 2026-10-17 | 0.03% |
| 2026-10-16 | 0.03% |
| 2026-10-15 | 0.03% |
| 2026-10-14 | 0.04% |
| 2026-10-13 | 0.04% |
| 2026-10-12 | 0.04% |
| 2026-10-11 | 0.04% |
| 2026-10-10 | 0.04% |
| 2026-10-09 | 0.05% |
| 2026-10-08 | 0.05% |
| 2026-10-07 | 0.05% |
| 2026-10-06 | 0.07% |
| 2026-10-05 | 0.06% |
| 2026-10-04 | 0.05% |
| 2026-10-03 | 0.05% |
| 2026-10-02 | 0.06% |
| 2026-10-01 | 0.05% |
| 2026-09-30 | 0.07% |
| 2026-09-29 | 0.06% |
| 2026-09-28 | 0.06% |
| 2026-09-27 | 0.06% |
| 2026-09-26 | 0.10% |
| 2026-09-25 | 0.08% |
| 2026-09-24 | 0.08% |
| 2026-09-23 | 0.08% |
| 2026-09-22 | 0.09% |
| 2026-09-21 | 0.09% |
| 2026-09-20 | 0.09% |
| 2026-09-19 | 0.09% |
| 2026-09-18 | 0.08% |
| 2026-09-17 | 0.10% |
| 2026-09-16 | 0.11% |
| 2026-09-15 | 0.09% |
| 2026-09-14 | 0.09% |
| 2026-09-13 | 0.09% |
| 2026-09-12 | 0.08% |
| 2026-09-11 | 0.08% |
| 2026-09-10 | 0.09% |
| 2026-09-09 | 0.11% |
| 2026-09-08 | 0.10% |
| 2026-09-07 | 0.10% |
| 2026-09-06 | 0.12% |
| 2026-09-05 | 0.15% |
| 2026-09-04 | 0.14% |
| 2026-09-03 | 0.15% |
| 2026-09-02 | 0.13% |
| 2026-09-01 | 0.11% |
| 2026-08-31 | 0.12% |
| 2026-08-30 | 0.12% |
| 2026-08-29 | 0.13% |
| 2026-08-28 | 0.14% |
| 2026-08-27 | 0.14% |
| 2026-08-26 | 0.14% |
| 2026-08-25 | 0.15% |
| 2026-08-24 | 0.15% |
| 2026-08-23 | 0.14% |
| 2026-08-22 | 0.17% |
| 2026-08-21 | 0.20% |
| 2026-08-20 | 0.19% |
| 2026-08-19 | 0.20% |
| 2026-08-18 | 0.21% |
| 2026-08-17 | 0.21% |
| 2026-08-16 | 0.21% |
| 2026-08-15 | 0.23% |
| 2026-08-14 | 0.25% |
| 2026-08-13 | 0.25% |
| 2026-08-12 | 0.25% |