Participation

Mine or Read | both earn.

Miners secure the chain and earn Kovs. Readers build a verifiable accuracy record and shape the protocol. A Bitcoin address is the only requirement.

Option 01, 02
Miners
ASIC SHA-256 or CPU Python. Secure the chain. Earn Kovs per block.
Option 03, 04
Readers
Validate intelligence. Commit state predictions. Build a permanent on-chain accuracy record.
Option 01, Miner
ASIC, SHA-256
Antminer, Whatsminer, or any Bitcoin SHA-256 hardware. Runs alongside Bitcoin mining at zero extra power cost.
Pool Settings
Pool: stratum+tcp://pool.quantsynth.net:3333
Username: YOUR_BTC_ADDRESS.worker1
Password: x
Replace YOUR_BTC_ADDRESS with your Bitcoin address.
Command Line
cgminer --url stratum+tcp://pool.quantsynth.net:3333 \
        --user YOUR_BTC_ADDRESS.worker1 --pass x
Earns 250,000 Kov / block
Option 02, Miner
CPU, Python Miner
Any laptop, desktop, or VPS. No special hardware. If it runs Python it can mine.
Install and Run
pip install requests numpy py_ecc
curl -O https://api.quantsynth.net/sdk/markovian.py
python3 markovian.py mine --address YOUR_BTC_ADDRESS
Submits blocks directly to the node. Kovs issue to your BTC address per block found.
Earns 250,000 Kov / block
Block Reward
250,000 KOV
per block mined
Revenue Share
BTC
Kov holders, pro-rata
Genesis Window
Open Now
lowest issuance cost ever
Treasury Floor
…
BTC reserves / supply
Check Your Balance
Questions: [email protected]  ·  Bitcointalk ANN ↗
Accuracy is power.
In the Markovian governance system, your on-chain accuracy record determines how much weight your vote carries over the transition matrix M. The matrix governs the entire protocol, every block, every state, every payout. Miners secure the chain. Readers shape it.
See the current rankings →
Option 03, Light Reader
Run Matrix M
Same chain, no hardware required. Run the canonical model against each block, submit your read. The chain scores your accuracy 10 blocks later. Your record is on-chain.
How It Works
Every block contains an s_output vector, a probability distribution over three states. You read it, classify it, submit it. Protocol scores your accuracy against on-chain output 10 blocks later. All on-chain. No oracle.
Install and Run
pip install requests numpy
curl -O https://api.quantsynth.net/sdk/reader.py
python3 reader.py YOUR_BTC_ADDRESS
Polls every 65 seconds. Classifies 6 dimensions. Submits automatically.
Leaderboard
curl https://api.quantsynth.net/leaderboard
Builds on-chain accuracy record
Option 04, Deep Reader
Build Matrix M
This path is for quant shops and data operations running the full Markov model. You have the infrastructure. You know how to derive state from data. Commit your state read before the block confirms, reveal after. The chain scores you against actual output. A verified accuracy record here carries governance weight. That is not available anywhere else.
How It Works
You derive s_input from the current block hash, apply your M matrix, and commit a hash of your prediction before the next block lands. After confirmation you reveal. The chain verifies your commitment hash, then scores your predicted state against actual chain output. Consistent early calls build a verifiable edge.
Your Matrix
# Genesis M, the canonical starting point
M = [[0.70, 0.25, 0.05],
     [0.10, 0.75, 0.15],
     [0.20, 0.15, 0.65]]

# Yours, same shape, your parameters. Rows must sum to 1.0.
M_yours = your_trained_matrix
Commit-Reveal
# Step 1, commit before the block mines
python3 reader.py YOUR_BTC_ADDRESS --deep

# Step 2, reveal happens automatically after target block confirms
Commit window: target block must be in the future. Reveal window: 5 blocks after target. Expired commitments are discarded.
Protocol Spec
Block schema, s_input derivation, commitment hash construction, and scoring algorithm: white paper.
Verifiable predictive track record on-chain
API Access
5B KOV
~$5,000 · claim a data key on-chain
Governance Weight
Accuracy
shapes matrix M, protocol rules
Record
On-Chain
permanent, public, verifiable
Leaderboard ↗  ·  Whitepaper ↗  ·  [email protected]