Blockprint.
Machine Learning for Ethereum Consensus
Blockprint. Shaping the Future of Blockchain Transparency
Dive into insights
Blockprint is currently undergoing retraining.
Diversity data will be updated and available shortly.
Diversity Charts
Where does this data come from?
Blockprint classifies blocks using a machine-learning model which sometimes makes mistakes. The statistics shown below are measurements of blockprint’s accuracy using a cluster of consensus clients that produce blocks every slot.
Dive into precision tables
Blockprint is currently undergoing retraining.
Precision data will be updated and available shortly.
Precision Charts
Overview
TPR Precision
PPV Precision
Used By
Blockprint is a tool for determining which consensus clients produced which blocks on the Ethereum mainnet.
It uses machine learning to guess the consensus client for a block, based on the similarity of that block to others in its training data. E.g. when blockprint saw the block at slot 6505122, it determined that this block was most likely produced by Prysm.
How does it work?
Diversity Charts
Validator Diversity
Precision Charts
Overview
TPR Precision
PPV Precision