This is a collection of three python3 blockchain libraries - chainlib, chainsyncer and chainqueue. Chainlib provides tooling for encodings for Solidity-EVM and Ethereum node networks. Chainqueue facilitates bulk send of transactions. Chainsyncer processes all transactions in mined blocks, and executes pluggable code for each of them.
It also provides Crypto Dev Signer, a daemon for use in development that performs Ethereum signatures over its standard JSON-RPC, along with a keystore with memory or sql backends. It also contains a cli tool to create and parse keystore files.
Taint tags crypto addresses, and merge tags for crypto addresses that trade with each other. It can be used as a simple forensic tool to check whether cryptographic identities are isolated from each other.
Statsyncer collects and aggregates blockchain state, like gas prices, over time. It in turns serves this data on a (JSON-RPC) API.
Leverages the HTTP HOBA challenge-response authentication scheme to authenticate with PGP and Ethereum wallets. It is supported by the dependencies python-http-hoba-auth and python-yaml-acl, which provide parsing of HOBA authorization strings and a simple YAML-based ACL structure respectively.
A small implementation of the Recursive Length Prefix serialization format in C. A python interface pylibrlp is also provided.
Work logging in the spirit of the absolutely awesome Taskwarrior and Timewarrior tools. Written in Python, it uses the filesystem as backend, and MIME Multiparts to allow attachments to the log items. The ambition is to integrate with Taskwarrior one day.
Mirroring tool to migrate your git repositories between computers without copying objects, and update existing repositories from remotes recursively. Written in bash.
A library that aims to simplify mutually signing generic serializable items offline with handheld devices. Leverages Typescript and Protobuf.
 A phenomenological law also called the first digit law, first digit phenomenon, or leading digit phenomenon. Benford's law states that in listings, tables of statistics, etc., the digit 1 tends to occur with probability ∼30%, much greater than the expected 11.1% (i.e., one digit out of 9). https://mathworld.wolfram.com/BenfordsLaw.html