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Jason Liu's Projects

awesome-blockchain-cn icon awesome-blockchain-cn

收集所有区块链(BlockChain)技术开发相关资料,包括Fabric和Ethereum开发资料

bcmaster icon bcmaster

A real-time blockchain monitor and comprehensive benchmarking tool

blockbench icon blockbench

BLOCKBENCH: A Framework for Analyzing Private Blockchains. Blockbench contains workloads for measuring the data processing performance, and workloads for understanding the performance of different layers of Blockchain systems.

blockchain-paper icon blockchain-paper

In this project, I will collect some classic blockchain-related papers for researchers.

blocksci icon blocksci

A high-performance tool for blockchain science and exploration

burrow-10-node icon burrow-10-node

The configuration files of a 10-node P2P network of Hyperledger Burrow (Using Tendermint consensus).

cc-attack icon cc-attack

Using Socks4/5 proxy to make a multithread Http-flood/Https-flood (cc) attack.

dashboard icon dashboard

General-purpose web UI for Kubernetes clusters

dtlg icon dtlg

App for making the IOTA network faster.

eaot_prompt-and-evo icon eaot_prompt-and-evo

The open source implementation of "Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers"

eos icon eos

An open source smart contract platform

eosbenchtool-modified icon eosbenchtool-modified

An useful tool for the performance benchmark of EOS. Note that Lancelot1998 modified some errors in the official version.

eosio.cdt icon eosio.cdt

EOSIO.CDT (Contract Development Toolkit) is a suite of tools used to build EOSIO contracts

fabric-10-node icon fabric-10-node

The configuration files of a 10-node P2P network of Hyperledger Fabric (Solo consensus, 1 Orderer, 2 CAs, 1 Channel).

fabric-test icon fabric-test

Read-only mirror of https://gerrit.hyperledger.org/r/#/admin/projects/fabric-test

fedlab-nlp icon fedlab-nlp

FedLab-NLP: A Fast and highly Customizable Framework for Federated Learning in Natural Language Processing

generative-models icon generative-models

This project aim to share the knowledge and code concerning generative models.

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