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followers: 124.0 following: 2.0K repos: 2.6K gists: 9.0

Name: mingfeng.zhang

Type: User

Bio: all things distributed

Location: beijing china

mingfeng.zhang's Projects

nginx-quick-reference

:zap: This notes describes how to improve Nginx performance, security and other important things; ssllabs A+ 100%.

nginx-systemtap-toolkit

Real-time analyzing and diagnosing tools for Nginx based on SystemTap

nginx-upsync-module

nginx-upsync-module, sync upstreams from consul or etcd and so on, dynamiclly modify backend-servers attributes(weight, max_fails, down...), needn't reload nginx

nginxxer

A programmatic extensible config generator for the popular web server nGinx

ngrinder

enterprise level performance testing solution

ngrok client for c language,Due to the use of GO ngrok language development, porting to embedded devices some inconvenience, such as openwrt, so use C language rewrite a client. Very mini, the need to support polarssl library.

ngt

Nearest Neighbor Search with Neighborhood Graph and Tree for High-dimensional Data

ngt-rs

Rust wrappers for NGT approximate nearest neighbor search

ngx_4c

n4c programming architecture, the Controllable, Computable and Communication Cluster in nginx.

ngx_cc

a framework of Nginx Communication Cluster. reliable dispatch/translation messages in nginx nodes and processes.

ngx_http_dyups_module

update upstreams' config by restful interface

ngx_lua_waf

ngx_lua_waf是一个基于lua-nginx-module(openresty)的web应用防火墙

ngx_tasks

Tasks management module for lua in nginx

nightwatch

证券异常交易行为监控系统。九州证券系统开发部自主设计、研发的证券异常交易行为监控系统，采用Storm+Esper架构实现，支持金证交易系统的指令拦截、复制与解析。

ninechronicles

Unity client application for Nine Chronicles, a fully decentralized idle RPG powered by the community.

njspecs

NJ'S Components Specs. 组建仓库,私有库,组件开发,私有库,组件化私有库

nlp-beginner

上手教程

nlp-competitions-list-review

复盘所有NLP比赛的TOP方案，只关注NLP比赛，持续更新中！

nlp-in-python

Intro to some NLP concepts in Python for a class

nn4nlp2017-code

Code Samples from Neural Networks for NLP

nni

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.