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Hossam Karim's Projects

activej icon activej

ActiveJ is an alternative Java platform built from the ground up. ActiveJ redefines web, high load, and cloud programming in Java, featuring ultimate performance and scalability!

airwindows icon airwindows

This contains all the Airwindows audio open source code

arpligner icon arpligner

A multi-track & polyphonic arpeggiator plugin that takes both chords and arp patterns as live MIDI data

blackhole icon blackhole

BlackHole is a modern macOS audio loopback driver that allows applications to pass audio to other applications with zero additional latency.

camomile icon camomile

An audio plugin with Pure Data embedded that allows to load and to control patches

chat.io icon chat.io

A Real Time Chat Application built using Node.js, Express, Mongoose, Socket.io, Passport, & Redis.

coremidi4j icon coremidi4j

Core MIDI Service provider Interace (SPI) for Java 1.7 and above on OS X

cpp-httplib icon cpp-httplib

A C++ header-only HTTP/HTTPS server and client library

dash-to-panel icon dash-to-panel

An icon taskbar for the Gnome Shell. This extension moves the dash into the gnome main panel so that the application launchers and system tray are combined into a single panel, similar to that found in KDE Plasma and Windows 7+. A separate dock is no longer needed for easy access to running and favorited applications.

ddia-references icon ddia-references

Literature references for “Designing Data-Intensive Applications”

doobie icon doobie

Functional JDBC layer for Scala.

dot-calculus icon dot-calculus

Formalization of the Dependent Object Types (DOT) calculus

dotty icon dotty

Research compiler that will become Scala 3

eqgrp icon eqgrp

Decrypted content of eqgrp-auction-file.tar.xz

gsp-graphql icon gsp-graphql

Experimental GraphQL support for Gemini Science Platform

hackpad icon hackpad

Hackpad is a web-based realtime wiki

handson-ml2 icon handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

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