If you’re a coder with too much time on your hands this diverse list will help you get involved with some of the coolest, most cutting-edge projects out there so you can gain both experience and street cred.
• IBM Quantum
Develop quantum programs in Python. IBM Quantum leads the world in quantum computing, which aims to solve complex problems the world’s most powerful supercomputers cannot solve, and never will. Integrate quantum into your workflows with high-level libraries designed for scientific and business applications. Just a call to an API is all it takes to get a quantum result on your classical machine through the cloud — it just works behind the scenes. Write scripts combining code, equations, visualizations, and narrative text using Quantum Lab, powered by Qiskit. Quantum Lab offers hands-on learning for your first experience with quantum, using interactive tutorials and an open source textbook. Qiskit is an open source SDK that expresses quantum computing concepts intuitively and concisely in Python. Qiskit modules offer professionally developed, rigorously tested, and fully documented functionality for a wide range of scientific and business applications. Some notable projects from the community include Quantum Dice, Quantum Image Processing and Qonway’s Game of Life. View more on GitHub.
This is an entirely open-source VR headset that was created by Max Coutte and Gabriel Combe when they were 15 years old with a 3D printer and a soldering iron. If you’re a hacker who can’t afford the top tier headsets that are out today but want to give them a run for their money by creating your own gear then this project is for you. The headset supports SteamVR games and can be modified to add support for any DIY or off-the-shelf VR device. Its room-scaling AI can be used with any camera, and tracks your body based on video input. Precision and freedom of movement are still very far from dedicated sensors, however, Max Coutte believes that the model can be trained and improved by orders of magnitude. You can find the source code as well as the source for the electronics, hardware, and mechanical parts on Github along with the official guide and the latest release
Robot simulation is an essential tool in every roboticist’s toolbox. A well designed simulator makes it possible to rapidly test algorithms, design robots, perform regression testing, and train AI system using realistic scenarios. Gazebo offers the ability to accurately and efficiently simulate populations of robots in complex indoor and outdoor environments. At your fingertips is a robust physics engine, high-quality graphics, and convenient programmatic and graphical interfaces. Best of all, Gazebo is free with a vibrant community. Gazebo supports the ODE, Bullet, Simbody and DART physics engines. By default Gazebo is compiled with support for ODE. In order to use the other engines, first make sure they are installed and then compile Gazebo from source.
For more open-source projects like these visit GitHub FOSS.
Top comments (2)
It would be great if you can provide the relevant GitHub link :)
The original article on Hacker Noon has all the links I don't know what went wrong here: hackernoon.com/3-open-source-proje...
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