flameberry,Aditya Gaikwad,github

Hi there 👋 I am Aditya Gaikwad from India and I am developing a Game Engine! Big one to take in right? Here's how you can get to know more about it:

→ Official Github Repository
→ Flameberry Website

Isn't it fun to look at some good ol' GitHub Stats?

Aditya Gaikwad's Projects

dehazing

Image dehazing is the process of scenically improving the degraded visibility caused by atmospheric conditions. Our aim is to restore the scene radiance from the hazy image. Several methods have been proposed to improve the quality of hazy images, including estimating the transmission function based on a linear model using deep learning techniques.

flameberry.github.io

This is the official repository for the Flameberry Website which is made using Hugo.

flameberryccompiler

Flameberry C compiler is primitive C compiler which is in early stages and the future plan is to make it a fully featured compiler with decent performance.

flameberryengine

Flameberry Engine is a C++ Game Engine based upon the Vulkan Graphics API and is currently under development. My goal is to build a capable fast and efficient 3D game engine which can ship games on platforms like macOS, Windows, and Linux, and maybe Consoles, PlayStations, Android and iOS in the future!

flameberrywebdjango

This is the former version of the Flameberry Website made using Django as the backend to showcase the Flameberry Game Engine made using mostly vanilla HTML and CSS (except for the Navbar which uses Bootstrap5).

interpreteringo

Based on the book - "Writing an Interpreter in Go".

light-dehazenet

Light-DehazeNet: A Novel Lightweight CNN Architecture for Single Image Dehazing

physx

NVIDIA PhysX SDK

raytracingcpu

Work in progress implementation of a basic raytracer using OpenGL and C++. This is currently performed on the CPU and is expected to be moved on to the GPU later.

regression

This is the repository where you'll get the comparison of many regression methods by their accuracies, errors, etc. The above is achieved by introducing you to an user interface where you can import any dataset you want and choose the column that you want to predict and predict it using various methods and comparing them by their accuracies.

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