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Hi there! 👋

I'm Ashutosh, a Compiler Engineer at AMD India. I work on Compilers and IoT. I've done Google Summer of Code with Arduino. I'm also a former Linux Foundation mentee with ELISA. I currently volunteer for the Free Software Foundation in my free time.

Ashutosh's github stats

Ashutosh Pandey's Projects

extendedreality icon extendedreality

Inspired by Pranav Mistry's sixth sense project. Uses Raspberry Pi, Raspicam, Machine Learning, OCR and CV to make a gesture controlled computer.

facepixer icon facepixer

Built for NITK Hackverse Hackathon. Uses GANs (Generative Adversarial Networks) to generate photorealistic images from sketches

fyp-latex icon fyp-latex

Final Year Project Template in LaTeX for CSE, DSCE.

glibc-cov icon glibc-cov

Repository to store the work of Glibc-coverage project for ELISA under communitybridge

healthhack icon healthhack

A next gen healthkit that reads ECG, EMG, Temperature, Pressure, Blood Alcohol Conc and takes Thermal Images to diagnose medical conditions anywhere, anytime.

julia icon julia

The Julia Programming Language

labview-motion-capture icon labview-motion-capture

Using Arduino, MPU6050, Flex Resistors, LCD and LabVIEW GUI to create a motion capture wearable device. Won Jury's Choice award in Rakathon 2018

llvm-clang-samples icon llvm-clang-samples

UNMAINTAINED: Examples of using the LLVM and Clang compilation libraries and tools

micropy-convert icon micropy-convert

A source to source compiler tool to convert Arduino sketches to MicroPython. Developed for Arduino in GSoC 2020

prosthetichand icon prosthetichand

Prototype for an affordable, modular, myoelectric prosthetic arm. Electronics development underway, design files included. Won 1st place at Dassault Systemes Aakruti competition

theia icon theia

Eclipse Theia is a cloud & desktop IDE framework implemented in TypeScript.

tldw icon tldw

Convert videos to short text summaries, automatically

workoutsleeve icon workoutsleeve

Project built by Team Arcis in Smart India Hackathon, Hardware Edition - July 2019. Uses Machine learning and Raspberry Pi to detect, classify and measure the accuracy of unconventional exercises. Project won 1st place nationwide.

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