C:\REETI\status > online

hi, i'm Reeti

Automating the boring stuff so humans can do the creative stuff.

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I build things that make messy systems elegant; from routing logistics across 50 warehouses to digitising centuries-old manuscripts with computer vision. Final-year CS undergrad who'd rather ship working code than write another to-do list about it. Currently breaking (and fixing) things in production at scale.

Technical Arsenal.

Languages

Python, TypeScript, Java, Bash

Frontend / Full-Stack

Next.js, React, Tailwind CSS

ML / AI

CNNs (TensorFlow/Keras, OpenCV), NLP (scikit-learn, NLTK), Google Vision AI

Backend & Data

REST APIs, ETL pipelines, async Firestore, data automation

Cloud / DevOps

GCP (Compute Engine, Cloud Storage, Firestore), Docker, Git, Gerrit

Leadership & Synergy

Communication, Team Leadership, Open-Source Mentorship

My Journey.

I've had the opportunity to tackle complex problems across different domains from digitizing historical texts to automating warehouse logistics. Here's how I approached them.

✦

Feb 2025 — Present

Full-Stack SWE Intern

VETRO GLOBALEDGE LLP

The Challenge: Warehouse logistics were plagued by manual tracking and inefficient SKU routing, leading to delayed fulfillment.

My Approach: I containerized the entire WMS (Warehouse Management System) backend using Docker, standardizing the deployment environment. Then, I developed an automated SKU routing algorithm.

The Impact: Delivered a robust automated SKU routing system that significantly streamlined warehouse logistics and reduced manual entry errors.

Docker Containerization

Automated Routing

May 2025 — Jun 2025

Research Intern

Open Knowledge Initiatives, IIIT Hyderabad

The Challenge: Digitizing old manuscripts and documents was a heavily manual, time-consuming process prone to human error.

My Approach: I engineered a custom Python pipeline integrating Google Vision AI for advanced OCR (Optical Character Recognition) capabilities, tailored for historical texts.

The Impact: The new pipeline cut digitization time by an impressive 60%, allowing researchers to access text data much faster.

60% Faster Digitization

Google Vision AI

Feb 2025 — Present

Open Source Contributor

Wikimedia Foundation

The Challenge: MediaWiki's massive global ecosystem relies on continuous community contributions, but new developers often struggle with complex legacy systems.

My Approach: I dove into the codebase, contributing technical patches via Gerrit and tracking bugs in Phabricator. I also focused on improving internationalization (i18n) technical documentation.

The Impact: Authored i18n docs merged into MediaWiki core (cited in FY25-26 Language Usability Annual Summary) and maintained an 88% patch acceptance rate.

88% Acceptance Rate

8/10 Tasks Merged

Aug 2024 — Oct 2024

Automation Intern

PING Networks

The Challenge: Recruitment teams were spending hours manually compiling and formatting applicant data into daily reports.

My Approach: I built automated ETL (Extract, Transform, Load) pipelines using Python to gather data, clean it, and automatically generate formatted reports.

The Impact: Reduced recruitment reporting time by 40%, freeing up the HR team to focus on interviewing rather than data entry.

40% Time Saved

Python ETL

My Work.

A collection of systems I've built, ranging from real-time logistics dashboards to machine learning models for agricultural disease detection.

Link2Logistics

A real-time logistics dashboard providing live tracking, fleet management, and dynamic route optimization for delivery operations.

Next.js

Firebase

Tailwind CSS

Sentiment Analysis Classifier

An NLP-based machine learning model utilizing Naive Bayes to classify textual sentiment with high accuracy, designed for social media monitoring.

Python

NLP

Naive Bayes

CaneCare

A CNN-powered disease detector for sugarcane crops. Uses a custom image classification pipeline built with TensorFlow and Keras to identify early signs of crop blight.

TensorFlow

Keras

CNN

Open Source & Community.

"For me, technology has never just been about writing code in isolation; it is about the people you build it with. My journey is deeply rooted in open-source philosophy and grassroots community building."

Acceptance Rate

0/10

Tasks Merged

Core Repos

Global Edits

Key Highlights

Authored i18n docs merged into MediaWiki core — cited in FY25-26 Language Usability Annual Summary
88% patch acceptance rate across multiple core repositories
1000+ edits globally on Wikimedia projects
8 tasks merged out of 10 in different core and famous repos

Open Source Profiles

Building the Free Knowledge Ecosystem

My heart lies in the open-source world, particularly within the Wikimedia Foundation's MediaWiki ecosystem. I regularly contribute technical code, track bugs via Phabricator, and submit patch sets through Gerrit.

I am incredibly proud to have 8 of my 10 tasks successfully merged, maintaining an 88% patch acceptance rate across repositories that serve millions of Wikipedia users globally. Beyond writing code, I authored internationalization (i18n) technical documentation that was merged into MediaWiki core and formally cited in the FY25-26 Wikimedia Language Usability Annual Summary.

But the most rewarding part of this ecosystem isn't just the code it's mentoring new contributors and helping them navigate their first open-source commits.