WELCOME TO MY LITTLE CORNER OF THE INTERNET

DINKAR
GUPTA.

Crafting thoughtful interfaces, building reliable systems,
and following my curiosity. One line of code at a time.

A LITTLE ROOM FOR BIG IDEAS.

JavaScriptPythonCC++SQLReact.jsNext.jsTailwind CSSRedux ToolkitRTK QueryTypeScriptMaterial UIVitestJestDjangoDjango REST FrameworkFastAPINode.jsExpress.jsREST APIsMicroservicesRedisARQPostgreSQLMongoDBMySQLFirestoreAWSDockerLinuxGitGitHubGitLabOpenSearchPostmanGenerative AILLMsDatabase Management SystemsOOPSComputer NetworksComputer Architecture

01 / WHO_AM_I

A curious mind.
A builder at heart.

The person on the other side of the screen.

player_01.profile↗

Dinkar Gupta Gurugram, India

Hi, I'm Dinkar ✳

Turning ‘what if’s into ‘what's next’.

I build with React.js, Next.js, and Django. I care about clean, maintainable code and the real-time systems that make software feel alive.

Curiosity is my default setting, sometimes excessively so. I want to understand why something works, not just how to make it work. That means exploring the details, questioning assumptions, and always finding something new to learn.

I'm usually in serious mode. But that focus is how I untangle problems, follow an idea through, and get things done.

Currently exploring agentic AI & real-time systems

GitHub

@Dinkar850

Connecting to scoreboard_

LeetCode

@dinkar_gupta_15

Connecting to scoreboard_

02 / POWER-UP INVENTORY

The tools in my inventory.

Different tools. One goal: make something that works beautifully.

Languages

JavaScript

Python

C++

SQL

Frontend & Testing

React.js

Next.js

Tailwind CSS

Redux Toolkit

RTK Query

TypeScript

Material UI

Vitest

Jest

Backend

Django

Django REST Framework

FastAPI

Node.js

Express.js

REST APIs

Microservices

Redis

ARQ

Databases

PostgreSQL

MongoDB

MySQL

Firestore

Cloud & DevOps

AWS

Docker

Linux

Tools & Technologies

Git

GitHub

GitLab

OpenSearch

Postman

AI & emerging tech

Generative AI

LLMs

Core Engineering

Database Management Systems

OOPS

Computer Networks

Computer Architecture

Always room for another power-up.

03 / CHARACTER UPGRADES

Learning. Building.
Leveling Up.

Real work, new challenges, and a little more experience every day.

Josh Technology Group / JTG

Software Development Intern

Gurugram, India·September 2025 - October 2026

Responsibilities

Develop and maintain production features using React, JavaScript, Django and FastAPI.
Work on core frontend and backend functionalities, including collaboration in shared component libraries, development of dashboards, participant management, examination workflows, notifications and background jobs.
Debug production issues using Sentry, application logs and codebase analysis.
Work closely with Firestore, Redis and OpenSearch for data management, caching and search.
Contribute to microservice workflows, data synchronisation and scheduled jobs.
Collaborate effectively with fellow and senior engineers on requirements, implementation, testing and deployment.

Highlights

Built a centralized examination email notification system using Python, ARQ, and scheduled background cron jobs to automate collaborator reminders across workflows, improving overall user productivity.
Optimized existing result publishing flow by reducing database queries by 10% and developing cleanup scripts for stale participant data across 500+ participant records per event, resulting in a 45% reduction in processing latency.
Automated local development environment setup using Bash scripts for initializing OpenSearch Docker containers, starting Redis clusters, index creation, and swap memory configuration, saving 80% of initial setup time.
Developed a production-grade dashboard in React using Redux, supporting 1000+ examinations with data aggregation and analytics enabling faster operational insights.
Resolved app-wide caching issues and optimized data-fetching strategies, improving each page load time by 25%.

ReactJavaScriptDjangoFastAPIFirestoreRedisOpenSearch

04 / MISSIONS COMPLETED

Things from the workbench.

A place for experiments, solved problems, and things worth making.

EXPLORE GITHUB

DG-01

MOVIE BOOKING UI

BookMovies - Frontend

A movie-booking interface for discovering movies and cinemas, filtering showtimes, and booking seats.

ReactRTK QueryJavaScript

Mission notes

Centralized data fetching with RTK Query, multi-token authentication and automatic refresh.
Reusable hooks support infinite scrolling.
URL-based filters keep movie, cinema, and showtime searches shareable.

Source

DG-02

BOOKING API

BookMovies - Backend

The API behind BookMovies, covering movie discovery, cinema management, showtimes, and concurrent seat booking.

PythonDjangoDRFPostgreSQLJWT

Mission notes

RESTful APIs support cursor pagination, filtering, sorting, and search.
Multi-token JWT authentication supports multiple devices.
Atomic database transactions prevent conflicting seat bookings.

Source

The Willow Paradise guest homepage with luxury cabins

DG-03

HOTEL RESERVATIONS

The Willow Paradise - Guest Portal

A guest-facing hotel reservation website for exploring cabins and managing bookings.

Next.jsContext APINextAuth

Mission notes

Secure authentication, profile management, and self-service reservations across 7+ cabin categories.
Guests can create, modify, and cancel their bookings.

Source

The Willow Paradise employee portal sign-in screen

DG-04

HOTEL MANAGEMENT

The Willow Paradise - Employee Portal

An employee-facing workspace for hotel operations, reservation management, and analytics.

ReactTanStack QueryStyled ComponentsSupabase

Mission notes

Analytical dashboards and reservation management for hotel operations.
Role-based operations built with TanStack Query, Styled Components, and Supabase.
Designed to manage 1000+ reservations.

Source

DG-05

SENTIMENT ANALYSIS

SentiLearn - Course Analyzer

An aspect-based sentiment analyzer that turns YouTube course comments into feedback about specific course aspects.

PythonBERTPyABSAFlaskMongoDBReact

Mission notes

A BERT-based transformer classifies sentiments across predefined aspects in large-scale YouTube comment datasets.
Achieved 85% classification accuracy on previously and newly collected reviews.

Source

05 / The Knowledge Tree