Rupam Das

Builder & creator

Rupam Jyoti Das.

I make software for people. I find a problem worth solving, then build until it's solved.

About

I like to understand how things work, then build my own take on them. I wrote a programming language to learn how code really runs. I gave personal goals an AI product owner you can talk to on a live call. I built a guitar trainer that listens to you play, and a studio that turns an idea into a finished video. I work on every layer, from the first idea to the running product.

12

things built outside my day job

2

already running in the cloud

~90%

manual work removed by a GenAI platform I led at work

4

awards and hackathon wins

After hours

Things I’ve built. 

Ollivue

Companies have product owners.Now your goals do too.

Every goal gets an AI product owner with a name and a personality. It plans your week in sprints, checks in daily, picks up the phone for a live voice call, and reviews your week honestly until the goal is done.

Android build in testingVisit Ollivue

Built with

  • Flutter
  • Dart
  • Riverpod
  • Python
  • FastAPI
  • SQLAlchemy
  • PostgreSQL
  • OpenAI
  • WebRTC voice
  • Firebase Auth
  • Firebase Messaging
  • Google Cloud Run
Ollivue chat with Maya, an AI product owner, planning a sprintOllivue chat with Maya, an AI product owner, planning a sprint
Ollivue's Today screen with sprint progress and a daily check-inOllivue's Today screen with sprint progress and a daily check-in
Ollivue's sprint board grouped by statusOllivue's sprint board grouped by status

Shorts Studio AI

Ideas in.Shorts out.

Start from a long video, a link, a topic, a script or today's trends. It researches, writes, voices, cuts and captions finished vertical videos, and can run the whole pipeline on a schedule with you reviewing before anything is published.

Running in the cloudVisit Shorts Studio AI

Built with

  • Next.js
  • React
  • TypeScript
  • Tailwind CSS
  • React Flow
  • Python
  • FastAPI
  • Celery
  • Redis
  • PostgreSQL
  • Whisper
  • OpenCV
  • Remotion
  • Claude API
  • OpenAI
  • Firebase
  • Docker
  • Turborepo
  1. Import
  2. Transcribe
  3. Find moments
  4. Edit
Shorts Studio AI's landing page: Ideas in. Shorts out.

FretGod

One skill. One screen.Focused practice.

A fretboard trainer for guitar players. It shows a note and a string, you find it on your real guitar, and the microphone checks your intonation. Levels, custom drills and a practice history keep you coming back.

Working app, 604 tests passing

Built with

  • Flutter
  • Dart
  • Riverpod
  • Real-time pitch detection
  • Audio capture
  • Automated testing

Jam

Find musicians. Build a jam. Show up and play.

Musicians shouldn't need a WhatsApp group to play together. Jam handles discovery, forming a session, venue listings, group chat and setlists.

Working web app, venue booking next

Built with

  • Next.js
  • React
  • TypeScript
  • Tailwind CSS
  • Prisma
  • PostgreSQL
  • Auth.js
  • Zod
  • Resend
  • PWA
Jam's landing page: Find musicians. Build a jam. Show up and play.

Extractly

Drop in a pile of PDFs. Get clean data out.

Say what you want extracted and an LLM pipeline works through the documents, tracking where every answer came from. Swap between Bedrock, OpenAI and Gemini.

Pipeline working, dashboard in progress

Built with

  • Go
  • PostgreSQL
  • Redis
  • AWS SQS
  • AWS S3
  • AWS Bedrock
  • OpenAI
  • Gemini
  • Prometheus
  • Next.js
  • React
  • TypeScript
  • Tailwind CSS

RecallAI

The right memory, at the right moment.

Capture lessons as you learn them. Before your next decision, ask, and it brings back what past you already figured out. Some memories can stay on your device.

Working mobile app

Built with

  • React Native
  • Expo
  • TypeScript
  • TanStack Query
  • Zustand
  • ExecuTorch on-device AI
  • SQLite
  • Python
  • FastAPI
  • PostgreSQL
  • pgvector
  • OpenAI
  • Google Sign-In

VisitWise

Every office visit, from invite to check-out.

Staff invite a guest and each invitation gets a QR pass. Security pairs a phone as a scanner, checks visitors in at the gate, handles walk-ins and logs belongings, while admins see who came and when.

Working MVP, built in 2024

Built with

  • Next.js
  • React
  • TypeScript
  • Tailwind CSS
  • daisyUI
  • Chart.js
  • Python
  • Django
  • Django REST Framework
  • Django Channels
  • Redis
  • SQLite
  • QR codes
VisitWise's admin analytics with visit counts, charts by day and month, and a summary table, shown with demo data

LiteCode

A coding-practice platform, built as microservices.

A LeetCode-style platform: pick a problem, write a solution in the browser editor, and a judge runs it against test cases in a Docker sandbox. Behind it, separate Spring Boot services handle login, users, questions, code execution, judging and notifications.

Built in 2024

Built with

  • Java
  • Spring Boot
  • Spring Cloud Gateway
  • Eureka
  • Kafka
  • Docker sandboxes
  • PostgreSQL
  • MongoDB
  • Zipkin
  • Next.js
  • TypeScript
  • Monaco Editor
Spring Boot microservicesHow the platform is put together
  1. Next.js web app
  2. API gateway
  • Auth
  • Users
  • Questions
  • Code runner
  • Judge
  • Notifications

Eureka registry · Config server · Kafka · Zipkin tracing

Innect

A smart home, from the chip to the app.

Switch devices on and off, room by room, from your phone. I built every layer: firmware for ESP8266 Wi-Fi chips, a real-time server that relays commands over WebSockets, and three apps to control it on the web, in Flutter and in React Native.

Built 2021–2023

Built with

  • C++
  • ESP8266
  • PlatformIO
  • Node.js
  • Express
  • WebSockets
  • Redis
  • MongoDB
  • React
  • Flutter
  • React Native

Aashroy

Mapping homelessness so help can reach people.

Anyone can mark someone who needs shelter on an interactive map, report crimes and offer donations, while the data shows where help is needed most. It works as an installable web app.

Built in 2021

Built with

  • React
  • Tailwind CSS
  • Mapbox
  • Recharts
  • PWA
  • Node.js
  • Express
  • MongoDB
  • Google sign-in
  • Cloudinary

LLM from scratch

A GPT-style language model, built from the ground up.

I built a GPT-style model from scratch in PyTorch, with about 110M parameters, and trained it on Google Colab. Then I trained several specialised versions, including one that writes poems and one that writes like a particular author.

Built and trained, 2022–2023

Built with

  • Python
  • PyTorch
  • GPT-style transformer
  • Self-attention
  • Model training
  • Google Colab
~110M parametersHow the model is put together
  1. Token + position embeddings
  2. Masked self-attention
  3. Feed-forward layers
  4. Next-token prediction

Specialised versions, including

  • Poem writer
  • Author-style writer

Rup

A programming language of my own.

Rup is a small scripting language with its own interpreter, complete with classes, inheritance and closures. I then made it mine with built-in lists, maps and HTTP, a VS Code extension, and error messages with a sense of humour.

With thanks to Crafting Interpreters by Robert Nystrom, the wonderful book that taught me how languages work and guided me as I built this.

Working interpreter, built in 2025

Built with

  • Java
  • Recursive descent parsing
  • Tree-walking interpreter
  • Static resolver
  • TypeScript
  • VS Code extension
class Vehicle {  move() {    show "The vehicle is on the move!\n";  }}class Car inherits Vehicle {  move() {    super.move();    show "Vroom vroom!\n";  }}take myCar = Car();myCar.move();

Output

The vehicle is on the move!
Vroom vroom!

Craft

What I make. 

Apps

Mobile apps that feel at home on your phone, down to push notifications and live calls.

  • Flutter
  • Dart
  • React Native
  • Expo
  • Firebase

Web products

Complete products, from the interface through the API to the data model.

  • Next.js
  • React
  • TypeScript
  • FastAPI
  • PostgreSQL
  • Prisma

AI tools

Agents, voice and retrieval that do real work for people, not just demos.

  • LLM agents
  • MCP servers
  • Voice
  • pgvector
  • Whisper
  • PyTorch

Data systems

Batch and real-time pipelines that handle enormous volumes without fuss.

  • Snowflake
  • PySpark
  • Databricks
  • Iceberg
  • Kafka

Infrastructure

Owning what happens after launch: deployment, observability and cost.

  • AWS
  • GCP Cloud Run
  • Docker
  • Kubernetes
  • CI/CD

At my day job

The same instinct, at enterprise scale. 

GenAI, at DISH

AI invoice and purchase order platform

Finance work was stuck in slow, manual processes. I led the build of a platform that takes it on, with an LLM agent on an MCP architecture, multi-level approvals and real-time analytics.

How AI invoice and purchase order platform fits together, step by step
  1. React

    Finance interface

  2. FastAPI

    Application services

  3. LLM agent

    MCP architecture

  4. AWS Bedrock

    Model access

  5. PostgreSQL

    Records, approvals

Cut manual processing by ~90% and earned the CFO AI Innovation Award.

Data platform, at DISH

Telecom analytics platform

Raw call records are no use to anyone as they arrive. One path turns high-volume call records into usage, call-quality and network-performance dashboards that teams can act on. A second path runs anomaly detection ML jobs on the data and raises alerts.

Analytics
  1. CDR sources

    Diverse systems

  2. Kafka

    Event streaming

  3. PySpark

    Databricks, Dataiku

  4. Iceberg

    S3 data lake

  5. Snowflake

    Analytics

  6. Elasticsearch

    Kibana dashboards

Anomaly detection
  1. Kafka

    Event streaming

  2. Python

    Dataiku

  3. Elasticsearch

    Kibana dashboards

  4. ML jobs

    Anomaly detection

  5. Alerts

    Notify the team

Reusable components and automated validation improved data availability for analytics teams.

Research, IEEE 2023

A warning system for roads shared with animals

An advanced warning mechanism, powered by renewable energy, designed to reduce conflict between people and animals on the road.

“Advanced warning mechanism to reduce man-animal conflict on roads using renewable energy,” IEEE, March 2023.

Alongside it: an animal-detection camera

A Raspberry Pi camera runs a TensorFlow Lite model to spot deer, cows and elephants, with motion detection and a live stream. A second version streams video from an ESP32 camera.

Built with

  • Python
  • TensorFlow Lite
  • Raspberry Pi
  • ESP32-CAM
  • Computer vision
  • Node.js

Recognition

  • CFO AI Innovation Award

    DISH India. For leading a GenAI platform that automated manual finance workflows.

  • WCA Innovation Award

    For technology-led process improvements and rapid GenAI innovation.

  • Dish-way Champion Award

    For technology-led process improvements and rapid GenAI innovation.

  • Winner, D'Spark Hackathon

    For technology-led process improvements and rapid GenAI innovation.

Toolbox

Whatever the idea needs. 

Apps & web

Back end

AI & ML

Data

Languages

Hardware & IoT

Storage

Cloud & delivery

Journey

How I got here. 

Since Aug 2023

The day job

Data and AI engineering, DISH Network Technologies, Bangalore

Where I keep sharpening the craft: batch and real-time platforms on high-volume telecom data, plus LLM agents and MCP servers on AWS Bedrock.

2019 – 2023

Where it started

B.Tech, Computer Science & Engineering, Jorhat Engineering College, Assam

Four years of computer science that ended with a published IEEE paper on keeping people and animals safe on the road.