Engineering Notes & Open-Source Work

Public engineering notes derived directly from active repositories and projects.

Contribution Feed

rohitraj.tech Redesign

open source

backendscale·2026-01-31

Complete redesign from consultancy landing page to engineer-centric project directory with documentation-first aesthetic.

→ Live at rohitraj.tech

src/appsrc/componentssrc/data
GitHub →

MicroItinerary — AI Travel Planner

ai systems

MicroItinerary·2026-01-27

AI-powered PWA for planning annual travel itineraries. Features intelligent destination suggestions via OpenAI GPT-4, cost estimation in INR, and Splitwise-style expense splitting for group trips.

→ Full-stack PWA with offline support, Google OAuth, Redis caching

backendwebdocker-compose.yml
GitHub →

StellarMIND — Chat-to-SQL with pgvector

ai systems

spring-ai-mcp-server·2026-01-25

Spring Boot MCP server that converts natural language questions into read-only SQL using LLM. Retrieves schema context via pgvector for RAG-based query generation.

→ Natural language → SQL with safe read-only execution

stellarmind-serverstellarmind-client
GitHub →

AWS SQS Integration Patterns

open source

sqs·2021-12-27

Reference implementation for AWS SQS integration with Spring Boot including producer/consumer patterns, retry logic, and dead-letter queue handling.

producerconsumer
GitHub →

Repository Summary

backendscale
TypeScriptactive

Engineering work visibility — portfolios show results but not thinking

Full implementation: Next.js 16, React 19, documentation-first design system

src/appsrc/componentssrc/data
MicroItinerary
Javaactive

Travel apps lack intelligent suggestions and group expense management

Full-stack: Spring Boot 3.2.2 + Java 21 backend, React 18 + Vite PWA, OpenAI integration

backendwebtest
spring-ai-mcp-server
Javaactive

Natural language database queries with retrieval-augmented context via pgvector

Full MCP server implementation, pgvector integration, Chain-of-Thought UI

stellarmind-serverstellarmind-clientpostman
my-expense
Javamaintenance

Simple expense tracking without bloated features

Full-stack Java application with REST API

apipersistencereports
sqs
Javaarchived

Reference implementation for AWS SQS with Spring Boot

Producer/consumer patterns, retry logic, DLQ handling

producerconsumerconfig
wealwa-api
Javaarchived

Personal wealth tracking and portfolio management

REST API design, portfolio analytics

apiportfolioanalytics

View all repositories on GitHub →

Project Notes

backendscale (rohitraj.tech)

frontend

Why it exists

Engineering work is often invisible. Traditional portfolios show polished results but not the thinking process.

Core technical challenge

Creating a documentation-first design system that feels like internal engineering docs while remaining visually professional.

Architecture

  • Next.js 16 with App Router for static generation where possible
  • React 19 with server components for optimal performance
  • Tailwind v4 for utility-first styling
  • Data-driven content structure in src/data/ for easy updates

Trade-offs

  • Chose vanilla CSS over component libraries for full control, at cost of development speed
  • Static generation means content updates require redeployment
  • Minimal dependencies reduce maintenance burden but limit feature velocity
Production — deployed on VercelView Repository →

MicroItinerary

ai systems

Why it exists

Travel apps lack intelligent suggestions based on season/budget and don't handle group expense splitting well.

Core technical challenge

Integrating OpenAI GPT-4 for destination suggestions and cost estimation while maintaining offline-first PWA capabilities.

Architecture

  • React 18 + Vite PWA frontend with IndexedDB for offline support
  • Spring Boot 3.2.2 + Java 21 backend with Flyway migrations
  • PostgreSQL 16 + Redis caching layer
  • Google OAuth 2.0 + JWT authentication
  • OpenAI GPT-4 API for AI features

Trade-offs

  • PWA over native app — broader reach but limited device APIs
  • OpenAI API adds latency and cost vs simpler rule-based suggestions
  • Expense splitting algorithm prioritizes simplicity over Splitwise feature-parity
Development — backend and frontend functional, needs polishView Repository →

StellarMIND (spring-ai-mcp-server)

ai systems

Why it exists

Business users need to query databases without knowing SQL. Existing tools lack context-aware query generation.

Core technical challenge

Retrieval-augmented SQL generation using pgvector for schema context, with strict read-only query enforcement for safety.

Architecture

  • Spring Boot MCP server with Tool interface for query execution
  • pgvector for storing schema knowledge chunks and embeddings
  • Spring AI for LLM integration (provider-agnostic)
  • Chain-of-Thought (CoT) web interface for query debugging
  • Read-only SQL enforcement (only SELECT, WITH allowed)

Trade-offs

  • Read-only restriction limits use cases but ensures safety
  • pgvector requires PostgreSQL — not database-agnostic
  • MCP transport (stdio) limits deployment patterns vs HTTP
Development — core query flow working, needs UI polishView Repository →