Projects

The projects I have developed from the past 2 year so far are included in this section, along with a concise overview of my work within these projects.

👉 FlowCD: Github

  • FlowCD is a lightweight GitOps controller for Kubernetes inspired by Argo CD.
  • Features:
    • Quick sync with Git repo.
    • Automatically syncs manifests from your Git repository.
    • Deploy to local Kubernetes cluster and apply synced manifests to keep the cluster in sync with Git.
  • Status: In active development — architecture and design decisions are ongoing.

👉 thanos-mcp-server: Site

  • A custom Thanos MCP server that provides AI agents a single endpoint to query and analyze globally aggregated, long-term Prometheus metrics.
  • Thanos does not have thier own mcp server so i built my own.
  • Features:
    • PromQL query tool: execute PromQL queries against Thanos/Prometheus endpoints.
    • MCP integration: works with MCP-compatible clients like Cursor.
    • Global metrics access: query aggregated metrics across your monitoring infrastructure.
  • Warning: NOT READY FOR PRODUCTION — experimental/development project.

👉 PromptSafely: Github

  • PromptSafely is a safety proxy that sits in front of LLM APIs (like OpenAI or Ollama) to make prompts and responses safe, policy-driven, and observable without requiring app developers to reimplement safety features.
  • Implemented prod ready GitOps CI/CD with PipeCD.
  • Currently only implemented for dev/stage env.

👉 federated-k8s-demo: Github

  • College project implementing federated learning across multiple K3s clusters using Flower and TensorFlow.
  • Privacy-preserving: differential privacy (TensorFlow Privacy), homomorphic encryption (TenSEAL), HTTPS/TLS, and OAuth2/JWT.
  • Uses FedAdam/FedProx strategies, model compression (mixed precision, TF Lite quantization), and Kubernetes Jobs for on-demand training.
  • Monitoring with Prometheus + Grafana. Focus: chest X‑ray classification for privacy-sensitive medical training.

👉 SkinSprite: Github

  • Built an AI-driven Personal Skin Health Assistant for diagnosing skin diseases. Users can upload images and receive responses along with details of nearby dermatologists.
  • Used the VGG19 Convolutional Neural Network architecture for accurate disease identification.
  • Developed the app with Java and Spring Boot, and the website with React.js, Node.js, and MongoDB. Used ngrok for tunneling to the ML model API.

👉 FlavorFleet: Github

  • Developed FlavorFleet, a food order site with Node.js and MongoDB.
  • Containerized the application with Docker and pushed it to Docker Hub.
  • Deployed the site on Kubernetes for seamless deployment.

👉 CI-CD-Pipeline: Github

  • Created a simple Golang application with a basic web server.
  • Dockerized the application and tested it locally.
  • Set up a CI/CD pipeline using Jenkins, including Docker for building and managing Docker images.

👉 microservices-backend: Github

  • Developed three microservices (User Service, Restaurant Service, and Delivery Agent Service) for a food delivery app using Node.js and PostgreSQL.
  • Containerized each microservice with Docker and set up Docker Compose for streamlined multi-container management.