Case Study

Production

Run App — Fitness Platform

My first project at DRCODE (as an intern), built ground-up: a microservice fitness platform with Strava/Garmin sync, a Flask ML pace predictor, and Gemini-powered run analysis.

Run App — Fitness Platform

Stack

Technologies

AstroReactNestJSNode.jsPythonFlaskPostgreSQLPrismaRedisAWS EC2

Context

Tags

backendaifull-stack

Overview

A running/fitness platform that generates phased workout plans, tracks activities, and gives AI feedback on runs. I built it from the ground up as an intern.

It syncs real data from Strava and Garmin (full OAuth 2.0), predicts training paces with a scikit-learn model, and analyzes runs lap-by-lap with a Google Gemini agent that sends summaries over WhatsApp.

Features

Architecture

  • Astro/React frontend with 5 NestJS microservices (API + Strava/Garmin connectors) and a Python/Flask ML service.
  • 3 PostgreSQL databases (~20 Prisma models), Redis + BullMQ queues, deployed to AWS EC2 via GitHub Actions.
  • Exposed as an MCP server so an AI agent can drive the app.