Karmpath

Overview

Karmpath is a scalable blogging platform with AI-powered content recommendations. Built to handle real traffic at scale, it uses collaborative filtering and content-based algorithms to surface relevant posts to readers.

The Challenge

Most blogging platforms either can't handle traffic spikes without expensive infrastructure, or they don't leverage AI for content discovery. I wanted to build something that does both — a platform that stays fast under load and gets smarter the more people use it.

Architecture

Before vs After

My Approach

I designed Karmpath as a microservices architecture from the start, knowing that different components would need to scale independently:

Content Service handles CRUD operations for blog posts, full-text search, and content delivery. It sits behind Redis caching for the most frequently accessed posts.

Recommendation Engine runs the AI/ML models — collaborative filtering based on user reading patterns and content-based matching using post embeddings. It operates asynchronously, pre-computing recommendations during off-peak hours.

Auth Service manages JWT-based authentication with refresh token rotation, session management, and rate limiting.

CDN Layer serves static assets, images, and cached HTML from the edge, keeping Time to First Byte under 100ms for most users.

The entire system deploys on Kubernetes with autoscaling policies — when traffic spikes, pods scale horizontally without manual intervention. CI/CD runs through GitHub Actions: lint, test, build, and deploy to the K8s cluster on every merge to main.

Key Decisions

Why Kubernetes over serverless? The recommendation engine needs persistent connections to the database and sustained compute for model inference. Serverless cold starts would have killed the user experience.

Why Redis for caching? Blog content is read-heavy. A single popular post might get thousands of reads but only one write. Redis brings hot content response times from ~200ms (database) to ~5ms (cache).

Why microservices for a blogging platform? Honestly, it would have been simpler as a monolith. But I built it this way intentionally — to prove I could architect distributed systems that actually work in production. The recommendation engine scaling independently from the content service has been the biggest win.

Results

The platform handles 10,000+ concurrent users with 99.9% uptime. Redis caching and edge optimization delivered a 60% improvement in load times. Sentry monitoring catches issues before users notice them, and the automated CI/CD pipeline means every merge to main is in production within minutes.