Saptarshi • Portfolio
Thoughts on Systems, AI, and Engineering —
written from the intersection of code and curiosity.
How I built a machine learning pipeline to predict VOCs, O₃, and NOx concentrations in urban environments — from raw sensor data to real-time forecasts.
A deep dive into the design decisions behind building scalable full-stack applications — from database schema design to REST API contracts and frontend state management.
Practical lessons from setting up automated deployment pipelines — containerizing apps with Docker, writing CI/CD workflows, and shipping without breaking production.
A pragmatic guide to the 12 DSA patterns — sliding window, two pointers, monotonic stacks, and more — with real implementations and when to apply each one.
The gap between a working ML model and a production AI system is enormous. Here's how to think about inference pipelines, latency, model versioning, and serving at scale.
What I learned shipping Flutter apps to production — clean architecture, state management with Riverpod, and how to structure code that survives a growing team.
Writing about systems programming, AI deployment strategies, and engineering craft. New posts ship regularly.
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