After months of late-night coding, debugging, and wrestling with compiler errors, I finally reached a major milestone: I published my first-ever desktop application to the Microsoft Store. 🎉
The app is called Focus Stream. It tracks how you spend time on your PC and turns it into an AI-generated focus journal. The catch? It runs fully on-device. No accounts, no cloud database, and absolutely no telemetry. Window snapshots, activity timelines, and LLM inference all happen locally.
In this post, I want to share the architecture behind Focus Stream, how I fit a local Large Language Model (LLM) into a lightweight desktop app, and what it took to package and ship it to the Microsoft Store.
The Core Concept: Privacy-First AI
We've all seen the news about cloud-based activity trackers scanning user screens. While productivity tracking is incredibly useful, uploading your screen snapshots, open documents, and active window titles to a third-party server feels like a major privacy risk.
