SiloFocus: A Zero-Cost, Local AI Study Companion Built for My Sister

Surendra Tripathi — Sun, 04 Oct 2026 20:12:15 +0000

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

My younger sister is currently in the trenches preparing for her 12th-grade board examinations. If you've been there, you know the cycle, and the everyday monotonicity that it carries: stare at a brutal Calculus problem, get frustrated, switch tabs to 'take a quick break', and suddenly lose 45 minutes to scrolling.

I wanted to build her a focus environment that actually understands her stress rather than just aggressively ticking down a timer.

Enter 'SiloFocus'. It's a hyper-customizable, privacy-first study dashboard that blends a fluid and kinetic Pomodoro timer with a local AI tutor or an older brother/mentor. It does three main things:

The Timepiece: A distraction-free, beautifully animated study clock with procedural, zero-asset audio (grandfather clock ticks & singing bowl chimes generated directly in the browser).
The Socratic Tutor: A slide-out sidebar where she can dump a physics or a math problem and get instantm step-by-step doubt clarification formatted in clean LaTeX.
The Procrastination Interceptor: If the app detects she's abandoned her tab for too long during a study block, it pauses the timer and generates a humorous, personalized reality-check based on her recent study history to gently nudge her back into flow.

Demo

Live Web Dashboard (UI/UX Preview): https://silofocus.onrender.com/

Note on the Live Demo: The Render link showcases the kinetic UI, audio engine, and task workflows. However, to stay true to my core philosophy of "absolute data privacy" and "zero API costs" for students, the Gemma 2 inference engine and Mastra agents do not run in the cloud. They are hard-routed to localhost to run exclusively on the user's local GPU. To experience the AI tutor, clone the repo and spin it up locally!

Code

GitHub Repo Link: https://github.com/Surendra-T/SiloFocus

How I Built It

My current area of interest as well as my curiosity pushed me to prove that you don't need expensive cloud APIs to build elite, multimodal AI tools.
The entire brain of this app runs locally on my laptop's RTX 4070.

The Local AI Engine: I used Ollama to run Google's Gemma 2 9B. It handles all the heavy lifting; explaining thermodynamics, formatting KaTeX math equations, & generating motivational nudges.
The Orchestrator: I used Mastra framework to handle the complex state machine.
Mastra orchestrates two agents: a studyAgent for academic doubts & a nudgeAgent that intercepts procrastination.
The Memory: I integrated MongoDB Atlas to track her daily mood and productivity scores. When she loses focus, the nudgeAgent queries Atlas to ground its motivation in reality (eg: "Hey, you felt like a 4/10 last Tuesday but still crushed 50 minutes of Chemistry. Close the tab. You've got this.")
The Voice: To reduce screen & typing fatigue as well as the time consumed in typing, she can use voice dictation to ask questions. I hooked up the ElevenLabs API to stream Gemma's answers back in crystal-clear audio. (I also built a graceful fallback to native browser TTS so the project remains 100% open-source friendly if someone doesn't have an API key).
The Telemetry: I wrapped the local inference routes in Sentry tracing. It was incredibly satisfying to watch the Sentry dashboard prove that my local Gemma 2 setup was hitting sub-second latency with zero cloud costs.
The Frontend UI: Built with Next.js (App Router), styled with Tailwind, and deployed on **Render. I spent way too much time tweaking & playing around with CSS spring physics (tabular-nums and translate3d) to make the clock digits roll smoothly at 60fps.

Why Does Open Innovation Matter?

High school students are stressed, and frankly, they are broke. The idea that a student needs to pay a 200-300 rupees or $20/month subscription just to keep the ads off and have an AI explain a math concept is wild to me.

Open-Weight models like the Gemma family, change the game. By running the inference entirely on local hardware, SiloFocus operates at zero-runtime cost.

More important, it guarantees absolute data privacy. A student's mood, their struggles with a subject, and their study habits are deeply personal. With a closed API, that data gets shipped off to a corporate server to train future models and to run targeted ad-campaigns.
With SiloFocus and open-source local inference, her data stays exactly where it belongs: in her own room, on her own system.

Prize Categories

I am officially throwing SiloFocus into the ring for the following Hacktoberfest partner categories:

Weekend Challenge: Build for a Friend (The core inspiration for the project)
Google/Gemma (Best open-weight AI application using local Gemma 2 9B)
Mastra (Best agenting workflow and state orchestration)
ElevenLabs (Best Voice/Audio AI integration)
MongoDB Atlas (Best AI Memory & Data Architecture)
Sentry (Best AI Observability and local LLM latency tracing)
Render (Best deployment)