This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady What I Built:
Every student and self-taught developer suffers from the same bad habit: hoarding hundreds of messy screenshots of lecture slides, textbook diagrams, code snippets, and whiteboard formulas with the vague promise of "I'll study this later."
Over time, this backlog morphs into crushing "study debt"—an overwhelming pile of visual clutter that induces guilt, decision paralysis, and procrastination. Facing a 4-hour marathon cramming session feels exhausting, so we do nothing.
StudyDebt is a liquid-glass study recovery web application designed in the true spirit of International Sloth Day: it rewards your laziness by turning academic chaos into steady, low-effort micro-wins.
Instead of demanding hours of tedious note-taking, StudyDebt automates the friction:
"Have 10 Minutes?": You don't have to organize anything. Drag and drop your raw screenshots, and open-weight models distill them into a plain-English gist, core concepts, and a 10-minute micro-lesson with an interactive knowledge check.
"Your Next Small Win": Eliminates decision fatigue entirely. You never have to figure out what to study; a recommendation engine analyzes your energy level (Low, Medium, High) and available time (5m, 10m, 15m, 25m) to serve up the single smallest, highest-leverage task.
"Rebalance My Week": Took a 3-hour sloth nap or skipped your Tuesday study goal? StudyDebt never shames you or breaks your schedule. One click redistributes your pending tasks across the remaining days without punishing you or destroying completed work.
Demo
🌐 Live Web Application: [https://trideep091.github.io/studydebt/](https://trideep091.github.io/studydebt/)
💻 Open-Source Repository: [https://github.com/Trideep091/studydebt](https://github.com/Trideep091/studydebt)
Key User Flows in Action:
The Sloth Dashboard: Deep midnight navy aesthetic (#080B18) with frosted glass depth (backdrop-filter: blur(20px)), real-time study debt hour counter, and immediate 10-minute sprint recommendations. Screenshot Vault: Drop study slides in PNG, JPG, or WebP format. Open-weight vision & OCR pipelines extract equations and definitions into structured flashcard overviews.
Adaptive Focus Mode: Distraction-free countdown timer, interactive mid-session checkpoint questions, and confidence ratings that feed directly into a Leitner-style spaced repetition queue.
Natural Language Weekly Planner: Type "I need to review Java memory allocation, finish linear algebra, and practice 2 SQL queries before Friday", and the AI balances the workload across your daily capacity meters.
Code
You can find the full source code on GitHub:
Small sessions. Steady progress. Less academic chaos.
StudyDebt is a creative, premium, liquid-glass-themed study recovery application designed to rescue students from academic chaos. It transforms forgotten piles of educational screenshots, lecture slides, and unstructured to-do lists into manageable, bite-sized study sessions and deadline-aware weekly plans.
#080B18) and frosted glass surfaces with refined background depth. backdrop-filter: blur(20px) with saturation boosts, translucent glowing borders, and realistic reflective highlights.prefers-reduced-motion.
Project Architecture:
studydebt/
├── src/ # React 19 + TypeScript + Vite Frontend
│ ├── components/ # Liquid-glass UI components (Dashboard, Vault, Planner)
│ ├── services/
│ │ ├── ai/client.ts # Open-weight inference client & offline fallback engine
│ │ ├── db/indexedDb.ts # Local-first persistence via IndexedDB (idb)
│ │ └── scheduler/ # Spaced repetition, recommendation & rebalancing engines
│ └── types/index.ts # Strictly typed domain schemas
├── backend/ # FastAPI Microservice
│ ├── app/
│ │ ├── api/routes/ # AI & OCR endpoints (/summarize-screenshot, /plan-week)
│ │ └── services/ # Prompt templates & Ollama/vLLM integration
│ └── tests/test_api.py # 100% automated test coverage with pytest
├── public/ # Static assets (.nojekyll, 404.html SPA fallback)
├── .github/workflows/deploy.yml# Automated GitHub Actions deployment pipeline
└── docker-compose.yml # Containerized local development stack
How I Built It
StudyDebt is built around an open-weight, local-first AI stack:
Open-Weight AI Inference:
Primary local runner: Meta Llama 3.2 (3B) and Gemma 2 (9B) served via Ollama on local hardware ([http://localhost:11434](http://localhost:11434)).
Production runner: Decoupled FastAPI ASGI microservice with Pydantic structured output validation, compatible with self-hosted vLLM and open-weight inference providers (e.g. Together AI, Groq Llama 3.3).
Local-First Privacy Architecture: Built with IndexedDB (idb) for offline-first client storage. All student notes, screenshots, study logs, and spaced repetition intervals persist privately in the user's browser.
The frontend includes a built-in client-side fallback engine that generates deterministic schedules and educational heuristics even when completely disconnected from the network.
Frontend Craftsmanship:
React 19, TypeScript, and Vite 8 with zero heavyweight UI library dependencies.
Hand-crafted liquid-glass styling using pure modern CSS (subtle radial gradients, specular highlights, and high-contrast accessible typography via Google Fonts' Outfit and Plus Jakarta Sans).
DevOps & Continuous Deployment:
Automated GitHub Actions pipeline (.github/workflows/deploy.yml) deploying directly to GitHub Pages with least-privilege OIDC tokens, SPA 404 redirects, and Jekyll bypasses.
Why Does Open Innovation Matter?
Open innovation and open-weight models are not just nice-to-haves for StudyDebt—they are the entire reason the project is viable:
Academic Privacy & Data Sovereignty: Students frequently screenshot unreleased lecture slides, proprietary university problem sets, grading rubrics, and handwritten study notes. Up private classroom materials to closed, commercial AI APIs risks training leakage and violates academic privacy policies. With open-weight models (Llama 3.2 / Gemma 2) running locally through Ollama, zero bytes of private course material ever leave the student's laptop.
Ending the "Student Tax": Closed proprietary APIs charge per-token fees or require $20/month subscriptions that students cannot justify for organizing disorganized notes. Open-weight models running on consumer hardware (Mac Apple Silicon or standard GPUs) make intelligent study recovery 100% free forever.
True Offline Resilience: Studying happens in library basements with no cell reception, on trains, and during campus Wi-Fi outages. Because open models and IndexedDB run locally, StudyDebt works with zero internet connection, guaranteeing zero vendor lock-in.
My Agent Session
This project was built and debugged in pair programming collaboration with Antigravity (powered by Gemini 3.8 Flash).
The agent helped inspect the repository, implement the spaced repetition scheduling heuristics, configure the decoupled API client with offline graceful degradation, build the GitHub Actions Pages deployment pipeline, and configure the subpath asset resolution.
You can inspect the agent session transcript preserved via DevRelay:
The Lazy Payoff
Here is the cold, hard math on how much effort StudyDebt saves:
Tidying & Filing Time: Manually sorting 30 screenshots into folders, typing out summaries, and writing flashcards usually takes 45 to 60 minutes of tedious manual labor. With StudyDebt, dragging the screenshots into the vault takes 5 seconds, and open-weight models instantly distill the formulas and key takeaways.
Decision Paralysis: Students spend an average of 15 to 20 minutes every evening staring at their notes trying to decide what to study first. "Your Next Small Win" computes the ideal 10-minute task in under 300 milliseconds.
Guilt-Free Sloth Mode: When you skip a study day, traditional calendar apps leave you with an intimidating wall of overdue red notifications. Clicking "Rebalance My Week" calculates daily capacity ceilings and re-allocates pending tasks in 1 click, saving you from ever having to rewrite a planner.