This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend
What I Built
I built StudyPulse AI for my 3rd-semester computer science classmate and friend, Aarav.
Like many engineering students, Aarav is constantly buried under dense lecture notes in subjects like Operating Systems (semaphores, Coffman deadlock conditions, CPU scheduling), Data Structures & Algorithms (AVL tree rotations, Red-Black balance invariants), and Database Management Systems (ACID properties, BCNF normalization).
Whenever exams and midterms approach, Aarav struggles with:
- Passive re-reading: Highlighting slides over and over without active recall or self-testing.
- Expensive API barriers: Not wanting to pay $20/month for proprietary cloud AI subscriptions just to summarize homework notes.
- Spotty campus internet: Living in a university dorm where Wi-Fi drops constantly during study sessions.
- Data privacy: Not wanting course material, professor slide drafts, and private notes uploaded to corporate cloud silos. StudyPulse AI is a lightweight, private, offline-first study companion designed to run directly with local open-weight models (such as Googleβs Gemma 2, Llama 3.2, or Mistral via Ollama/LocalAI). It takes raw lecture notes or textbook snippets and instantly converts them into:
- ποΈ Active-Recall 3D Flip Flashcards with intuitive everyday analogies
- π― Interactive Exam Practice Quizzes with instant feedback and grading
- π‘ ELI5 (Explain Like I'm 5) breakdowns
- π₯ One-click Anki TSV and Markdown exports for long-term spaced repetition ### What My Friend Thought (Real User Feedback) I shared the working build with Aarav over the weekend while revising Operating Systems. Here is his feedback: > "The fact that this runs completely offline on my laptop without requiring an internet connection or an API subscription is a lifesaver for studying in the library basement where our campus Wi-Fi always disconnects. > > The killer feature for me is the everyday analogies on the back of the flashcards. I kept confusing the four Coffman deadlock conditions during revision, and the 4-way stop-sign intersection analogy made it immediately click. Also, being able to export the whole deck into Anki in one click saves me hours."
Demo
Code
β‘ StudyPulse AI
Private Open-Weight Note-to-Flashcard & Exam Prep Assistant
Built for a classmate for the Hacktoberfest 2026 Weekend Challenge: "Build for a Friend"
π Overview
StudyPulse AI is an offline-first, privacy-respecting study companion that transforms dense semester lecture notes, textbook passages, and slides into active-recall flashcards, practice quizzes, intuitive everyday analogies, and Anki-ready decks using local open-weight AI models.
Built specifically for engineering and CS students tackling tough 3rd-semester subjects like Operating Systems, Data Structures & Algorithms, and Database Management Systems.
β¨ Features
- ποΈ 3D Active-Recall Flashcards: Spatial card flips with keyboard shortcuts (
Spaceto flip,Arrowkeys to navigate) and mastery tracking. - π― Exam Practice Quiz: Auto-generated multiple-choice questions with instant rationale and scoring.
- π‘ ELI5 & Intuitive Analogies: Translates complex system concepts into relatable mental models.
- π 100% Offline & Private: Connects locally to Ollama (
gemma2:2b,llama3.2:3b,mistral) or any localβ¦
How I Built It
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Prompt Engineering for Open-Weight Models:
Open-weight models likegemma2:2borllama3.2:3bare efficient and run smoothly on modest student laptops. I structured strict JSON-schema system prompts requiring the model to extract:- A core question testing causal understanding (not simple rote memorization)
- A concise, high-yield exam answer
- A relatable, real-world everyday analogy (e.g., explaining Coffman deadlock conditions like a 4-way stop sign intersection, or ACID atomicity like an ATM cash withdrawal).
Resilient Local Architecture:
The application communicates withhttp://localhost:11434/api/generatevia browserfetchwith configurable model and endpoint options. If a student is on the go and hasn't started their local Ollama daemon, StudyPulse seamlessly falls back to its built-in heuristic semantic parser, guaranteeing 100% uptime.Active Recall & Spaced Repetition Design:
Instead of overwhelming the student with giant walls of text, StudyPulse focuses on atomic cards, self-evaluations ("Mastered" vs "Needs Review"), and Anki export compatibility.
Why Does Open Innovation Matter?
- Zero Cost for Students: College students shouldn't have to budget monthly subscriptions just to generate study flashcards from their own notes. Open-weight models like Gemma and Llama bring state-of-the-art reasoning to everyday personal computers for free.
- Complete Privacy: Course notes, university research, and student drafts never leave the student's device. No telemetry, no cloud training on user notes, and zero data leakage.
- True Offline Independence: Open-source AI isn't dependent on cloud server status pages, rate limits, or campus Wi-Fi connection drops. It runs reliably on an airplane, in a library basement, or in a dorm room.
- Customizability: Because the models are open, students can fine-tune small open-weight models on their own department's curriculum or switch models freely without vendor lock-in.
My Agent Session
This project was developed with the assistance of Google Antigravity and configured with DevRelay for Hacktoberfest 2026. The agent workflow helped scaffold the semantic extraction prompts, structure the 3D CSS flip animations, and curate high-yield 3rd-semester computer science concepts.
Prize Categories
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Best Use of Gemma: StudyPulse AI is built to run directly with Google's Gemma 2 open-weight model (
gemma2:2b) locally on modest student laptops via Ollama/local inference, generating active-recall flashcards, practice quizzes, and intuitive analogies 100% offline with zero subscription cost.
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