StudyPulse AI: An Offline-First Open-Weight Study Companion Built for my Classmate
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…
StudyPulse AI is an offline-first open-weight study companion developed by the author for their third-semester computer science classmate, Aarav. The app tackles common study struggles such as passive re-reading, expensive API barriers, spotty internet connectivity, and data privacy concerns. It utilizes local open-weight AI models like Google's Gemma 2, Llama 3.2, or Mistral via Ollama to transform dense lecture notes and slides into active-recall flashcards, practice quizzes, ELI5 breakdowns, and Anki-ready decks.
Key features include 3D active-recall flashcards, interactive exam quizzes, intuitive analogies, and one-click Anki exports. The app runs completely offline, ensuring privacy and eliminating the need for costly subscriptions. The tech stack consists of Semantic HTML5, Vanilla CSS3, Vanilla JavaScript, and integration with Ollama for AI processing.
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