Clarity.ai — AI Academic Study Notes Compiler | First Commit Hackathon Demo

Clarity.ai is an AI-powered academic study-notes compiler built during the First Commit Hackathon. It can take: An academic topic A study PDF Or both together and generate structured, exam-focused notes with: Executive Summary Key Definitions / Concepts Core Equations, Laws, or Principles Step-by-Step Derivation / Analysis Worked Example Exam Pitfalls Subject-aware presentation Custom Focus Deep Dive mode LaTeX + MathJax mathematical rendering PDF export How it works Clarity.ai is built with Python and Flask, with the Gemini API used to generate the academic content. Pydantic is used to structure and validate the generated response, Jinja is used for dynamic HTML rendering, MathJax handles mathematical notation, and Playwright is used for PDF generation. Demo In this video, I demonstrate Clarity.ai using a Physics topic and show the note-generation workflow, mathematical rendering, study-material support, and PDF export. AI Disclosure AI tools were used during development as learning and coding aids for brainstorming, understanding unfamiliar concepts, debugging, UI improvements, and final refinement of the HTML, CSS, and Python code. The Gemini API is also a core part of Clarity.ai and is used to generate the academic study notes. Project GitHub: https://github.com/akshobhya-dev/Clarity.ai Built for the First Commit Hackathon.