# How I Built an AI-Powered Literature Review Tool for Thesis Students

From scraping 3 academic databases to AI summaries — a solo build story The Problem That Started It All Every thesis student knows the pain. You sit down with a research topic, open Google Scholar, and spend the next 3-4 hours manually: Searching across Google Scholar, Scopus, and Semantic Scholar separately Downloading papers one by one Copy-pasting metadata into a spreadsheet Repeating this every time your advisor asks for "more references" I was doing exactly this for my own thesis when I thought — this entire workflow is automatable. So I built LitAssist: a full-stack web app that scrapes journals from 3 sources, processes them through a Python pipeline, and generates AI literature reviews using Gemini. Here's everything I learned building it. Tech Stack Overview Frontend: Alpine.js + Tailwind CSS (MPA, no build framework) Backend: Node.js + Express 5 + Socket.IO Database: MongoDB + Mongoose Scraping: Puppeteer (Google Scholar) + Semantic Scholar API AI: Google Gemini 2.5 Flash Inf