✨ Inspiration People read a lot online, but understanding content often requires extra searching and background knowledge. We noticed how frustrating it is to leave a page just to understand what you’re reading. ContextIQ was inspired by the need to bring clarity directly into the reading experience. 🔍 What it does ContextIQ allows users to highlight any text on a webpage and instantly receive a simple explanation, background context, and a basic bias indicator — without leaving the page. 🛠️ How we built it ContextIQ was built as a Chrome Extension with a lightweight popup UI. A Node.js and Express backend processes the selected text and returns structured explanations using an AI-ready modular design. 🚧 Challenges we ran into Working solo with limited time and no live AI API access was challenging. Designing a smooth user experience while keeping the system simple required careful prioritization. 🏆 Accomplishments that we're proud of Building a complete, working end-to-end product solo and delivering a clear, impactful demo that solves a real problem. 📘 What we learned We learned how important focus, architecture planning, and user experience are in hackathon projects. 🚀 What's next for ContextIQ Integrating Google Gemini for deeper reasoning, adding credibility scoring, and personalizing explanations for different user levels.

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Future Roadmap: Context IQ ​To take Context IQ to the next level, I plan to integrate the following high-impact features: ​Multimodal Vision: Leveraging Gemini to analyze and explain on-screen images, diagrams, and charts in real-time. ​Semantic Memory: Enabling the AI to remember past queries to provide personalized, long-term learning support. ​Cross-Platform Expansion: Developing dedicated browser extensions and mobile overlays for a seamless experience across all devices. ​Voice Interactivity: Adding hands-free voice commands to assist users without interrupting their workflow.

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ContextIQ is officially LIVE!

We just finalized the backend integration with Google's Gemini API. The biggest challenge was handling model rate limits, but we implemented a custom "Auto-Switch" logic in Node.js that automatically finds the best available model (switching between Gemini 1.5 Flash and Experimental versions) to keep the extension running smoothly.

The extension now successfully analyzes text for Bias, Context, and Simplicity in real-time. Can't wait for you to see the demo!

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