[[["เข้าใจง่าย","easyToUnderstand","thumb-up"],["แก้ปัญหาของฉันได้","solvedMyProblem","thumb-up"],["อื่นๆ","otherUp","thumb-up"]],[["ไม่มีข้อมูลที่ฉันต้องการ","missingTheInformationINeed","thumb-down"],["ซับซ้อนเกินไป/มีหลายขั้นตอนมากเกินไป","tooComplicatedTooManySteps","thumb-down"],["ล้าสมัย","outOfDate","thumb-down"],["ปัญหาเกี่ยวกับการแปล","translationIssue","thumb-down"],["ตัวอย่าง/ปัญหาเกี่ยวกับโค้ด","samplesCodeIssue","thumb-down"],["อื่นๆ","otherDown","thumb-down"]],[],[],[],null,["# Xibon AI SuperBrain\n\n[More Apps](/competition/vote) \n\nXibon AI SuperBrain\n===================\n\nA truly personalised knowledge companion that is an extension of you \nVote \nVoted!\nWhat it does\n\nOur SuperBrain app is designed to be a knowledge companion catered to your needs and requirements that can absorb online information relevant to you. Xibon AI's SuperBrain's goal is to combat information overload, overcome failure to make cognitive connections between content we interact with online, redundant repeat research and a lack of a knowledge companion that understands us in great depth. Our app records a user's relevant online interactions (when the chrome extension and the Start Process Button on the home page are both switched on to activate the SuperBrain), summarises relevant online info from webpages, pdfs, YouTube videos, research articles etc. using the Gemini API. We store this info in a Graph Database where different relationships are formed for better contextual outputs (Vector search is performed on the Graph DB too) down the line. We then use the Gemini API for users to generate personalised documents (technical documentation, Study Guide, Project Plan, Podcast Scripts, YouTube Video scripts, PRDs, etc.) based on their specific context and info in their SuperBrain. The Gemini API is further used to generate Proactive Insights, which when the Proactive Agent button is switched on automatically suggests relevant insights to users based on info in their SuperBrain to help them in their current workflow (for example if you are reading a complex article then old info you have come across can help you understand the current findings better). \nBuilt with\n\n- Web/Chrome \nTeam \nBy\n\nXibon AI \nFrom\n\nAustralia \n[](/competition/vote)"]]