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Mindweb Technologies- I worked as a part-time fullstack Developer in here and worked on various issues that they were facing, I optimised the LMS of the site which end up giving 2.2x better result in long term as well as I worked on the overall UI optimisation making the 1.7x faster to load and fine tuned the animation and some design aspects.
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My client’s website before and after the redesign 👇
Which version feels more welcoming and trustworthy for someone looking for therapy?
For Ri Shuja, an online psychotherapist and hypnotherapist, I redesigned the site to create a calmer first impression, make her approach clearer, and help visitors take the next step with confidence.
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9 voted
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The After design is so much better. The old version felt like reading a long essay. The new one feels calm, friendly and very easy on the eyes.
🚀 Introducing WanderLust — My Full-Stack Web Development Project
I built WanderLust, a full-stack booking web application using Node.js, Express.js, MongoDB, and EJS.
✨ Key features:
User authentication and authorization
Create, edit, and manage property listings
Reviews and booking functionality
Image uploads using Cloudinary
Interactive maps with Mapbox
Search and category filters
🛠️ Tech Stack: JavaScript, Node.js, Express.js, MongoDB, Mongoose, EJS, Passport.js, Cloudinary, and Mapbox.
This project helped me strengthen my backend development, database integration, authentication, and deployment skills.
I'm a junior full-stack developer interested in building useful web applications and collaborating on real-world projects.
🔗 Live Demo: https://wanderlust-tphz.onrender.com
💻 GitHub: https://github.com/Aabi-dev10/WanderLust
#FullStackDevelopment #MERNStack #JavaScript #WebDevelopment #NodeJS
did you build the reviews and booking flow first?
𝐀𝐈 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 & 𝐃𝐚𝐭𝐚 𝐐𝐮𝐞𝐫𝐲 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦
I built an AI-powered research intelligence platform that helps analysts and business teams query complex datasets through a single conversational interface.
𝐓𝐡𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦
Research teams often work across disconnected databases, reports, spreadsheets, dashboards, and documents. This slows analysis, makes cross-source searching difficult, and can reduce trust in AI-generated answers.
𝐓𝐡𝐞 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧
The platform combines RAG, AI agents, natural-language-to-SQL, structured and unstructured retrieval, source attribution, confidence scoring, and evaluation pipelines.
Users can ask a business question in natural language, and the system decides whether to search documents, query databases, run SQL, or combine multiple sources before returning an evidence-backed answer.
𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲 & 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞
The system evaluates answer correctness, retrieval quality, SQL accuracy, source relevance, hallucination risk, and response performance. Low-confidence results are captured for review and continuous improvement.
The frontend is built with React + TypeScript and includes AI chat, dataset exploration, source references, analytics, evaluation dashboards, and real-time updates.
𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐕𝐚𝐥𝐮𝐞
The platform helps teams analyze data faster, reduce manual SQL work, improve trust in AI outputs, and uncover insights across multiple data sources.
𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤: 𝐑𝐞𝐚𝐜𝐭 • 𝐓𝐲𝐩𝐞𝐒𝐜𝐫𝐢𝐩𝐭 • 𝐏𝐲𝐭𝐡𝐨𝐧 • 𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐒𝐐𝐋 • 𝐑𝐀𝐆 • 𝐕𝐞𝐜𝐭𝐨𝐫 𝐒𝐞𝐚𝐫𝐜𝐡 • 𝐋𝐋𝐌 𝐀𝐠𝐞𝐧𝐭𝐬 • 𝐒𝐐𝐋 • 𝐀𝐏𝐈𝐬 • 𝐄𝐯𝐚𝐥 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬
