Building Mr.360-AI: Orchestrating Vision Intelligence for the Future of Sports 🏏🚀

Rahul Khati — Tue, 05 May 2026 10:34:50 +0000

The Vision
As the CEO of MindPulse AI and a Microsoft Imagine Cup 2026 Regional Finalist, I’ve always believed that data is the soul of modern sports. But standard apps only scratch the surface. With Mr.360-AI, we are moving beyond basic scoring into 360° Visual Intelligence.

The Problem Stack
In the world of cricket, we identified four critical gaps:

Analytical Blind Spots: Missing shot mechanics and ball trajectories.

Fan Isolation: Latency in shared match experiences.

Stadium Chaos: Fragmented gate and crowd management.

Quality Control: Zero transparency in stadium hospitality.

The Architecture (GSoC 2026 Focus)
I built Mr.360-AI to be a scalable, open-source-friendly ecosystem. Here’s how the engine works:

Frontend: React + Vite for lightning-fast performance and atomic design.

Backend & Auth: Firebase for real-time synchronization.

Intelligence: Custom Computer Vision models designed to extract biomechanical data from raw video frames.

Key Features & Innovation
Shot-Ball Correlation: Analyzing the physics of the game in real-time.

AI Commentary Sync: Using a specialized orchestration layer to broadcast live insights over Bluetooth/VoIP.

Smart Logistics: Real-time crowd-flow algorithms for stadium gate advisories.

Why I'm Open-Sourcing This Journey
My goal is to contribute this expertise to the Google Summer of Code (GSoC) 2026. I am looking to collaborate with global organizations that are pushing the limits of Computer Vision, IoT, and Real-time Analytics.

Call to Action (CTA)
I’m looking for feedback from the global dev community! How would you scale the edge-processing for live match analytics?

🔗 GitHub Repo: https://github.com/RAHULKHATI369/Mr.360-AI
🚀 Live Demo: https://mr-360-ai-858c4.web.app/
📺 Video Walkthrough: Demo Link

Let’s connect!
LinkedIn | Portfolio

showdev #ai #opensource #gsoc2026 #visionai #webdev #startup

Bridging the Gap Between Data & Empathy: Building Scalable Emotion AI Systems

Rahul Khati — Tue, 30 Dec 2025 15:43:59 +0000

The Vision
As a Tech Innovator and AI enthusiast pursuing my BCA at MDU Rohtak, I’ve always believed that data should do more than just sit in a database—it should understand us. While my experience in data analytics simulations at Deloitte and Goldman Sachs taught me how to handle large-scale enterprise data, I wanted to apply those rigorous standards to something more human: Mental Well-being.

This led to the creation of MindPulse AI.

The Problem: The Monitoring Gap
Traditional mental health tracking is often manual, subjective, and retrospective. People struggle to accurately label their emotional states in real-time, leading to undetected burnout and stress.

The Solution: Multimodal Fusion
MindPulse AI isn't just a sentiment analyzer. It's an intelligence engine that synchronizes:

Textual Analysis: Understanding context and intent using NLP.

Voice Analytics: Detecting stress through vocal patterns (Web Speech API).

Visual Cues: Analyzing facial micro-expressions for a 360-degree emotional view.

Technical Deep-Dive
Building this required a robust and scalable infrastructure:

The Brain: Integrated Google Gemini AI for complex reasoning and multimodal data fusion.

Frontend Mastery: Developed with React.js and Tailwind CSS, featuring a high-fidelity Claymorphic & Glassmorphic UI to reduce user anxiety.

Data Integrity: Applied statistical data analysis skills to ensure the accuracy of our 30+ mood state mappings.

Why It Matters
This project is a culmination of everything I’ve learned—from software engineering at Goldman Sachs to business visualization at Tata Group. It’s about building technology that doesn't just calculate, but empathizes.

Live Demo: https://mindpulse--ai.web.app/ GitHub: https://github.com/RAHULKHATI369/MindPulse-AI.git

DEV Community: Rahul Khati

Building Mr.360-AI: Orchestrating Vision Intelligence for the Future of Sports 🏏🚀

showdev #ai #opensource #gsoc2026 #visionai #webdev #startup

Bridging the Gap Between Data & Empathy: Building Scalable Emotion AI Systems