Spot-Undoo v2: Open-Weight AI Layover Studio & GPU Map Engine for Kerala

A developer built Spot-Undoo v2, an open-source travel and layover-optimization studio for Kerala, upgrading the mobile app into a desktop workstation with a GPU-accelerated Leaflet map and local open-weight AI inference. The system runs Qwen 2.5:3B and Gemma 4 via Ollama with structured JSON outputs to enforce time, budget and transit constraints, falling back to mathematical routing when offline, and performs on-device image downsampling to 512px JPEGs before vision-model analysis.

This is a submission for the Hacktoberfest Open-Source AI Challenge: Week 3 https://dev.to/challenges/hacktoberfest-week3-2026-10-19 . 🌟 Spot-Undoo v2: Open-Weight AI Travel Studio & Layover Optimizer Spot-Undoo is an open-source, privacy-preserving micro-travel concierge engine tailored for travelers, transit layover passengers, and explorers across Kerala's 14 districts. During Week 3 , we advanced Spot-Undoo from a mobile-first app into a full-scale Open-Source AI Desktop Studio & GPU-Accelerated Geo-Engine , designed to deliver instant, constraint-guaranteed transit itineraries with zero cloud inference cost . 🚀 What's New in Week 3 Development 1. 🖥️ Widescreen Desktop Studio Layout 1680px : - Complete architectural redesign away from cramped mobile layouts into a split-screen studio workstation. - Docked Inspection Panel : Left-docked details card with high-resolution photography, transit breakdowns, and authentic community reviews. - Expanded Canvas : Right-hand interactive Leaflet map canvas utilizing full desktop screen real estate. - Dual-Column Layover Assistant : Dedicated left-side chat cockpit with right-hand live telemetry inspector. 2. ⚡ GPU-Accelerated Leaflet Map Zero Pan Lag : - Eliminated panning and zoom stutter across large Kerala spot clusters using memoized zoom tiers and batched L.layerGroup . - Hardware-accelerated CSS transforms will-change: transform; transform: translate3d 0,0,0 achieving smooth 60fps rendering. 3. 🧠 Open-Weight Local AI Inference Ollama & Qwen : - Powered by local open-weight models Qwen 2.5:3B / Gemma 4 with structured JSON outputs. - Guaranteed constraint matching max available time, strict budget limits, transit travel time . - Instant mathematical fallback routing if running completely offline. 4. 📸 On-Device Multimodal Verification : - HTML5 Canvas client-side downsampling to 512px JPEG before sending to vision models, keeping memory footprint low. - Instant visual spot analysis with vibe tagging and authentic local tips. 🛠️ Open-Source Tech Stack - Frontend : React 18, Vite, Tailwind CSS 3, Lucide Icons, Leaflet OpenStreetMap. - AI Models : Local open-weight models via Ollama qwen2.5:3b , gemma4 , Groq Cloud API, Google Gemini Flash. - Maps & Data : OpenStreetMap, Leaflet GPU acceleration, LocalStorage zero-database architecture. - Mobile & Android : Capacitor native Android bridge SpotUndoo-v1.0.apk . 🔗 Open-Source Repository & Demo Built with ❤️ for Hacktoberfest 2026 & MLH Hackathons.