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ClimateIQ - AI Acceleration

This is a submission for the AI Challenge for Cross-Platform Apps - AI Acceleration What I Built I built ClimateIQ — a comprehensive climate intelligence platform that demonstrates how AI coding assistants can accelerate the d…

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This is a submission for the AI Challenge for Cross-Platform Apps - AI Acceleration






What I Built



I built ClimateIQ — a comprehensive climate intelligence platform that demonstrates how AI coding assistants can accelerate the development of complex, production-quality cross-platform applications.



APIs Used:





  • Google Gemini AI — Powers climate alerts, crop recommendations, eco-tips, waste scanning


  • NASA FIRMS — Real-time fire/thermal anomaly data


  • OpenWeather API — Temperature, air quality, precipitation data


  • NREL PVWatts — Solar potential calculations


  • Planet Labs — Vegetation health (NDVI) data


  • Storyblok CMS — Community content, events, learning modules



The Challenge: Build a feature-rich climate app with 15+ tools, 6 real-time data layers, AI integrations, and CMS-powered content — all running on multiple platforms from a single codebase.



The Solution: Leverage AI coding assistants with Uno Platform MCP for contextual, grounded guidance throughout development.






Demo



🔗 GitHub Repository: https://github.com/omkardongre/ClimateIQ-App



🌐 Live WebAssembly Demo: roaring-gumption-b8bf96.netlify.app









AI Development Screenshots






IDE with AI Assistant



IDE Screenshot 1



Google Antigravity IDE with Gemini AI researching Mapbox integration and recommending Mapsui as the best map solution for Uno Platform



IDE Screenshot 2



Windsurf IDE with Cascade AI fixing XAML build errors and adding Uno Toolkit to multiple pages simultaneously






Uno Platform MCP in Action



MCP Screenshot 1



Uno Platform MCP searching documentation for Material Controls Styles and Toolkit UI components



MCP Screenshot 2



Uno Platform MCP fetching Material Toolkit theme setup guidance while AI updates App.xaml resources









App Screenshots






Home Page - Feature Discovery



Home Page






Interactive Climate Map - 6 Data Layers



Real-time visualization with NASA FIRMS fire data, air quality, flood risk



Climate Map 1



Climate Map 2



Climate Map 3






AI Climate Alerts - Gemini Powered



Personalized alerts with severity indicators and actionable recommendations



AI Alerts






Smart Agriculture Hub - Multi-Agent AI



AI Crop Advisor



Crop Advisor 1



Crop Advisor 2



Carbon Calculator



Carbon Calculator 1



Carbon Calculator 2



Solar Irrigation Calculator



Solar Irrigation 1



Solar Irrigation 2






Urban Sustainability Hub



Waste Scanner, Solar Savings Calculator, AI Eco-Advisor, Smart Home Tracker



Urban Hub



Waste Scanner



Waste Scanner



Solar Savings Calculator



Solar Savings 1



Solar Savings 2



AI Eco-Advisor



Eco Advisor






Community Hub - Storyblok CMS



Environmental events, news, learning modules



Community Hub









Cross-Platform Testing






Linux Desktop (Ubuntu)



Linux Desktop App



ClimateIQ running as a native desktop app on Ubuntu Linux with Skia renderer






WebAssembly (Browser)



WebAssembly Browser



Same app running in the browser via WebAssembly, deployed to Netlify









AI Tooling in Action






AI Agents Used





  • Windsurf (Cascade/Claude) — Primary AI coding assistant for code generation, debugging, and architecture


  • Google Project IDX with Gemini — Additional AI assistance for rapid prototyping



I used AI coding assistants throughout the entire development process. Here's how AI accelerated my workflow:






1. Uno Platform MCP Integration



The Uno Platform MCP Server provided contextual, grounded guidance for:




  • XAML layout patterns and best practices

  • Cross-platform compatibility considerations

  • Material Design integration with Uno Toolkit

  • Navigation patterns and state management

  • Platform-specific adaptations



Example Interaction:




Me: "How do I create a responsive card layout with shadows?"
MCP: [Provided specific Uno Platform guidance on ThemeShadow,
Border styling, and responsive Grid layouts]









2. Code Generation Acceleration



Before AI: Manually writing 100+ XAML files, ViewModels, Services, and Models would take weeks.



With AI: The AI assistant generated production-quality code efficiently:

































Component Approximate Lines AI Contribution
XAML Pages ~3,000 lines ~90% AI-generated, human-refined
ViewModels ~2,500 lines ~85% AI-generated
Services ~2,000 lines ~80% AI-generated
Models ~500 lines ~95% AI-generated





3. Real-Time Problem Solving



Challenge: Emojis not rendering on Linux Skia renderer.



AI Solution: The AI researched the issue, identified the root cause (font fallback limitations), and implemented a comprehensive fix — replacing all emojis with styled text badges across 10+ pages in a single session.



Challenge: Complex multi-step wizard for Solar Savings Calculator.



AI Solution: The AI designed the 4-step wizard architecture, implemented progress tracking, and integrated NREL API calls — all following Uno Platform best practices from MCP guidance.



Challenge: JSON deserialization failing in WebAssembly Release builds.



AI Solution: The AI identified the JsonSerializerIsReflectionDisabled error caused by .NET trimming, and replaced all reflection-based JSON operations with manual JsonDocument parsing across 7 service files.






4. API Integration Patterns



The AI helped integrate 6 different APIs with proper:




  • Error handling and fallbacks

  • Rate limiting considerations

  • Response parsing and mapping

  • Caching strategies




// Example: AI-generated NASA FIRMS integration
public async Task<IEnumerable<ClimateDataPoint>> GetFireDataAsync(double lat, double lon)
{
// AI generated this with proper error handling,
// CSV parsing, and data point mapping
}









5. UI/UX Refinement



The AI helped maintain consistent design patterns:




  • Gradient headers on every page

  • Card-based layouts with proper spacing

  • Accessible color contrasts

  • Responsive breakpoints









Key AI Acceleration Metrics

































Metric Traditional Estimate With AI
Initial prototype ~2 weeks ~2 days
Full feature set ~2 months ~2 weeks
Bug fixes Hours each Minutes each
Cross-platform testing Days Hours








MCP-Grounded Development



The Uno Platform MCP ensured that AI suggestions were:




  • ✅ Compatible with Uno Platform's Skia renderer

  • ✅ Following MVVM patterns correctly

  • ✅ Using proper XAML syntax for cross-platform

  • ✅ Leveraging Uno Toolkit components appropriately









Targets



ClimateIQ runs on multiple platforms from a single codebase:

































Platform Framework Status
🪟 Windows net9.0-desktop ✅ Working
🐧 Linux net9.0-desktop (Skia) ✅ Working
🍎 macOS net9.0-desktop ✅ Builds
🌐 WebAssembly net9.0-browserwasm ✅ Working





Build Commands






# Desktop (Linux/Windows/macOS)
dotnet run -f net9.0-desktop

# WebAssembly
dotnet run -f net9.0-browserwasm












Development Experience



The AI development experience was transformative:





  • AI + MCP = Grounded Intelligence — The Uno Platform MCP kept AI suggestions relevant and accurate


  • Iterative Refinement Works — Quick feedback loops with AI accelerated learning


  • Complex Apps Are Achievable — What seemed like months of work became weeks


  • Cross-Platform Is Real — Write once, run everywhere actually works with Uno Platform






Built with Uno Platform, .NET 9, Google Gemini AI, Windsurf AI Assistant, and a passion for climate action. 🌍

2. Cyber Threat Intelligence & Forensik

CTI Threat Relationship Graph6 Knoten / 5 Relationen
CVE / Incident Software MITRE ATT&CK CWE Weakness IoC
MITRE ATT&CK Matrix Navigator 14 Taktiken
1 belegte TechnikenLive-Mapping
Reconnaissance
Resource Development
Initial Access
Execution
Persistence
Privilege Escalation
Defense Evasion
Credential Access
Discovery
Lateral Movement
Collection
Command and Control
Exfiltration
Impact
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