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- 🧑‍💻 Technology Stack
- 🔍 Context
- 📝 Process
- 💡 Current Solution
- Space Planner ⇒ MVP
- Space Planner ⇒ MAP
- 🗓️ Development Roadmap
- Research Impact
- Technical Takeaway
🧑‍💻 Technology Stack
JavaScript/TypeScript, React, Zustand, Next.js, Tailwind, Paper.js, Three.js, Rhino3dm.js, ThatOpen, Supabase, RhinoCommon, Revit API, C#, Node.js, ngrok
🔍 Context
Traditional homeowner spatial planning produces fragmented, disconnected experiences that leave owners dependent on professionals throughout their property's lifecycle, creating barriers to autonomy, informed decision-making, and cost-effective maintenance.
- Technical: Manual floor planning through consumer apps results in low-fidelity outputs incompatible with professional workflows, creating unbridgeable gaps between homeowner vision and contractor execution
- Coordination: Non-standardized home documentation undermines effective communication between homeowners, contractors, and service providers, leading to miscommunication during renovations and maintenance
- Manufacturing/Quality: Inconsistent spatial understanding across renovation phases creates unpredictable project outcomes and limits homeowners' ability to evaluate contractor proposals or quality effectively
- Project-Specific: Limited post-renovation spatial intelligence prevents homeowners from independently managing ongoing maintenance, warranty tracking, and future improvements throughout their property's 20+ year lifecycle
📝 Process
The Challenge
I initially chose PixiJS for its 2D rendering strength, but quickly hit walls with modularity and scalability, limiting my vision for a comprehensive spatial planning tool.
The Pivot
Switching to Next.js unlocked the potential I was seeking:
- Modular architecture for seamless user workflows across planning stages
- Robust state management via Zustand for complex spatial data
- Professional integration through API endpoints for architectural tools
The Reality
The transition wasn't without hurdles though, as I soon faced a wall of WebAssembly compatibility and server-side rendering challenges. But these obstacles became opportunities to deepen my technical understanding too!
The Result
Today, the platform seamlessly combines Paper.js for 2D visualization and Three.js for 3D capabilities, with backend services ready for future BIM integration and environmental analysis. This journey reinforced my belief that the right technology stack doesn't just solve today's problems, it can also enable tomorrow's possibilities, supporting both Senibina's mission to democratize spatial planning and my continuous learning as a developer.
💡 Current Solution
Space Planner ⇒ MVP
A web-based platform focused on intuitive 2D floor plan creation, featuring essential design tools and automated backend processing. The system includes core functionality for basic spatial planning and element design capabilities, leveraging professional-grade architectural technology under the hood.
Space Planner ⇒ MAP
Advanced spatial planning platform incorporating 3D modeling, comprehensive asset libraries, and professional documentation features. Enhanced with environmental analysis capabilities, performance simulation tools, and standards-based dimensioning for professional-grade spatial intelligence.
🗓️ Development Roadmap
Research Impact
Establishes an architectural practitioner-led computational methodology that democratizes professional spatial intelligence by developing comprehensive cross-platform BIM integration and a scalable module ecosystem. Demonstrates that complex AEC accessibility challenges can be systematically solved through hybrid architectural-programming approaches, validated by Antler pre-seed investment..
Technical Takeaway
Demonstrates how architecture technology combined with full-stack development can create accessible spatial intelligence platforms using automated server-side processing. By removing dependencies on professional software while maintaining BIM compatibility, this approach breaks down industry accessibility barriers through smart automation.
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