Introduction
At Nova Labs, creators of the Helium Network, I contributed to one of the most ambitious real-world applications of crypto. Helium began as a decentralized wireless network for IoT, powered by nearly a million user-deployed hotspots across the globe. By rewarding hotspot owners with the $HNT token, Helium built a vast, community-powered network infrastructure, proving that blockchain incentives could bootstrap physical infrastructure at global scale. This model helped pioneer the DePIN movement, and the project later migrated to Solana to enhance scalability and cost-efficiency.
My work focused on 1663, an IoT data aggregation and intelligence platform built on top of Helium. 1663 transformed raw wireless telemetry into usable insights by aggregating sensor data, normalizing it, and providing real-time analytics for applications in logistics, agriculture, environmental monitoring, and beyond. We built robust APIs and dashboards for enterprises to interact with IoT data meaningfully, positioning 1663 as the intelligence layer of the Helium ecosystem.
Live Project
Challenges
Designing 1663 required tackling the complexity of real-time IoT data at global scale. Devices across the Helium Network produced heterogeneous telemetry, location, temperature, motion, humidity, often in inconsistent formats and intervals. Creating a cohesive user experience that could normalize and visualize this data in a meaningful way was a major challenge. We had to build a flexible, modular UI that could adapt to varied device types while still feeling unified and intuitive.
Another key challenge was designing for real-time responsiveness. With thousands of devices streaming data concurrently, we had to account for latency, gaps, and noisy signals. The interface needed to remain fast and reliable even when data was incomplete or delayed. We implemented fallback states, intelligent polling, and progressive loading to ensure the system felt responsive without overwhelming users with raw, unprocessed data.
Finally, we had to balance technical depth with usability. Many users were engineers or data scientists who needed full access to queries, filters, and raw payloads, while others were operators or business users looking for high-level insights. Designing for both required layered complexity: default views for quick insight, with deeper controls available when needed. This tension between power and clarity shaped nearly every design decision on the platform.
