WORKING WITH AI COPILOTS

In our project, we utilized ChatGPT as an AI copilot to help debug and troubleshoot issues with Particle Cloud connectivity. Specifically, we aimed to pair two Particle devices using Particle.publish and Particle.subscribe functions. While ChatGPT provided useful general advice, such as checking event names and verifying cloud connectivity, it struggled with more nuanced, project-specific challenges.

One major issue we encountered was ensuring proper communication between the devices. ChatGPT suggested several debugging steps, including reviewing Particle documentation and using the Particle Console to monitor events. However, these suggestions did not address the root cause of our problem. The core issue lay in account management—both devices needed to be under the same Particle account to communicate effectively, a limitation ChatGPT did not account for in its guidance. Therefore, we don't have effective prompts to provide here. 

Ultimately, we resolved the problem manually by moving both devices under the same Particle account. This allowed the devices to pair and communicate successfully. While ChatGPT was helpful for general debugging and brainstorming, its lack of precision in addressing specific technical constraints meant we had to rely on manual experimentation to fully resolve the issue. This experience highlighted the need to complement AI suggestions with hands-on problem-solving when tackling complex, real-world challenges.

One area we did use AI for was editing the video we wanted to show in the background during our showcase. We used the AI features built into the Clipchamp video editing software to edit our clips and images together in a way that captures the experience of the Lover's Cups.