This project was the result of a collaborative engineering effort involving both hands-on experimentation and AI-assisted development. Each AI assistant contributed in different ways throughout the project.
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Claude – Lead AI for all aspects of the project. Helped maintain technical focus, reviewed the evolving documentation for clarity and consistency, and provided extensive assistance in coding, organizing, and refining the final Markdown document.
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Google Gemini – Provided valuable guidance during the migration to ESP32 Arduino Core 3.3.10, helping identify and avoid legacy code patterns from earlier ESP32 core releases. Gemini also contributed to development discussions involving SX1262 Channel Activity Detection (CAD), Wake-on-Radio (WOR), and ESP32-S3 Deep Sleep integration.
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ChatGPT – Assisted throughout firmware development, debugging, architecture discussions, RadioLib integration, power optimization, Nordic PPK2 measurement interpretation, battery-life analysis, and technical review of the final documentation.
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GitHub Copilot – Assisted with firmware implementation, code completion, and day-to-day development within the programming environment.
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Jan Gromeš – Special thanks for the outstanding RadioLib library!
The successful implementation of an ESP32-S3 Deep Sleeping Wake-on-Radio node using the SX1262 and RadioLib was made possible through this collaborative process. While the hardware design, firmware integration, measurements, testing, and final engineering decisions were performed by the author, the insights provided by each AI assistant significantly accelerated development and helped improve the quality and accuracy of the final project.
"Project not possible without everyone's help."