week 3 challenge ( Hacktoberfest )
🦥 SlothMesh: Energy-Paced Edge AI with Gemma 4 on Arduino UNO Q This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady . What I Built What if your wireless network could detect problems, investigate what went wrong, and help choose a response without requiring you to constantly monitor dashboards or manually inspect network metrics? That is the idea behind…
Week 3 of the Hacktoberfest Open-Source AI Challenge introduced SlothMesh, an edge AI prototype built around the Arduino UNO Q. The project aims to detect wireless network issues, investigate problems, and suggest remediation actions without constant monitoring. SlothMesh's design involves two AI layers: TinyML on the Cortex-M33 for classification and Gemma 4 E4B, a 4 billion parameter language model, for higher-level reasoning.
The TinyML classifier analyzes six wireless telemetry features - RSSI, SNR, packet error rate, retry count, channel busy ratio, and jitter - to classify the network link as healthy, degrading, or critical. The project is open-source, released under the MIT License, and available on GitHub.
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