week 3 ( 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…
Hacktoberfest Week 3 introduced SlothMesh, an open-source edge AI prototype for wireless network monitoring and remediation built on Arduino UNO Q and Gemma 4. The system employs two AI layers: TinyML on the Cortex-M33 microcontroller for lightweight classification of link conditions, and local language-model reasoning using Google's Gemma 4 for higher-level analysis.
SlothMesh aims to detect network issues quickly, investigate when necessary, and keep decisions under deterministic control. The project seeks to reduce unnecessary computation and avoid reliance on external AI APIs. The demonstration includes a TinyML classifier, Gemma 4 E4B model, and a deterministic Laya decision layer to constrain AI recommendations.
The source code for the project is available on GitHub under the MIT License, detailing the TinyML model, firmware, simulation workflows, and interactive dashboard.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
