A Local AI System Reliability Agent with Gemma 3 4B
This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend What I Built I built a local AI system reliability agent that monitors a Windows computer, stores system metrics locally, analyzes trends, and uses Gemma 3 4B to provide reliability and maintenance recommendations. The idea came from a simple problem: A friend of mine often noticed that their computer would become…
A local AI system reliability agent has been developed for Hacktoberfest Weekend Challenge, specifically focusing on a Windows computer. The project aimed to address the problem of users struggling to understand why their computers become slow or unresponsive by providing clear system metrics and maintenance recommendations.
The solution utilizes Gemma 3 4B, a local AI model running through Ollama, to analyze trends and provide reliability insights. The agent monitors key system components such as CPU, RAM, disk usage, network, cache, and Windows events, storing historical data locally in SQLite. By analyzing this data, the AI can offer recommendations and insights to help users understand their system's performance and what they may need to investigate.
The architecture is built using Python, Tkinter, SQLite, FastAPI, MCP, Ollama, and Gemma 3 4B. The agent keeps system metrics and AI processing local, avoiding the need to send data to cloud AI services. This approach provides control over the AI stack, allowing for flexibility in changing the model, prompts, or tools without redesigning the monitoring system.
The project can be found on GitHub, showcasing the monitoring application, SQLite data layer, FastAPI service, reliability tools, MCP server, and local Ollama/Gemma integration.
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