Building an AI Agent Skill with Persistent Memory of Computer Activities
The article describes the development of 'Nex Life Logger', a background activity tracker that gives AI agents persistent memory of a user's computer activities, including browsing history, active windows, and YouTube video transcripts.
Why it matters
Nex Life Logger provides a solution for giving AI agents persistent memory of a user's computer activities, which can improve the agent's understanding and responsiveness.
Key Points
- 1Nex Life Logger tracks user activities locally without sending data to the cloud
- 2It generates hierarchical AI summaries of daily, weekly, and monthly activities
- 3Users can query their activity history through a desktop app or an OpenClaw/ClawHub skill
- 4The system includes multiple filtering layers to protect user privacy
Details
The author, who runs a digital transformation agency, built Nex Life Logger to address the issue of AI agents not having any memory or context of a user's previous activities and research. The tool tracks browser history, active windows, and YouTube video transcripts, storing everything locally in a SQLite database. It then generates hierarchical AI summaries of the user's daily, weekly, and monthly activities, which can be queried through a desktop app or an OpenClaw/ClawHub skill. The system includes multiple filtering layers to protect user privacy, excluding sensitive content like chat/messaging apps and focusing only on productivity-related activities. The author shares insights on the use of SQLite for this use case and the challenges of content filtering.
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