Three optimization strategies based on hardware characteristics
For Mac users with M1/M2 chips, you can unleash the full potential of Fenn's hardware acceleration by doing the following:
- Neural Network Engine CallsThe "Apple Neural Network Engine Acceleration" option in Settings-Performance improves the efficiency of AI content recognition by about 40%, especially when dealing with video content analysis, scene detection speed can be increased to real-time level.
- Memory Optimized Configuration::
- Allocate at least 2GB of free memory (checked by activity monitor)
- Close other background apps that consume the NPU (e.g. AI retouching tools running at the same time)
- Intelligent Caching Mechanism: By using the "rebuild index" function (Settings - Maintenance) every week, the system will automatically learn the user's high-frequency access paths to form a hot data cache. Tests show that after 2-3 optimization cycles, the response speed of repeated search scenarios can be improved by 60%.
Additional tip: For M1 Macs with insufficient storage space, you can set Fenn to perform deep indexing only when connected to a power source (Settings - Battery) to avoid affecting mobile use range.
This answer comes from the articleFenn: Local AI search tool to find Mac computer files quicklyThe
May not be reproduced without permission:AI productivity tools " How can I improve Fenn's search responsiveness on M-series chip Macs?
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