Build blueprint
Personal File Attention Index
A local index that scores files, downloads, screenshots, and documents for urgency and project relevance.
- Problem
- Desktop search finds keyword matches but does not continuously infer what is currently important.
- Why Jev
- The index becomes useful through thousands of cheap decisions per filesystem event.
- Architecture
- File metadata/extract → parallel relevance Scores → local SQLite index → Spotlight-style UI.
- Current alternative
- Recency sorting, embeddings, or manual folders.
- Jev advantage
- Adds active judgment to every file event without generating summaries.
- Unknowns
- Private-data controls and whether lightweight features provide enough context.
Why this confidence: Visually compelling and high frequency, with unproven personalization quality.
This is an authored hypothesis derived from the research corpus. Nothing here demonstrates product demand, or that Jev performs well on this particular workload. Run the validation experiment before building past the MVP.
Sources
Limitations
- Private-data controls and whether lightweight features provide enough context.
- This is a research hypothesis, not evidence of product demand or Jev performance in this workflow.
Supporting research
- Parallel decision matrix PlausibleOne state is evaluated against many named questions, replacing repeated prompt/parse cycles.
- HA-Jev ObservedA Home Assistant custom integration exposing Jev evaluations as sensors and automation actions.
- Cheap decision calls could make tens or hundreds of semantic judgments per event economical. SpeculativeThe product design follows from published pricing and parallel question primitives, not independent production evidence.
- Decision quorum PlausibleMultiple typed judgments are aggregated through voting or confidence-aware stopping.