Building Nix: ML-Powered Notification Prioritization

The Challenge

The average smartphone user receives 80+ notifications per day. Most are irrelevant noise, but buried within are genuinely important messages. The challenge: how do you separate signal from noise without requiring manual configuration for every app and sender?

Traditional solutions rely on static rules or simple heuristics. But notification importance is deeply contextual. A message from your manager at 9 AM during work hours is different from the same message at 11 PM. A notification from a group chat you actively participate in is different from one you've muted mentally.

Our Approach

We built an on-device ML model that learns from your behavior patterns to predict notification importance. Here's how it works:

Feature Engineering

We extract features from multiple dimensions:

**Sender signals**: Contact frequency, recency of interaction, relationship graph position

**Content signals**: Message length, presence of questions, urgency keywords, @mentions

**Temporal signals**: Time of day, day of week, proximity to calendar events

**Behavioral signals**: Historical response time, interaction patterns, app usage context

Model Architecture

We use a lightweight transformer-based model optimized for on-device inference. The model is small enough to run in real-time (< 50ms inference) while maintaining high accuracy.

Key architectural decisions:

Quantized weights (INT8) for memory efficiency

Pruned attention heads for faster inference

Distilled from a larger teacher model

Privacy-First Design

All inference happens on-device. We never see your notifications. The model is pre-trained on anonymized, aggregated patterns and fine-tunes locally based on your behavior.

Results

In internal testing with 50 beta users over 4 weeks:

73% reduction in notification interruptions

0.2% false negative rate on high-priority notifications

89% user satisfaction with prioritization accuracy

What's Next

We're continuing to improve the model with:

Better handling of novel senders

Calendar-aware context modeling

Cross-device synchronization of learned preferences

The goal remains the same: you should see what matters, when it matters, and nothing else.