Machine Learning
ML use-cases for Nostr: ranking, moderation, embeddings, and summarization in privacy-preserving ways.
In this guide:
Use Cases
What ML adds to Nostr apps
- Timeline ranking
- Abuse and spam moderation
- Search via embeddings
Dataset Curation
Quality in, quality out
Sampling
Avoid bias; stratify by kinds, authors, time.
Labeling
Lightweight human-in-the-loop for moderation/quality.
Versioning
Track dataset and feature versions for reproducibility.
Offline Pipeline
ETL, features, training, and evaluation
Export curated events, build features, and train models offline; deploy small on-device models where possible.
Integration
Real-time inference and fallbacks
Serve models behind a feature-flag; cache results and provide predictable fallbacks for cold-start.
Evaluation
Measure model value, not just accuracy
Define task metrics (CTR, abuse catch-rate), offline AUC/precision-recall, and run online A/B tests gated by safety checks.
Next steps
Explore analytics integration, system architecture, and implementation approaches for ML in Nostr.