I built ALEC, a platform for detecting anomalies in sensor/metrics data using complexity theory instead of static thresholds.
The core idea: instead of alerting when "CPU > 80%", we monitor the relationships between metrics using Shannon entropy and Pearson correlation. When the complexity signature changes, something is wrong — often 70% earlier than traditional thresholds would catch it.
Three components:
- Gateway: ingests data from multiple sources, computes real-time correlations
- Complexity: calculates entropy and a single "health score" (R) per system
- Codec: compression for IoT (80-95% reduction, works on tiny messages where gzip fails)
The math is based on Quantitative Complexity.
Written in Rust. Open source (AGPL-3.0).
Use cases: IoT sensor fleets, Kubernetes monitoring, industrial predictive maintenance.
Hi HN,
I built ALEC, a platform for detecting anomalies in sensor/metrics data using complexity theory instead of static thresholds.
The core idea: instead of alerting when "CPU > 80%", we monitor the relationships between metrics using Shannon entropy and Pearson correlation. When the complexity signature changes, something is wrong — often 70% earlier than traditional thresholds would catch it.
Three components: - Gateway: ingests data from multiple sources, computes real-time correlations - Complexity: calculates entropy and a single "health score" (R) per system - Codec: compression for IoT (80-95% reduction, works on tiny messages where gzip fails)
The math is based on Quantitative Complexity.
Written in Rust. Open source (AGPL-3.0).
Use cases: IoT sensor fleets, Kubernetes monitoring, industrial predictive maintenance.
GitHub: https://github.com/zeekmartin/alec-codec Demo: https://alec-codec.com
Would love feedback on the approach — especially from anyone who's worked with entropy-based anomaly detection.