6 dated technical articles. These pages record the methods and claims as published; follow each article's source links and update notes for its current scope.
How Voidly gets verified censorship incidents to journalists, researchers, and monitoring systems: HMAC-signed webhook delivery with exponential-backoff retry, PGP-encrypted email for verified alerts, per-country and per-confidence-tier RSS feeds, alert deduplication by incident_id, and rate-limiting to prevent fatigue.
Censorship and information control · Engineering and infrastructure
How Voidly transitions a censorship incident through five states (Anomaly/MultiSourceAnomaly/Corroborated/Verified/Resolved) with threshold-gated transitions, stores every state change as an append-only event in a TimescaleDB hypertable with SHA-256 idempotency_key, and fans out verified incidents to alert delivery and cache invalidation via three Kafka topics — with the compute_incident_id() Rust function that makes incident IDs deterministic across pipeline restarts.
Censorship and information control · Engineering and infrastructure
How Voidly schedules 80-domain probe runs across 37+ nodes: domain priority tiers by OONI category code, anomaly-driven priority boosts, protocol selection per domain, ±15% jitter for anti-detection, ASN distribution to ensure cross-ASN coverage, adaptive scheduling that injects urgent re-measurements on anomaly detection, and per-country task budgets (CN 68, IR 74, RU 72, global avg 49 tasks/window).
Censorship and information control · Engineering and infrastructure
How Voidly ingests 200M+ OONI Explorer measurements, aligns them with Voidly probe data on a country-domain-date key, generates probabilistic training labels using five Snorkel-style label functions, handles OONI coverage gaps with label distillation, and constructs the labeled dataset that trains the five-class anomaly classifier.
Censorship and information control · Machine learning and OSINT
How the Voidly ML classifier distinguishes DNS tampering, TLS interference, HTTP blocking, BGP withdrawal, and throttling — five per-class binary models, country-specific calibration, and why 95% recall beats 95% precision when cross-source corroboration filters the noise.
Censorship and information control · Machine learning and OSINT · Engineering and infrastructure
How Voidly evaluates the five-class censorship anomaly classifier offline before deployment: the ClassifierEvaluator test harness, per-country AUC-PR vs. AUC-ROC tradeoffs for imbalanced censorship data, F2 scoring rationale, per-country confusion matrix case studies (Iran 0.97 DNS recall, China 0.78 precision from CDN noise, Russia TSPU throttling), ECE calibration before and after Platt scaling, and model promotion criteria.
Censorship and information control · Machine learning and OSINT