4 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's corroboration engine fetches and aligns data from three independent sources in near-real-time despite their different latency profiles: tokio::join! parallel fetches with per-source timeouts, adaptive OONI polling (15m/60m/3h/6h), in-memory CensoredPlanet daily dump index, independence-weighted source agreement scoring, and retroactive nightly reprocessing against the CP daily dump.
Censorship and information control · Engineering and infrastructure
How the Voidly MCP server exposes 83 tools for querying the global censorship dataset from Claude, GPT, and agent frameworks — incident lookup, measurement queries, country summaries, BGP events, shutdown forecasts, and wiring it into Claude Code.
Censorship and information control · Engineering and infrastructure
How the nightly Voidly export job extracts measurements from TimescaleDB and pushes Parquet snapshots to HuggingFace Hub: PyArrow schema with dictionary-encoded columns, server-side cursor streaming at 50K rows per round-trip, Zstandard level 3 compression, country + year_month partitioning, atomic HuggingFace commit with CommitOperationAdd, post-push SHA-256 verification, and the incremental vs. monthly full-snapshot strategy.
Censorship and information control · Engineering and infrastructure · Transparency and open data
How the Voidly CC BY 4.0 measurement dataset and the OONI historical corpus are hosted on HuggingFace — Parquet snapshot structure, daily incremental updates, git-lfs versioning, and Python/R filter recipes for journalists, ML researchers, and infrastructure teams.
Censorship and information control · Transparency and open data · Engineering and infrastructure