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Crowdsourced, inline LLM investigations of the things you're reading. OpenErrata fact-checks posts on Wikipedia, X, LessWrong, and Substack using frontier AI models with web search. When you visit a post that's been investigated, incorrect claims get a red underline. Hover for a summary of what's wrong. Click for full reasoning and source links. Posts with no issues show nothing — the extension stays out of your way. How it works: - The AI reads the full post, searches the web, and identifies claims that are demonstrably incorrect - Only flags things with concrete evidence against them — disputed claims, jokes, and ambiguity are left alone - Every investigation is public and auditable: the prompt, reasoning, and sources are all inspectable You can trigger investigations yourself with your own OpenAI API key, or wait for the community-driven queue to pick up popular posts. Once an investigation is completed Supported platforms: LessWrong, Substack (including custom domains), X/Twitter (Sort of), OpenErrata is fully open source. Code, spec, and design goals are public at https://github.com/ZeroPathAI/OpenErrata.
Mar 6, 2026
rating_count is the Chrome Web Store ratings count, not a written-review count.
Media assets
Screenshots and videos on the listing.
Has promo video
Whether the listing includes at least one video.
Languages
Declared language locales.
Developer website
Listing exposes a developer website URL.
Contact email
Listing exposes a contact email.
Keyword in name
Case-insensitive substring match in the name.
Keyword in description
Case-insensitive substring match in the description.
Keyword occurrences in description
Count of case-insensitive occurrences in the description.
Category user-count percentile
Share of same-category extensions with fewer users (null if unknown).
These are transparent listing completeness / keyword signals, not a prediction of Chrome Web Store search ranking.