MioLink.it — AI Press Desk

MioLink.it was built to test how far an editorial workflow can be assisted by AI without losing sight of quality. Instead of handing all the work to a single model with one generic prompt, I built something that looks more like a real newsroom: every stage of the journalistic process has its own dedicated AI model, with its own role, its own "profile" and a specific task — much like a real newsroom, where the reporter doesn't do the fact-checker's job and the editor-in-chief doesn't write the articles.

The newsroom, role by role

The flow is orchestrated with n8n and moves through eight steps, each handled by a model chosen for the task, not the same model reused for everything:

• News gathering — sources are read via RSS by a fast, lightweight model: this needs sorting speed, not reasoning depth.
• Editor-in-chief — a much more technical model evaluates the clusters of gathered news and decides angle, priority and framing, the way an editor would in a newsroom meeting.
• Desk editor — routes the work between the two writing lines depending on the type of story.
• Tech reporter & feature reporter — the actual writing is handled by two different models, both specialized in writing Italian text, but with a different style: one more technical, the other more narrative.
• Senior byline — reviews and polishes the piece, giving it a consistent editorial tone before it moves to review.
• Fact-checker & SEO — another technical LLM, separate from the writers, compares the data in the article against the cited sources, checking that numbers, names and facts match what was actually gathered — not a grammar check, a truth check.
• Headline writer & copy editor — writes the headline and does a final editing pass.
• Art director & publishing — picks the cover image and publishes the finished piece to WordPress.

Splitting the roles this way has a precise reason: a model optimized to write fluent Italian isn't necessarily reliable at verifying facts, which is why fact-checking is handed to a different, more "technical" model whose only job is to compare the article against the sources — not rewrite it, not judge its style, just check its truthfulness against what was gathered.

What I learned

It's the most editorially "sensitive" project on the portal, because it touches directly on the quality and originality of AI-generated content — the same criterion Google applies when evaluating a site for advertising. That's why fact-checking isn't an optional step in the flow: it's its own node, with a model dedicated solely to that, before any article gets published.