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.

Why n8n and WordPress

All the orchestration lives in n8n: it is the diagram above, where each node is a step of the newsroom and the arrows are the piece moving from one desk to the next. Having it as a visual flow, rather than a monolithic script, lets me change a single role — swap the fact-checker's model, add an RSS source, move a check — without touching the rest. The destination is WordPress: not for fashion, but because it brings a ready-made editorial ecosystem (categories, tags, feeds, SEO, images) that the finished articles lean on instead of reinventing it.

The numbers so far

MioLink.it is not a theoretical demo: it actually publishes. As I write it has 35 published articles, the first in late November 2025 and the latest in late July 2026, organized across dozens of categories and tags. The pace is that of a small automated outlet: a few well-worked stories, not a wall of content churned out at speed — which is exactly the trap this project is built to avoid.

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.