How we build a weekly AI roundup from primary sources

The short answer

Readers see the editorial method: source watchlist, change log, impact filter, claim sheet, conflict check, commentary label, and final update pass.

The decision standard is simple: preserve the source, state the limits, and make the next human check obvious. A useful article should reduce uncertainty without pretending that every unknown has been resolved.

What to examine

A strong roundup is not a pile of links. It chooses changes that affect readers, checks the original material, adds context, labels uncertainty, and leaves out stories with weak evidence.

Start with scope. Identify the product, account, audience, jurisdiction, data, and decision involved. Then separate what was directly observed from what a vendor, researcher, regulator, or commentator says. Record dates because AI products, access rules, and prices change quickly.

Speed increases error risk. A roundup should say when an announcement is preliminary, access is staged, or independent evidence is missing.

A practical way to do it

Start with dated primary-source feeds and documents.
Write one verified sentence per development before analysis.
Group by reader impact and disclose what remains unknown.

Keep the worksheet or test record with the draft. If another editor cannot reproduce the check from the saved evidence, the article is not ready.

Editorial guardrail

Do not fill a missing fact with a plausible sentence. Mark it as unknown, find a stronger source, narrow the claim, or remove it. Commentary belongs in a clearly labeled paragraph after the reported facts, not inside them.

Primary-source reading list

These are starting points, not automatic support for every sentence. The publishing editor must open each cited page and confirm the claim it supports on the day of review.

Bottom line