# Identical cross-post Part of [The AI Tells Index](https://feedsquad.com/ai-tells). A tell signals low effort. It does not identify an author. Skilled writers produce every shape listed here, some of them daily, and automated detectors misread those writers for it at rates measured above 60 percent on non-native English prose. Nothing in this index proves that a machine wrote anything. Read an entry as one piece of evidence to weigh against the false-positive notes printed beside it. ## Facts - Id: identical-cross-post - Category: Platform-specific (platform) - Subcategory: distribution - Also known as: same text on every surface, unadapted syndication - Status: Active. Currently signals low-effort writing. - Severity: medium - Evidence grade: feedsquad-observed (our own corpus only, no external attestation) - Languages: en - Added: 2026-08-15 - Updated: 2026-08-15 - Page: https://feedsquad.com/ai-tells/identical-cross-post ## Description One text published verbatim on platforms whose formats and audiences do not match. The result is usually visibly broken on at least one surface: a reference to a link where no link exists, a sentence truncated by a character limit, a hashtag block on a platform that ignores tags. Keep this separate from unenhanced-repost, which is about reusing someone else's material rather than your own. ## Why it reads as machine-written Adapting text per surface takes a decision per surface, and generation makes producing one text so cheap that the decisions get skipped. The tell is not that the text repeats. It is that nobody read the repeat against the place it landed. ## Detection Type: statistical (a measured metric against a threshold with a stated basis) Metric: cross-platform-text-identity-ratio Threshold: 0.95 (fires when above) Threshold basis: > No published baseline exists for how much a post should change between surfaces. Ninety-five percent character-level identity across two platforms is a FeedSquad review trigger, deliberately set high so that a short factual announcement, which legitimately needs no adaptation, passes it. Compare only posts over 60 words published to two surfaces within 48 hours. The number is ours and it is a prompt to look at the two posts side by side, not a verdict about either. ## Examples Constructed specimens. Written for this index. Never quoted from anyone's posts. ### Specimen 1 Before, exhibiting the tell: > Thrilled to announce that our analytics dashboard is now live for all customers. Click the link below to read the full announcement and see what it means for your team. After, repaired: > The analytics dashboard is live. For X, the one number worth knowing is that report load time dropped from 9 seconds to under 1. For LinkedIn, the rebuild took two engineers eleven weeks and the query plan that made the difference is in the post. For Instagram, a screenshot of the new view beside the old one. Note: The before was published identically on four surfaces. On Instagram there was no link below, on X it truncated mid-sentence, and on LinkedIn the announcement it pointed to was the post itself. Figures in this repair are invented for the specimen. ## False positives Who legitimately writes this way. Small teams cross-post deliberately to save time and are right to, and a short factual announcement often needs no adaptation at all. Legal, safety and recall notices are supposed to be identical everywhere, and changing the wording per surface would be the error. The reviewer check is whether the text refers to something the surface does not have: a link, a thread below, a swipe, a character count it exceeded. ## Model attribution No family attribution. This is a publishing-workflow property rather than a model property, and it happens just as readily when a person copies and pastes. ## Sources 1. FeedSquad multi-platform publishing (FeedSquad observation) Corpus: Posts scheduled from one draft to two or more of LinkedIn, X, Threads and Instagram inside FeedSquad, 2026. The product adapts text per surface when asked; the observation is what reaches the feed when it is not asked. Qualitative, no counts kept. Observed 2026-08-15 ## Status history Ids are permanent. A retired tell keeps its id and its page. - 2026-08-15, Active: This is a genuine observation from FeedSquad's own multi-platform publishing, where the same draft can be pushed to four surfaces in one action, and it is graded on that corpus rather than dressed up with an external citation. Medium severity because the failure is visible to readers and cheap to fix. ## License CC BY 4.0. https://creativecommons.org/licenses/by/4.0/ Attribution: The AI Tells Index, feedsquad.com/ai-tells Reuse the data, including commercially. Keep the attribution line. --- Dataset version 1.0.0. Schema version 1. Part of [FeedSquad](https://feedsquad.com). Built by [Herman Foundry](https://hermanfoundry.com) from Levi, Finnish Lapland.