Mara Linden
AI Detection Researcher & Editor
Mara Linden has spent the past five years writing about AI tools, content authenticity, and the technologies reshaping how institutions verify human-generated work. She covers AI detection, content authenticity, and the intersection of generative AI and academic integrity — researching how these systems work, where they fail, and what the methodology behind a score actually means. Her work focuses on helping readers make sense of detection results rather than simply accepting them. She writes for general audiences with a background in editorial research, not engineering.
A detection score with no methodology is just a number someone invented. Before trusting a result, you should be able to ask: what did this tool actually measure, and how was that measured? If the answer isn’t in the documentation, that absence is itself information worth reporting.
AI detection · content authenticity · academic integrity
2020
Remote / International
Editorial Standards
Every article on this site begins with primary research: reading the tool’s own documentation, running independent tests where practical, and locating any third-party benchmarks that have been published on the subject. Statistics are cited with named sources and, where possible, direct links to the underlying data. Numbers that cannot be traced to a named origin are not used.
Limitations are stated as part of the review, not omitted to make a verdict tidier. AI detection is a genuinely contested area of research — detection rates vary by model, text length, writing style, and post-processing. Where evidence conflicts or a study’s methodology is disputed, that conflict is reported rather than resolved by selecting whichever result supports a cleaner headline.
No article on this site contains fabricated user quotes, invented test results, or accuracy figures presented without a source. Where a tool’s performance cannot be verified independently, the piece says so. The goal is to give readers enough information to evaluate the tool themselves — not to produce a rating that substitutes for their own judgment.
Editorial decisions — what to cover, how to frame findings, which caveats matter — are made by the author. They are not directed by the tools reviewed, the companies that build them, or any commercial arrangement. Affiliate disclosures appear separately, on the pages where they apply.
Published on This Site
What independent testing and published benchmarks actually say about detection accuracy — and where the numbers come from.
A breakdown of which generative engines the detector is built to recognize, and how engine identification works in practice.
Why the two tools measure completely different things — and what that means if you’ve been flagged by either one.
A Note on Independence
This site is editorially independent and has no affiliation with Hive, thehive.ai, or any generative AI company whose products are discussed in these pages. Coverage decisions are made without input from the developers of any tool reviewed here. The site is not a product of any AI detection company, and no article on it should be read as an official statement from any of the services it covers.
There is no financial relationship between this site and any AI detection model, engine, or platform discussed in its articles. Where affiliate arrangements exist — such as referral links to tools — those arrangements are disclosed clearly and separately on the individual pages where they appear. Affiliate compensation does not influence editorial conclusions, methodology descriptions, or accuracy assessments.