reviewer
Tuan Hoang
detection lead, amige.
Tuan Hoang leads detection at amige. He sets how the detector panel is assembled and scored, and reviews every model guide, glossary entry, and comparison on this site for accuracy.
His focus is the gap between lab benchmarks and real-world detection: where detectors break on compressed, cropped, and re-shared media, and why a panel of independent classifiers fails less often than any single one. The articles here cite primary sources and name their limits on purpose.
Every article in the amige. corpus carries a “last reviewed” date. When a model ships a new version or a benchmark updates, the affected guides get re-checked against primary sources.
what Tuan reviews
- how amige. detects AI →the methodology: a panel of independent detectors, fused honestly.
- model guides →how to recognize and detect each generator, version by version.
- the glossary →plain-language definitions of the terms behind every verdict.
- detector comparisons →honest head-to-heads against the other AI detectors.