Veronica Prilutskaya

Localization Engine Suggestions now come from the corrections human reviewers make, not only from low AI-review scores.

When a reviewer edits a translation whether in-house or through an external provider the localization engine compares the change against what it first produced. When the same fix recurs across jobs, or a correction clearly sets a term, it proposes the glossary, instruction, or brand-voice edit behind it. Apply writes the change into the engine, and the next translation runs with the fix in place.

Suggestions can now be approved or dismissed in bulk select a set and act on all of them at once, instead of one card at a time.

Full changelog: lingo.dev/changelog

AI reviews got more accurate: false positives dropped by 59.7%.

A false positive is a review that marks a correct translation as failing fewer of them means fewer alerts to check by hand, and review scores that can be trusted. We measured the drop on our internal benchmark. The biggest change: when a review doesn't apply to a given string, it no longer counts against the score, so scores reflect only the reviews that genuinely apply.

The reviewer setup form now shows example instructions as a placeholder for each review type.

Also this week:

lingo push --estimate reports what a translation run will cost before it runs.

The CLI uploads the source bytes so the server plans the exact delta only the keys and locales that are new or changed against what's already localized then prints the estimate and exits without translating anything.

Run it before a large or first-time translation to size the job, then run lingo push to translate for real. The number is an estimate, not a quote: it reflects the planned delta at the time it runs.