The more users we spoke to, the more we realized something important:

The biggest challenge isn t writing code anymore.

AI already helps with that.

The real challenge is understanding whether that code is healthy.

Two new built-in AI Reviewers ship: a glossary reviewer and an instructions reviewer. Toggle on the glossary reviewer and every configured glossary term is automatically checked on every translation. Toggle on the instructions reviewer and every configured instruction is checked the same way. Open any translation in the dashboard log and see pass/fail per term, pass/fail per instruction, with the reviewer's reasoning on failure. One toggle replaces authoring an AI Reviewer for every single criterion.

Also shipped

• Per-engine AI Reviewer assignment. Attach different reviewers to different engines instead of one set for the org.

• Organization-level timezone setting. Reports display in the timezone you configure for your org.

Full changelog: lingo.dev/changelog

Honest question for this forum.

I save AI tool threads constantly. New model drops, someone's slick Cursor workflow, "10 tools that'll change everything." I open maybe one in twenty. And I still feel behind every single week.

Lately I think the problem isn't information, there's an ocean of it. The problem is that almost all of it tells you about AI instead of showing you. A thread describes a workflow. A launch video is scripted so everything works on the first try. None of it shows the real thing: the prompt someone throws away, the tool they reach for and why, the moment it breaks and how they dig out.

And that's the part that actually teaches you. Nobody got good at cooking from recipes or at chess from the rulebook. You get good watching people better than you do the real work.