mobile-tyre

We built LineageLens because teams were wasting reviewer time guessing where unfamiliar code came from. Archival logs are useful for audits, but reviewers need provenance in the flow of review: a prompt, a model, and a confidence score attached to the diff. Recent product work focused on small, high-leverage UX and correlation improvements drag/drop captures, click-to-insert in vscode extension so provenance is readable and actionable in minutes, not days. I'm curious: in your org, how do reviewers triage unfamiliar code today reproduce prompts, ask the committer, or revert and re-implement? What one capability would make provenance useful for your reviewers tomorrow?

New AI models pop up every week. Some developer tools like @Cursor, @Zed, and @Kilo Code let you choose between different models, while more opinionated products like @Amp and @Tonkotsu default to 1 model.

Curious what the community recommends for coding tasks? Any preferences?