Hey Indie Hackers! We just shipped ThinkReview Cloud 2.2.0, adding search grounding to our AI code reviews for GitHub, GitLab, Azure DevOps, and Bitbucket.
Frontier models, libraries, CVEs, and API IDs ship on a weekly cadence now. A reviewer that cannot look anything up will sound completely sure while being weeks behind. Previously, a review might flag a brand-new fallback chain as using "nonexistent" model IDs simply because those models shipped in the weeks before the review, resulting in confident, stale feedback.
With search grounding in Cloud 2.2.0, when a finding depends on what exists today—like a new model name, a package version, or a deprecation—the review can check current vendor docs and live sources instead of inventing a story from memory. It combines full-context repo awareness with live web facts.
Nothing changes in how you run a review: open a PR/MR on your platform of choice, pick a cloud model, and search grounding handles the rest automatically (local reviews remain strictly local). Check out the release details at https://thinkreview.dev/blog/2026-08-23-search-grounding-thinkreview-cloud. I'd love to hear your thoughts or feedback on handling stale AI context in your code review workflows!