Claude AI Recovers 99.94% of Data from Corrupted 12TB BTRFS Array

A developer reported recovering 99.94% of data from a corrupted 12TB BTRFS array using Claude AI after native recovery tools made the situation worse. The corruption occurred during a forced reboot while swapping kernels on a data server with a 3×4TB disk BTRFS array.

What Happened

The developer tried BTRFS native tools first, but every recovery attempt made things worse. Claude diagnosed the problem as a destroyed index table at around 80%, with every node at that percentage corrupted, resulting in 80% or more data loss (over 8 terabytes).

Claude's Approach

Without a backup for the fs_tree, Claude proposed diving in to map the entire binary tree in memory, make predictions, and build nodes manually. The developer gave permission, with the alternative being to resign to 8TB of data loss.

Claude worked on the problem over several days, discussing binary arrays and hard disk terminology that the developer (a 20-year software engineer) found unfamiliar. The AI produced an essay report explaining findings and solutions.

Results

Claude managed to recover 99.94% of the data with a full tree rebuilt from scratch with 0 errors, 100% functional. Only 7MB of trash files were lost from 8.4 terabytes of data. The developer published a case study on the BTRFS GitHub repository.

This demonstrates how AI coding assistants can tackle complex, low-level filesystem recovery problems that standard tools aren't designed to handle, particularly when dealing with fatal failures in BTRFS arrays.