"Rubber Duck caught that the proposed scheduler would start and immediately exit, running zero jobs-and that even if fixed, one of the scheduled tasks was itself an infinite loop."
"Rubber Duck caught a loop that silently overwrote the same dict key on every iteration. Three of four Solr facet categories were being dropped from every search query, with no error thrown."
"Rubber Duck caught three files that all read from a Redis key which the new code stopped writing. The confirmation UI and cleanup paths would have been silently broken on deploy."
Rubber Duck, when paired with Claude Sonnet 4.6 and GPT-5.4, significantly improves resolution rates for complex coding problems. It closes 74.7% of the performance gap with Claude Opus 4.6. Rubber Duck is particularly effective on difficult problems that involve multiple files and extensive steps. It outperforms the Sonnet baseline by 3.8% and 4.8% on the hardest problems. Specific examples include catching architectural issues, one-liner bugs, and cross-file conflicts, demonstrating its utility in real-world coding scenarios.
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