Stronger AI Models Boosted CauterRule Scores But Left Core Safety Gaps Unsolved

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Developer tool CauterRule, now available on GitHub and PyPI, was field-tested across 4 AI models, 13 corpus types, and 394 trajectories to evaluate how much model quality affects product reliability. The best-performing model, meta-llama/llama-3.1-8b-instruct, achieved 123 passes out of 392 candidates — the highest of any model tested — yet still left 69% of outputs either inconclusive or failing. Cloud models outperformed local ones in parse reliability and overall pass rates, but neither cloud nor local models resolved the tool's underlying safety-related failures. The findings suggest that while upgrading to a stronger model yields measurable gains, it does not substitute for fixing deeper product-level problems. CauterRule's v0.1.0 release includes 25+ CLI commands, an MCP server, and seven export formats, installable via pip.

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