Topic · 7 articles
KillSwitch's interpreter walks the process tree, works out it is talking to a coding agent, then returns a fake error about drawing a pelican instead of the real parse failure.
Anthropic made claude.ai 3x faster in two weeks by putting a model inside a measure-build-steer loop. An eval-grounded look at what the sprint proves about agentic workflows.
Teams drowning in AI-generated code often let an LLM review the LLM's own patches. Amazon's judge-correlation work shows why that misses real defects.
Eval-grounded comparison of AI code review tools for large teams with many repositories: cross-repo context, verification, permission boundary.
The volume of AI-generated code is rising faster than review capacity. The fix starts in evaluation design: don't let the model that wrote the code also judge it.
When AI writes 30% of your lines, patch-text review hits a ceiling. The fix is an execution layer, not a bigger reviewer.
The failure mode that matters in AI review is not missing a bug, it is fluent output that is structurally wrong and easy to trust. How to benchmark for that one specific failure mode.