Show HN: Adversarial Code Review paired agents, zero noise,validated findings
quality 3/10 · low quality
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AI Summary
A code review system using two adversarial LLM agents—one finding vulnerabilities, one challenging findings with counter-evidence—to overcome the problem of LLMs agreeing with themselves and to produce zero-noise validated security findings.
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Author here. We built this because one AI reviewing code and checking its own work just agrees with itself — the research confirms LLMs can't reliably self-correct on hard reasoning.
The fix: split it into two agents with opposed goals. A reviewer agent finds problems.
A dev agent tries to disprove each finding with specific counter-evidence from the codebase.
Three verdicts: VALID, INVALID, AMBIGUOUS. Only what survives reaches your team.
Agents are auto-generated per service — point the skill at your repo, it scans your stack and produces the full set. ~30 min of human tuning per service to add tribal knowledge. Repo includes the shared preamble (quality constitution), reviewer/dev templates, and the auto-generation skill. Happy to answer questions.