{
  "id": 17381930,
  "title": "What smart-contract audits taught me about evaluating LLMs",
  "url": "https://urgent.news/2026/10/31/what-smart-contract-audits-taught-me-about-evaluating-llms",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-31T10:25:50.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/piyushmali/what-smart-contract-audits-taught-me-about-evaluating-llms-1em1"
  },
  "original_language": "en",
  "account": "After spending years participating in smart contract audit contests, the author transitioned to building AI production systems and discovered that the skills they honed during audits are highly beneficial in this new context. The experience taught them the importance of evaluating LLM systems differently by focusing on worst-case scenarios rather than just happy paths, and by using invariants, code checks, and hostile input cases in their evaluation process.",
  "summary": "Before I worked on LLM systems, I spent a lot of my time in smart-contract audit contests. I entered more than twenty of them, on platforms like Sherlock, Code4rena and Immunefi. The setup is always similar: a team publishes its contracts, auditors get a limited window to read them, and you are rewarded only for issues you can prove. That work trains one habit above all others. You stop asking…",
  "key_points": [
    "Author's experience in smart contract audit contests",
    "Transitioned to building AI production systems",
    "Emphasized worst-case scenario evaluation for LLMs"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}