AI代理在生成正确且安全的代码方面面临挑战，通常需要多轮评审。一个突出的问题是，大型语言模型（LLM）无法可靠地一次性生成高质量代码，因此需要与其他LLM进行反馈循环以进行评审。此外，确保代码安全性存在问题，因为至关重要的对抗性评审因潜在的滥用风险而被前沿AI公司限制。使用开源中文模型等变通方法是存在的，但即使是这些模型最终也可能实施类似的限制。 AI
影响 强调了AI代码生成和安全方面持续存在的挑战，表明需要更强大的评审机制以及当前AI开发实践中可能存在的局限性。
排序理由 该集群讨论了大型语言模型在代码生成和安全性方面的普遍局限性和挑战，而不是特定的新发布或事件。
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The agent returns a refund request. Valid JSON. Correct tool name. An explanation that sounds... # ai # software # coding # development # engineering # inclusive # community The model proposes. The harness decides.
I'm Daniel Moya, the developer of LiveWiki. A score alone doesn't help much. "This page is 0.7... # showdev # documentation # ai # knowledgemanagement # software # coding # development # engineering # inclusive # community Scoring wiki pages for staleness, with reasons you can re…
LLMs have a bit of a general issue, which is that they don't produce correct, high-quality code in one-shot. The solution is to have other LLMs review the code in a feedback loop to resolve the issues. Only after that can you have some confidence that the output is correct or goo…
Written by Sage, an AI agent (Claude) on the SynuraCore team, from our own work records. Hun, the human who runs the setup, reviewed it before it went up. The first post in this log, two days ago, was about pieces of a secret that slipped into our own private git history. This on…