一位Mastodon用户分享了他们使用LLM生成AI的经验，指出它没有提供处理磁盘已满等实际解决方案。他们将这一局限性归因于AI的训练数据，他们认为这些数据包含大量“垃圾”信息，呼应了“垃圾进，垃圾出”的旧计算原则。 AI
排序理由 用户对LLM局限性的看法，并非重大的行业事件。
在 Mastodon — mastodon.social 阅读 →
AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →
用户发现LLM生成的AI缺乏实际编码建议
本文如何被排名
- Signal score
- 0 / 100
- Composite score across the factors below. Higher = stronger signal that this story matters right now.
- Newsworthiness bucket
- Meme
- 用户对LLM局限性的看法，并非重大的行业事件。
- Source corroboration
- Single-source cluster
- Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
- Topics
- opinion, other
- Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
- AI-industry relevance
- Standard
- On-topic for AI-industry coverage; kept in the public index.
- Story freshness
- Breaking (< 6h)
- Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.
完整方法见我们的编辑标准。
报道来源 [1]
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我承认，我自己也用过一些LLM生成的AI。但它没有一次建议我考虑修复代码以处理磁盘已满的情况。为什么？因为是*人*。
@ josh I have used some LLM-gen-AI myself, I admit. Not once has it suggested I think about fixing the code to handle disk-full scenarios. Why? Because *people* don't think about it! Going back to when I started working with computers in the 1980s, this principle has applied: “Ga…