n8n - 启幕AI | 全球 AI 新闻、产品发布与一手编译

快来看，n8n更新了！何时语义分块优于固定大小切分一手编译

Learn how semantic chunking improves RAG performance by preserving context, increasing retrieval accuracy, reducing token costs, and improving AI responses.

n8n

qimuai 发布于 2026-8-7 22:01

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快来看，n8n更新了！您的AI项目终将失败。欢迎来到“第二天”问题。一手编译

The questions to ask BEFORE you start building, to make sure it keeps working.

n8n

qimuai 发布于 2026-8-4 22:01

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快来看，n8n更新了！LLM安全：如何保护生产环境中的AI工作流一手编译

Explore LLM security threats, from prompt injection to data poisoning. Discover actionable best practices for reliable, auditable enterprise deployment.

n8n

qimuai 发布于 2026-8-1 22:01

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快来看，n8n更新了！RAG与智能体RAG：架构、权衡及如何选择一手编译

Classic RAG hits a wall on multi-hop and ambiguous queries. Learn how agentic RAG turns retrieval into a control loop, and when the tradeoffs are worth it.

n8n

qimuai 发布于 2026-7-29 22:01

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快来看，n8n更新了！微调与RAG：在生产级大语言模型中分别何时使用一手编译

Explore fine-tuning versus RAG to understand how they differ, when each approach works best, and why many production LLM systems use both.

n8n

qimuai 发布于 2026-7-16 22:01

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快来看，n8n更新了！我应该使用 Claude Code 还是 n8n？一手编译

Should you build it with Claude Code or n8n? Five questions that let you answer for your own situation, from someone who uses both daily.

n8n

qimuai 发布于 2026-7-15 22:01

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快来看，n8n更新了！在 n8n 中构建由 AI 驱动的事件响应工作流一手编译

Build an AI-powered incident response workflow with n8n. Combine RAG, threat intelligence, and historical incidents to accelerate SOC investigations.

n8n

qimuai 发布于 2026-7-9 22:01

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快来看，n8n更新了！AI安全监控：风险、检测与自动化响应一手编译

Learn how AI security monitoring works from both sides. Discover the unique AI risks and the strategies engineers use to automate detection and response.

n8n

qimuai 发布于 2026-7-7 22:01

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快来看，n8n更新了！LLM工具调用中的错误处理架构指南一手编译

This guide to LLM tool calling error handling covers how to classify failures, implement smart retries, design fallbacks, and wire circuit breakers.

n8n

qimuai 发布于 2026-7-4 22:01

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快来看，n8n更新了！智能体AI设计模式：从架构到生产一手编译

This practical guide to agentic AI design patterns covers validation, governance, context management, error recovery, and cost control in production.

n8n

qimuai 发布于 2026-7-2 22:01

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qimuai 11 个月前
We’re opening an office in Seattle

Come join us!

https://t.co/E9BY2xFU2j
qimuai 11 个月前
Get through PDFs effortlessly with Grok.

Highlight any section and click "Explain" to have Grok help you understand it.

Or click "Quote" to ask a specific question. https://t.co/epQt4Nypfw
qimuai 11 个月前
i have had the strangest experience reading this: i assume its all fake/bots, even though in this case i know codex growth is really strong and the trend here is real.

i think there are a bunch of things going on: real people have picked up quirks of LLM-speak, the Extremely Online crowd drifts together in very correlated ways, the hype cycle has a very "it's so over/we're so back" extremism, optimization pressure from social platforms on juicing engagement and the related way that creator monetization works, other companies have astroturfed us so i'm extra sensitive to it, and a bunch more (including probably some bots).

but the net effect is somehow AI twitter/AI reddit feels very fake in a way it really didnt a year or two ago.
qimuai 11 个月前
Jakub and Szymon:

https://t.co/6qrVNp5Q3u
qimuai 11 个月前
有时候会觉得，写的这些东西都太浅显了，发到朋友圈会不会被加的大V们嘲笑？如果有这个想法，肯定是做不好自媒体的。

除非大V们是你的目标用户，否则永远不要让其他无关的人左右你的选题。

你应该只关注，你的目标用户需要什么。
qimuai 11 个月前
人类在学习新知识或解决新问题时，往往不是直接“改写大脑参数”，而是通过“记笔记”或“自我提醒”的方式，将经验和策略以显式的形式保留下来。

例如，当你遇到某类问题时，会总结出“下次遇到类似情况可以这样做”的经验。这种方式更像是在不断编辑自己的“系统提示”，而不是每次都重新训练大脑。
qimuai 11 个月前
此时常有人问该选哪个模型。我的答案永远是：工作环境允许的最强模型。我们不是大规模使用AI，所以即使工程角度认为"贵"或"慢"，也要选最好的大脑。（唯一限制应是公司对敏感信息上传的规定。）
qimuai 11 个月前
成功与思考伙伴（人或机器）协作的关键在于取其精华去其糟粕
qimuai 11 个月前
引用David Lieb（YC合伙人、Google Photos创始人）的话："你的直觉是世界上最复杂的机器学习模型。"使用生成式AI时，用你的专业品味识别有价值的内容。
qimuai 11 个月前