<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>方法论 on 安知生 angelife</title><link>https://angelife.github.io/tags/%E6%96%B9%E6%B3%95%E8%AE%BA/</link><description>Recent content in 方法论 on 安知生 angelife</description><generator>Hugo -- 0.147.4</generator><language>zh-cn</language><copyright>2009–2026 angelife / 安知生</copyright><lastBuildDate>Tue, 26 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://angelife.github.io/tags/%E6%96%B9%E6%B3%95%E8%AE%BA/index.xml" rel="self" type="application/rss+xml"/><item><title>AI 原生知识系统：从散乱材料到长期作品</title><link>https://angelife.github.io/series/chan-shi-lu/ai-native-knowledge-system/</link><pubDate>Tue, 26 May 2026 00:00:00 +0000</pubDate><guid>https://angelife.github.io/series/chan-shi-lu/ai-native-knowledge-system/</guid><description>这篇文章讨论如何把聊天记录、旧笔记、网页摘录、读书笔记和临时灵感，通过 AI、Obsidian、Hugo 与 Git 整理成可检索、可复盘、可发布的长期知识资产。</description></item><item><title>AI 补印：把 AI 当作后天系统能力</title><link>https://angelife.github.io/series/ai-bu-yin/ai-as-jinyin/</link><pubDate>Tue, 26 May 2026 00:00:00 +0000</pubDate><guid>https://angelife.github.io/series/ai-bu-yin/ai-as-jinyin/</guid><description>AI 补印不是炫技，而是把 AI 当作后天补足认知、资料、表达和复盘能力的系统工具，让散乱经验能够持续沉淀。</description></item><item><title>信息质量判断框架：先看来源，再看叙事</title><link>https://angelife.github.io/series/information-judgment/information-quality-framework/</link><pubDate>Tue, 26 May 2026 00:00:00 +0000</pubDate><guid>https://angelife.github.io/series/information-judgment/information-quality-framework/</guid><description>判断信息质量不能只看观点是否顺耳，而要看来源、证据、叙事方式、利益位置和纠错机制。真正好的信息源，经得起追问。</description></item><item><title>儒家为体，AI 为用</title><link>https://angelife.github.io/series/confucian-framework/confucianism-as-base-ai-as-use/</link><pubDate>Tue, 26 May 2026 00:00:00 +0000</pubDate><guid>https://angelife.github.io/series/confucian-framework/confucianism-as-base-ai-as-use/</guid><description>技术可以提高效率，但不能替人安放价值。儒家为体、AI 为用，是让工具服务于正见，而不是让效率放大偏见。</description></item></channel></rss>