Jerry Liu推文
关于AI编码工具切换成本的思考
作者认为实验室有机会在Claude Code、Codex、Grok Bot等工具之间设置更高的切换成本。每当新模型发布时，用户因已有技能、习惯、系统指令和项目设置而难以切换。尽管作者维护外部wiki和工件，但对话历史的细微差别会丢失。若启用记忆功能，工具可索引并记住上下文，减少重复输入。核心痛点在于如何高效提供正确上下文，答案可能是构建良好的自改进上下文图。
译文
我认为实验室确实有机会在Claude Code/Codex/Grok Bot等工具之间设置更高的切换成本。每当新模型发布时，我已经因为现有的技能、习惯、系统指令、项目设置等因素，而对在不同应用之间切换感到一种惯性阻力。我确实为每个项目维护了一个外部生成的wiki/工件，所以从技术上讲，我可以让任何应用指向同一个wiki并获得类似的结果，但这会丢失对话历史中的细微差别。如果开启了记忆功能，这些应用会索引并记住你的上下文，以便在后续会话中使用，从而免去每次新会话都要重新输入大量上下文的麻烦——这些工具最大的痛点就是如何每次都高效地喂入正确的上下文。也许答案真的在于构建一个良好的、自我改进的上下文图谱。
i think there's a real opportunity for labs to bake in even higher switching costs between claude code/codex/grok bot etc. whenever a new model release comes out, i already feel an inertia to switch between apps because of my existing skills, routines, system instructions, project setup, and more. i do maintain an externally generated wiki / artifacts for each project, so technically i could point any app at the same wiki and get similar results, but this loses the nuances of conversation history. if memory is turned on, then these apps index and remember your context for subsequent sessions, alleviating the need to retype massive amounts of context for new sessions the biggest pain point for any of these tools is figuring out how to efficiently feed it the right context every time. maybe the answer really is around building a good, self-improving context graph