AI coding agents don’t have a workspace. Here’s what that costs you.

AI 编程智能体缺乏工作空间：这让你付出了什么代价？

This week half my feed is people switching AI models. A new one drops, the old one gets throttled, everyone re-evaluates their setup. It happens every few weeks now. Watch those threads long enough and you notice the real complaint underneath. It is not the model. It is that switching costs you everything you had built up: your rules, your context, what the last agent figured out. All of it lived inside one tool, and now you are rebuilding it somewhere else. That is not a model problem. It is a workspace problem.

本周，我的信息流里有一半内容是关于人们更换 AI 模型的。每当新模型发布，旧模型被限流，大家就会重新评估自己的配置。现在这种情况每隔几周就会发生一次。只要你仔细观察这些讨论，就会发现其背后的真正痛点：问题不在于模型本身，而在于更换模型会让你失去之前建立的一切——你的规则、你的上下文，以及上一个智能体已经摸索出的成果。所有这些原本都存在于一个工具中，而现在你却不得不在另一个地方从头再来。这并非模型问题，而是工作空间的问题。

Your setup is not a workspace. Think about what an AI coding setup actually is today. You have a chat window. You have your codebase. Maybe a terminal, maybe an IDE extension. And that is it. Nothing sits above them holding the work together. So where do your decisions live? The reasons you chose one approach over another? What you already tried that did not work? What another agent, in another window, concluded an hour ago? Nowhere durable. It is scattered across chat histories that each tool keeps to itself, and most of it is gone the moment you close the tab. Your files are shared. Your thinking is not.

你的配置并非一个“工作空间”。想想当今的 AI 编程环境到底是什么样的：你有一个聊天窗口，有代码库，或许还有一个终端或 IDE 插件。仅此而已。没有任何东西能统筹这些工具并将工作内容串联起来。那么，你的决策存在哪里？你选择某种方案而非另一种方案的原因是什么？你已经尝试过哪些行不通的方法？另一个智能体在一小时前在另一个窗口得出了什么结论？这些信息没有持久的归宿。它们散落在各个工具各自的聊天记录中，一旦你关闭标签页，大部分信息就随之消失了。你的文件是共享的，但你的思考过程却不是。

Your files are shared already. Claude Code and Codex can both read them. But your files are only the code. They are not the thinking around the code, and the thinking is the part that is expensive to rebuild. This is why a better model never quite fixes it. A smarter agent is still an agent working alone, with no idea what the others did. Give it your whole repo and it still opens cold on everything that is not in the repo.

你的文件虽然已经实现了共享（Claude Code 和 Codex 都能读取它们），但文件仅仅是代码本身。它们不包含围绕代码产生的思考，而这些思考才是重建成本最高的部分。这就是为什么更好的模型也无法彻底解决问题的原因。一个更聪明的智能体依然是孤军奋战，它完全不知道其他智能体做了什么。即使你把整个代码库交给它，对于库之外的信息，它依然是一无所知。

What a workspace actually gives you. What would help is a workspace. Not a chat window. A place the work lives, that every agent reads from and writes to. Concretely, that means a few things. One shared memory. What one agent learns, the next already knows, even on a different model. You stop re-explaining your project every time you open a new chat. Visible work. Every task, decision and change is something you can see, not something buried in a chat transcript you will never scroll back through. Many agents, one project. Claude, Claude Code, ChatGPT and Codex working from the same source of truth, so they stop redoing each other’s work. Continuity. Close your laptop, come back tomorrow, and it picks up exactly where you left off. None of that is about the model. It sits above the model, which is exactly why no model release touches it. The model will keep changing.

一个真正的工作空间能为你提供什么？我们需要的是一个工作空间，而不是聊天窗口。它是一个承载工作内容的地方，供所有智能体读取和写入。具体来说，这意味着几点：统一的共享记忆。一个智能体学到的东西，下一个智能体也能知晓，即使它们基于不同的模型。你无需在每次开启新对话时都重新解释项目。工作可视化。每一项任务、决策和变更都是可见的，而不是埋没在永远不会回看的聊天记录中。多智能体协作。Claude、Claude Code、ChatGPT 和 Codex 基于同一个事实来源工作，从而避免重复劳动。连续性。合上电脑，明天回来，工作能从上次中断的地方无缝衔接。这些都与模型无关，它们位于模型之上，这正是为什么任何模型更新都无法解决这些问题的原因。模型会不断更迭。

This is what we are building Memeri to be. Not another memory tool. The workspace your AI coding agents never had, so the next time you switch models, or run three agents at once, the work comes with you instead of starting over. The model you use will keep changing. Your workspace should not have to.

这就是我们打造 Memeri 的初衷。它不是又一个记忆工具，而是你的 AI 编程智能体从未拥有过的工作空间。这样，当你下次更换模型或同时运行多个智能体时，你的工作成果会随你迁移，而不是被迫从零开始。你使用的模型会不断变化，但你的工作空间不应如此。