将ChatGPT的“保存记忆”转变为一个持久的、自我更新的运行时工具
大多数人认为ChatGPT的保存记忆功能只是一个被动的笔记记录工具——一个模型可以在会话之间“记住”的静态知识库。
我一直在尝试将其重新构建为更像一个主动的运行环境——在这个环境中，记忆条目本身包含程序规则，模型会在每次交互中自动遵循这些规则，而无需我重新提示。
例如，我已经配置它运行一个实时的、持久的令牌计数器，该计数器在每次回复后更新，跟踪累计总数，计算成本和能量使用，并始终以锁定格式显示。它从一个固定的基线开始，在每次交互中扣除使用量，并在整个聊天会话中持续存在而不打断序列。
这有效地将记忆从一个静态数据库转变为一个状态化的计算层，存在于对话引擎内部——没有API接口，没有扩展，没有服务器，没有脚本。所有的操作都是通过内部的记忆指令和精心设计的提示完成的。
这开启了许多可能性：
• 每次交互更新的内部分析仪表板。
• 不需要手动重述的多步骤持久工作流程。
• 在交互中存活并适应的嵌入式“代理”。
这是一个小但根本的转变——让ChatGPT的记忆不仅仅是记住某些东西，而是能够执行某些操作。
还有其他人尝试过这个想法吗？我对将记忆作为模型内自动化层的更广泛影响感到好奇。
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Most people think of ChatGPT’s Saved Memory as a passive note-taking feature — a static knowledge store the model can “remember” between sessions.
I’ve been experimenting with re-tooling this into something more like an active runtime environment — where the memory entry itself contains procedural rules that the model follows automatically, every single exchange, without me re-prompting.
For example, I’ve configured it to run a live, persistent token counter that updates after every reply, tracks cumulative totals, calculates cost and energy usage, and always displays in a locked format. It starts at a fixed baseline, deducts usage on each turn, and persists across the entire chat session without breaking the sequence.
This effectively transforms memory from a static data vault into a stateful computation layer that lives inside the conversation engine — no API hooks, no extensions, no servers, no scripts. It’s all done internally, purely through memory instructions and careful prompt engineering.
This opens up a lot of possibilities:
• Internal analytics dashboards that update every turn.
• Multi-step, persistent workflows that don’t require manual restating.
• Embedded “agents” that survive and adapt across exchanges.
It’s a small but fundamental shift — making ChatGPT’s memory do something, not just remember something.
Has anyone else played with this idea? I’d be curious about the broader implications for using memory as an in-model automation layer.