From 7cfcaae2fa908921dfe565d23ee4ca86e42a40a9 Mon Sep 17 00:00:00 2001 From: Wonderforge <74642251+Wonderforge-Lab@users.noreply.github.com> Date: Sun, 6 Sep 2026 18:02:17 -0700 Subject: [PATCH 1/2] =?UTF-8?q?Explain=20LabNote=E2=80=99s=20continuity=20?= =?UTF-8?q?and=20audit=20boundaries?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/WHY_LABNOTE.md | 77 ++++++++++++++++++++++++++++++++++++++------- 1 file changed, 66 insertions(+), 11 deletions(-) diff --git a/docs/WHY_LABNOTE.md b/docs/WHY_LABNOTE.md index 3814905..99f245d 100644 --- a/docs/WHY_LABNOTE.md +++ b/docs/WHY_LABNOTE.md @@ -1,8 +1,29 @@ -# LabNote alongside context files and model memory +# LabNote alongside context files, agent memory and observability -LabNote keeps selected project continuity in visible files. It complements a -model's context window and repository instruction files; it does not replace -them. +LabNote is a human-controlled project ledger for work that moves between AI +assistants, coding agents, chats and people. It keeps selected project +continuity in visible Markdown artifacts and structured JSON registry records. + +Its job is not to enlarge a model's context window, automatically remember +everything, or trace every model call. Its job is to keep the project thread: +the sources, handoffs, responses, reviews, decisions and next actions that +people decide should travel forward. + +## The record is selective + +Not every message belongs in a durable project record. LabNote keeps the +stepping stones that make the next piece of work understandable: + +- a source or incoming packet; +- a response, contribution or draft; +- a review or correction; +- a decision or signoff; and +- the next action or handoff. + +That makes the retained trail smaller and easier to inspect than a full +transcript or a growing context blob. An AI can help prepare the record, but +the workspace does not silently harvest conversations: the human decides what +belongs and what needs review. ## Model context is not project continuity @@ -11,26 +32,60 @@ by itself, decide what should remain important after the session, show the next tool why a decision was made, or create a selective record that a human can inspect. -LabNote does not enlarge a model’s native memory or silently capture your -conversations. A human or an AI session deliberately writes the record. That is -why the trail can be checked, corrected, reviewed, and carried to another -tool. +LabNote does not enlarge a model's native memory. A human or an AI session +deliberately writes the record, so the trail can be checked, corrected, +reviewed and carried to another tool. Context length is useful. Project continuity is a separate job. +## Agent memory is a different trade-off + +Some AI-memory systems automatically extract, compress, index and retrieve +information across interactions. That can be useful when an agent needs +automatic recall. + +LabNote takes a different route. Basic ledger use needs no LabNote background +service, database or model API key, and it does not make an automatic memory +store. It keeps the selected project record in the repository, where the +people running the project can see and govern it. + +These approaches can coexist. Use automatic memory when automatic retrieval is +the need; use LabNote when the project needs a deliberate, visible handoff and +decision trail. + ## Context files set local rules -Files such as `AGENTS.md` or `CLAUDE.md` are useful ways to tell an AI about a -repository: where important files are, how to run tests, and what local rules +Files such as `AGENTS.md` or `CLAUDE.md` are useful ways to tell an AI about +a repository: where important files are, how to run tests and what local rules apply. LabNote complements them. Its job is to route ongoing project work: where a session begins, what it should read, where it may leave work, how that work is -reviewed, and when the session should stop and ask. +reviewed and when the session should stop and ask. A context file tells an AI what kind of repository it is in. LabNote gives it a route through the work happening there. +## An audit trail is not full observability + +AI-observability tools can trace prompts, model calls, tool calls, timing and +token use. They answer runtime questions such as “what did this system call?” + +LabNote records a different layer: the project artifacts people choose to +retain, and the review, decision and handoff around them. It is a +project-level, human-controlled audit trail—not a claim to capture every model +call or every action automatically. + +## Rails make the routine legible + +The rails do not make a model deterministic, smarter or infallible. They make +routine coordination work clearer: a known entry, a bounded reading route, +clear write targets and defined points to stop and ask. + +That means an incorrect contribution can remain visible as part of the record: +it can be reviewed, corrected, rejected or superseded rather than quietly +becoming unexamined “memory.” + ## Use the smallest useful amount If a one-shot answer is enough, use the best tool available and get on with it. From 1b2a0559d21f8f10dbd0732045e22f2f123b4059 Mon Sep 17 00:00:00 2001 From: Wonderforge <74642251+Wonderforge-Lab@users.noreply.github.com> Date: Sun, 6 Sep 2026 18:02:47 -0700 Subject: [PATCH 2/2] Align Chinese continuity and audit guidance --- locales/zh-CN/docs/WHY_LABNOTE.md | 40 ++++++++++++++++++++++++++++--- 1 file changed, 37 insertions(+), 3 deletions(-) diff --git a/locales/zh-CN/docs/WHY_LABNOTE.md b/locales/zh-CN/docs/WHY_LABNOTE.md index 0867348..b5f8ee5 100644 --- a/locales/zh-CN/docs/WHY_LABNOTE.md +++ b/locales/zh-CN/docs/WHY_LABNOTE.md @@ -1,15 +1,37 @@ -# LabNote 与上下文文件和模型记忆 +# LabNote 与上下文文件、智能体记忆和可观测性 -LabNote 将经过选择的项目连续性保存在可见文件中。它与模型的上下文窗口和仓库指令文件互补，而不是取代它们。 +LabNote 是一个由人掌控的项目工作台账，适用于在 AI 助手、编程智能体、聊天会话和人之间流转的工作。它将经过选择的项目连续性保存在可见的 Markdown 成品和结构化 JSON 登记记录中。 + +它的职责不是扩大模型的上下文窗口、自动记住一切，或追踪每一次模型调用。它的职责是保留项目脉络：人们决定要向前传递的来源、交接、回复、审阅、决策和下一步行动。 + +## 记录是经过选择的 + +并非每一条消息都应进入持久的项目记录。LabNote 保留的是让下一段工作仍然可理解的踏脚石： + +- 来源或进入的任务包； +- 回复、贡献或草稿； +- 审阅或修正； +- 决策或签署；以及 +- 下一步行动或交接。 + +与完整对话记录或不断膨胀的上下文块相比，这让保留的轨迹更小、更容易检查。AI 可以协助准备记录，但工作台账不会悄悄收集对话：由人决定什么应被保留、什么需要审阅。 ## 模型上下文不等于项目连续性 更长的上下文窗口可以帮助模型在一次会话中阅读更多内容。但它本身不会决定什么应在会话结束后继续重要，不会向下一个工具说明为什么做出了某个决定，也不会自动留下人类可以检查的选择性记录。 -LabNote 不会扩展模型原生记忆，也不会悄悄捕获你的对话。记录由人类或 AI 会话有意写入。因此，这条轨迹可以被检查、修正、审阅，并带到另一个工具中。 +LabNote 不会扩展模型原生记忆。记录由人类或 AI 会话有意写入，因此这条轨迹可以被检查、修正、审阅，并带到另一个工具中。 上下文长度很有用。项目连续性是另一项工作。 +## 智能体记忆是一种不同的取舍 + +有些 AI 记忆系统会跨交互自动提取、压缩、索引和检索信息。当智能体需要自动回忆时，这会很有用。 + +LabNote 选择了另一条路径。基本台账使用不需要 LabNote 后台服务、数据库或模型 API 密钥，它也不会构建自动记忆库。它把经过选择的项目记录保存在仓库中，让运行项目的人可以看见并治理它。 + +这些方法可以共存。需要自动检索时使用自动记忆；项目需要有意、可见的交接和决策轨迹时使用 LabNote。 + ## 上下文文件设定本地规则 `AGENTS.md` 或 `CLAUDE.md` 之类的文件很适合向 AI 说明一个仓库：重要文件在哪里、怎样运行测试，以及有哪些本地规则。 @@ -18,6 +40,18 @@ LabNote 与它们互补。它的职责是路由持续进行的项目工作：会 上下文文件告诉 AI 它身处什么样的仓库。LabNote 给它一条穿过正在进行的工作的路径。 +## 审计轨迹不等于完整可观测性 + +AI 可观测性工具可以追踪提示、模型调用、工具调用、时序和 token 使用。它们回答的是运行时问题，例如：“这个系统调用了什么？” + +LabNote 记录的是另一层：人们选择保留的项目成品，以及围绕它们发生的审阅、决策和交接。它提供的是项目层面、由人掌控的审计轨迹；并不声称会自动捕获每一次模型调用或每一个动作。 + +## 轨道让例行协作清晰可见 + +这些轨道不会让模型变得确定、更聪明或不会出错。它们让例行协调工作更清楚：明确的入口、有限的阅读路径、清晰的写入位置，以及定义好的停下并询问的节点。 + +这意味着不正确的贡献也可以作为记录的一部分保留：它可以被审阅、修正、拒绝或取代，而不是悄悄变成未经检查的“记忆”。 + ## 使用最小但足够的量 如果一次性答案已经足够，就使用手边最好的工具并继续推进。如果普通文件夹已经足够，就使用普通文件夹。