Merge pull request #3418 from deepseek-harness/fix/ptc-note-cloudflare-link
docs: restore Cloudflare's Code Mode name and blog link in PTC Agent Note
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# side as of the last confirmed-consistent state. Both languages carry equal authority;
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# side as of the last confirmed-consistent state. Both languages carry equal authority;
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# after editing either side, bring the other along and re-record with:
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# after editing either side, bring the other along and re-record with:
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# pnpm run verify-translation-pairing --write .agents/notes/implemented/feature/2026-06-15-ptc.md
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# pnpm run verify-translation-pairing --write .agents/notes/implemented/feature/2026-06-15-ptc.md
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2026-06-15-ptc.md: 5b3971c33df4be46c8a466e564ac86ba6454663a
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2026-06-15-ptc.md: c0a0b3ad54716761a74eed8b54c11480e9ca41bc
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2026-06-15-ptc.zh.md: fe44d8a0e97913998fc001c92f632f493888c720
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2026-06-15-ptc.zh.md: b774fc001509f30d41d1c519f344f67d797c2cbf
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@ -10,7 +10,7 @@ In the registry's native presentation, the agent loop advertises every visible c
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For multi-step tool work this is token-heavy and serial. The model cannot compose tools — loop over a result set, branch on an intermediate value, fan out, post-process — without a full model round-trip per call, and each round-trip drags the entire intermediate result back into context whether the model needs it or not.
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For multi-step tool work this is token-heavy and serial. The model cannot compose tools — loop over a result set, branch on an intermediate value, fan out, post-process — without a full model round-trip per call, and each round-trip drags the entire intermediate result back into context whether the model needs it or not.
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Cloudflare's [PTC mode](https://blog.cloudflare.com/ptc/) proposes an alternative grounded in a simple observation: LLMs are better at writing code than at emitting tool calls, because they have seen millions of lines of real code and comparatively few contrived tool-calling traces. Instead of one tool call per step, the model writes a TypeScript program against a generated API over the tools, the program executes in a sandboxed runtime, and the model curates what comes back — only what it prints or returns — instead of every intermediate result.
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Cloudflare's [Code Mode](https://blog.cloudflare.com/code-mode/) proposes an alternative grounded in a simple observation: LLMs are better at writing code than at emitting tool calls, because they have seen millions of lines of real code and comparatively few contrived tool-calling traces. Instead of one tool call per step, the model writes a TypeScript program against a generated API over the tools, the program executes in a sandboxed runtime, and the model curates what comes back — only what it prints or returns — instead of every intermediate result.
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Tool presentation belongs to the registry that owns tool visibility: implementing a second presentation as an after-the-fact waterfall transform would make correctness depend on listener order and fight [reconstructable requests](../architecture/2026-07-05-reconstructable-requests.md). The execution substrate is also part of the foundation rather than a placeholder: Node `worker_threads` provides a separate isolate, an empty environment, heap caps, and termination of a hot synchronous loop, while fitting the harness's existing trust model (§Trust posture).
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Tool presentation belongs to the registry that owns tool visibility: implementing a second presentation as an after-the-fact waterfall transform would make correctness depend on listener order and fight [reconstructable requests](../architecture/2026-07-05-reconstructable-requests.md). The execution substrate is also part of the foundation rather than a placeholder: Node `worker_threads` provides a separate isolate, an empty environment, heap caps, and termination of a hot synchronous loop, while fitting the harness's existing trust model (§Trust posture).
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@ -10,7 +10,7 @@ Status: implemented
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对于多步工具操作,这种方式 token 开销大且串行。模型无法组合工具——遍历结果集、根据中间值分支、扇出、后处理——每次调用都需要一次完整的模型往返,而每次往返都会把完整的中间结果拖回上下文,不管模型是否需要。
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对于多步工具操作,这种方式 token 开销大且串行。模型无法组合工具——遍历结果集、根据中间值分支、扇出、后处理——每次调用都需要一次完整的模型往返,而每次往返都会把完整的中间结果拖回上下文,不管模型是否需要。
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Cloudflare 的 [PTC mode](https://blog.cloudflare.com/ptc/) 提出了一种替代方案,基于一个简单的观察:LLM(大语言模型)编写代码的能力优于发出工具调用,因为它们见过数百万行真实代码,而人为构造的工具调用 trace 相对很少。模型不再每步发出一次工具调用,而是针对工具生成的 API 编写一段 TypeScript 程序,程序在沙箱运行时中执行,模型只筛选返回的内容——仅限它 print 或 return 的部分——而非所有中间结果。
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Cloudflare 的 [Code Mode](https://blog.cloudflare.com/code-mode/) 提出了一种替代方案,基于一个简单的观察:LLM(大语言模型)编写代码的能力优于发出工具调用,因为它们见过数百万行真实代码,而人为构造的工具调用 trace 相对很少。模型不再每步发出一次工具调用,而是针对工具生成的 API 编写一段 TypeScript 程序,程序在沙箱运行时中执行,模型只筛选返回的内容——仅限它 print 或 return 的部分——而非所有中间结果。
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工具呈现属于掌管工具可见性的注册表:如果把第二种呈现方式实现为事后的 waterfall(瀑布式事件)变换,正确性将依赖监听器顺序,并与[可重建请求](../architecture/2026-07-05-reconstructable-requests.zh.md)冲突。执行基底同样属于基础设施而非占位实现:Node `worker_threads` 提供独立 isolate、空环境、堆上限以及对热同步循环的终止能力,同时契合 harness 既有的信任模型(§信任姿态)。
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工具呈现属于掌管工具可见性的注册表:如果把第二种呈现方式实现为事后的 waterfall(瀑布式事件)变换,正确性将依赖监听器顺序,并与[可重建请求](../architecture/2026-07-05-reconstructable-requests.zh.md)冲突。执行基底同样属于基础设施而非占位实现:Node `worker_threads` 提供独立 isolate、空环境、堆上限以及对热同步循环的终止能力,同时契合 harness 既有的信任模型(§信任姿态)。
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