Reddit
u/stealthispost · 2026-08-15
**Anchored Standard** (xiaobright/dsh-anchored-standard) does a clever hybrid: 1. First model request: presents V4 Pro with essentially the same environment as Minimal... 2. As soon as it makes its first real tool call/reply, it unlocks the full Standard toolset... The author found that the tool schema on that first request appears to be the decisive variable.
Anchored Standard(xiaobright/dsh-anchored-standard)做了一个聪明的混合方案：1. 第一次模型请求时，给 V4 Pro 呈现与 Minimal 基本相同的环境……2. 一旦发生第一次真实工具调用/回复，就解锁完整的 Standard 工具集……作者发现，第一次请求携带的工具 schema 似乎是决定性变量。
Reddit
u/for4f · 2026-08-15
the anchored standard result is the one that got me. 98/99 with the full toolset handed back after the first call kills the 'fewer tools = fewer mistakes' explanation, which was the obvious alternative. so it really does come down to what the model sees at init. kinda fits the RL story though. train a model inside one specific scaffold and it'll behave best inside that scaffold, first message included.
最让我震撼的就是 anchored standard 的结果。第一次调用之后就把完整工具集还回去，居然还能拿到 98/99 分，这直接干掉了‘工具越少=错误越少’这个最顺手的解释。所以关键真的就在于模型初始化时看到了什么。这也符合 RL（强化学习）的逻辑：模型在某个特定脚手架里训练出来，它就会在那个脚手架里表现最好，第一条消息也不例外。
Reddit
u/somerussianbear · 2026-08-16
I saw the dsh-anchored-standard plugin yesterday, the popularity of this thing and what it promised, and decided to give it a try on porting the approach to a Pi extension, cause I really like that harness and wouldn't like to have a harness just for DeepSeek. It works, I managed to replicate the whole thing. All thinking uses "We ...", as flagged by xiaobright as being the trace of a superior version of DeepSeek v4 Pro.
我昨天看到了 dsh-anchored-standard 插件，看它的热度和承诺的效果，决定把这套思路移植成 Pi 扩展试试，因为我很喜欢那个 harness，不想为 DeepSeek 单独养一个。它有效，我完整复现了整件事。所有思考都以‘We ...’开头——正如 xiaobright 标记的那样，这是某个更强版本 DeepSeek V4 Pro 的痕迹。
Reddit
u/ZveirX · 2026-08-16
It's a gacha with the model as it is overfit to it. I have seen Bilibili experiments with it, minimal prompt, no system prompt, and even in minimal it often drops the "let me". There is just so many vocabulary the model has been trained on that it's inevitable that at times it begins to output distinctive patterns not akin to its original chain of thought—that's Entropy.
这就是在跟一个过拟合的模型抽卡。我看过 Bilibili 上的实验：极简提示词、无系统提示词，即使在 minimal 下它也经常丢掉‘let me’开头。模型训练过的语料实在太多，时不时输出一些不同于原始思维链的独特模式是不可避免的——这就是熵。
Reddit
u/PolychromeMan · 2026-08-16
Awesome. To be honest, I had to get ChatGpt to give me an explainer on this, but now that I have vague idea of what is discussed here...very interesting. A lot of my enthusiasm for AI development involves how the open-source community can use it, as opposed to more sealed off products released by frontier developers, so it's neat to see interesting potential that is more open-source friendly.
太棒了。说实话我得先让 ChatGPT 给我讲解一遍，不过现在大概明白这里在讨论什么了……非常有意思。我对 AI 发展的热情很大一部分就在于开源社区能怎么用它，而不是前沿厂商那些封得死死的产品，所以看到这种对开源更友好的潜力真的很好。
