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)做了一個聰明的混合方案：1. 第一次模型請求時，給 V4 Pro 呈現與 Minimal 基本相同的環境……2. 一旦發生第一次真實工具呼叫/回覆，就解鎖完整的 Standard 工具集……作者發現，第一次請求所帶的工具 schema 似乎是決定性變數。
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.
最讓我震撼的就是 anchored standard 的結果。第一次呼叫之後就把完整工具集還回去，居然還能拿到 98/99 分，這直接推翻了‘工具越少=錯誤越少’這個最順手的解釋。所以關鍵真的在於模型初始化時看到了什麼。這也符合 RL（強化學習）的邏輯：模型在某個特定鷹架裡訓練出來，它就會在那個鷹架裡表現最好，第一則訊息也不例外。
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 dsh-anchored-standard 外掛，看它的熱度和承諾的效果，決定把這套思路移植成 Pi 擴充套件試試，因為我很喜歡那個 harness，不想為 DeepSeek 單獨養一個。它有效，我完整重現了整件事。所有思考都以‘We ...’開頭——正如 xiaobright 標記的，這是某個更強版本 DeepSeek V4 Pro 的痕跡。
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.
這就是在跟一個過擬合的模型抽卡。我看過 Bilibili 上的實驗：極簡提示詞、無系統提示詞，即使在 minimal 下它也常常丟掉‘let me’開頭。模型訓練過的語料實在太多，偶爾輸出一些不同於原始思維鏈的獨特模式是不可避免的——這就是熵。
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。老實說我得先請 ChatGPT 幫我解說一遍，不過現在大概知道這裡在討論什麼了……非常有意思。我對 AI 發展的熱情有很大一部分在於開源社群能怎麼運用它，而不是前沿廠商那些封得死死的產品，所以看到這種對開源更友善的潛力真的很棒。
