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  "text": "Dipsic Flash 從4.0到4.1的進化程度堪比去年Cloud Ops 4.5到4.6就這麼誇張。第一它更快了,它的回復速度明顯提升了。第二它更強了,它是個小模型,但是能力超過了上一代的V4 Pro。我們看一下這個圖,可以看到在Terminal Bank 3.0里,它超過了KMI K3和GLM 5.3。要知道模型的主要工作環境就是在終端里,所以這項測試非常有意義。然後在其他三個測試里,它甚至超過了OPS 5和GPT 5.6 Soul。\n而且不僅如此,它還更便宜了。它的輸入價格相對於V4 Flash 降了60%。我們先看一下我測的任務,然後我再說一下這一波它為什麼提升這麼大。主要得益於它的新架構。這個是我用兩輪對話讓它做的一個之前很火的一個遊戲。可以看到它完成的很好,我現在是在控制人物在行動。可以看到這裡我還可以去攻擊敵人。OK,這裡就被Clear掉了。然後來到了第二關。\n整個效果我覺得非常非常棒。基本上沒有什麼Bug。\n然後我用一段提示詞讓它附現了一下遊戲王的卡片。\n當我們不斷旋轉卡片的時候,它這個流光也是在不斷的變化。\n然後下面還可以調這個輻射的強度。我們可以調的很高。\n可以把它調眉。\n調眉之後就只有這個光的反射。你可以看到當我把卡片旋轉到一定角度,是可以看見這個人物的一個輪廓的。\n這個細節還原的非常好。其實還有一些別的角色。整體效果也非常棒。然後這個主體也是可以縮放的。包括深度。\n我感覺用這個模型去做一些輕度任務、中度任務都非常非常好。而且它的生成的速度很快。好,我們詳細看一下這個模型這次用的一個新架構。\n它在這裡說的這個新架構有一些不容易懂。我用一個比較容易懂的邏輯來講一下。是這樣的。你就想像模型工作的時候,一般分為讀體和答體。\n這兩個步驟。第一個步驟,獨體的時候,每個字都要進入它所有的神經網絡。如果有1萬個字,這1萬個字都得從這40層裡面走一遍。\n而且每一層神經網絡走完之後,它不是走完就完了。而是它要把每一個字記住。把這1萬個字全部都走完記住完之後,模型才開始輸出文字。\n然後輸出的每一個文字也會走完這40層神經網絡。現在DeepSick這個新架構,就是在獨體的時候可以不用走完這40層神經網絡。它只要走完前20層就行了。那後面20層就直接把前20層的記錄。\n那後面20層就直接把前20層的記錄,直接翻譯成自己的就行。這樣的話,成本就直接少了一半。\n那為什麼這樣可以呢?因為後面的20層神經網絡從剛開始訓練的時候,它就是這麼訓練的。它從誕生起就幹了這個活。那為什麼不直接把39層神經網絡都這麼訓練,只留一層讀動就可以了。\n那首先是因為前20層每一層都把前一層的理解再加工一遍,到了第二層。那這份理解已經經過了20道工序,就已經夠深了。如果只有一層那還是太淺了,模型還是沒有把這個題目給內化。\n而且說實話,翻譯過來的內容和自己寫的相比肯定還是有損失。但實測下來性能和老做法是差不多的。\n微軟之前也驗證了這條路。Deepseek這次是放大了做,但報告自己也承認,在極端情況下的編輯也還沒有完全摸清。\n然後簡單總結一下為什麼獨體省一半這麼有意義。因為現在AI幹的活是讀的多寫的少。它一次可能讀幾萬字,但是它回復可能就幾百字。\n所以讀題省了一半,速度就會快一大截。然後官方說明的第二點是,它有更少的緩存也更省成本。那這一點是怎麼理解呢?\n就我們剛剛說到模型讀題就是記筆記嘛。那答題就是翻筆記。那這份筆記就叫做KVCatcher。那這份筆記佔的空間就要你付錢。\nDeepseek這次是把每個字的筆記縮到了上一代的四分之一。所以你就能聊更長的內容,花更少的錢。\n如果你接著上次的聊,筆記就會直接從硬盤裡拿出來,就不用重新讀一遍了。那這份筆記就縮到了八分之一。\n這就是這次Deepseek V4.1 Flash它更快又更便宜的一個底層邏輯。\n好,我是達斯海,我會一直分享一些有用有價值的AI內容。如果你覺得這期有用,那我們下次再見。",
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      "text": "這兩個步驟。第一個步驟,獨體的時候,每個字都要進入它所有的神經網絡。如果有1萬個字,這1萬個字都得從這40層裡面走一遍。",
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      "text": "而且每一層神經網絡走完之後,它不是走完就完了。而是它要把每一個字記住。把這1萬個字全部都走完記住完之後,模型才開始輸出文字。",
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      "text": "然後輸出的每一個文字也會走完這40層神經網絡。現在DeepSick這個新架構,就是在獨體的時候可以不用走完這40層神經網絡。它只要走完前20層就行了。那後面20層就直接把前20層的記錄。",
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      "text": "那後面20層就直接把前20層的記錄,直接翻譯成自己的就行。這樣的話,成本就直接少了一半。",
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      "text": "而且說實話,翻譯過來的內容和自己寫的相比肯定還是有損失。但實測下來性能和老做法是差不多的。",
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      "text": "微軟之前也驗證了這條路。Deepseek這次是放大了做,但報告自己也承認,在極端情況下的編輯也還沒有完全摸清。",
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      "text": "然後簡單總結一下為什麼獨體省一半這麼有意義。因為現在AI幹的活是讀的多寫的少。它一次可能讀幾萬字,但是它回復可能就幾百字。",
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      "text": "所以讀題省了一半,速度就會快一大截。然後官方說明的第二點是,它有更少的緩存也更省成本。那這一點是怎麼理解呢?",
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      "text": "就我們剛剛說到模型讀題就是記筆記嘛。那答題就是翻筆記。那這份筆記就叫做KVCatcher。那這份筆記佔的空間就要你付錢。",
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      "text": "Deepseek這次是把每個字的筆記縮到了上一代的四分之一。所以你就能聊更長的內容,花更少的錢。",
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      "text": "如果你接著上次的聊,筆記就會直接從硬盤裡拿出來,就不用重新讀一遍了。那這份筆記就縮到了八分之一。",
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    {
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      "text": "這就是這次Deepseek V4.1 Flash它更快又更便宜的一個底層邏輯。",
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      "text": "好,我是達斯海,我會一直分享一些有用有價值的AI內容。如果你覺得這期有用,那我們下次再見。",
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