feat: deepen attention and transformer chapter

src/pages/index.astro

@@ -81,6 +81,7 @@ const paths = [
         <div class="hero-actions">
           <a class="button primary" href="/roadmap/">选择学习路径 <span aria-hidden="true">↓</span></a>
           <a class="button" href="/foundations/language-models/">语言模型从哪里来</a>
+          <a class="button" href="/foundations/">注意力与 Transformer</a>
           <a class="button" href="/k3/">直接解剖 K3</a>
           <a class="button" href="/deepseek/">DeepSeek 专题</a>
           <a class="button" href="/scaling/">Scaling Laws 专题</a>
@@ -99,7 +100,7 @@ const paths = [
         <div class="hero-stats">
           <div><b>16</b><span>核心专题</span></div>
           <div><b>151</b><span>K3 报告来源</span></div>
-          <div><b>247</b><span>关键论文索引</span></div>
+          <div><b>258</b><span>关键论文索引</span></div>
           <div><b>47p</b><span>K3 技术报告</span></div>
         </div>
       </aside>
@@ -113,6 +114,22 @@ const paths = [
 
   <section class="section compact release-section" id="new-chapters">
     <div class="release-grid">
+      <a class="release-card transformer-release" href="/foundations/">
+        <div>
+          <p class="eyebrow"><span>NEW / CHAPTER 02</span> ATTENTION · TRANSFORMER</p>
+          <h2>Attention 不只是“看哪里”，Transformer 也不只有一种 Block</h2>
+          <p>
+            用信息路径、匹配几何、可见性、多头、位置、局部计算、深度、架构目标、系统成本与当代映射十张账，
+            从 Bahdanau 软对齐和 2017 原始 encoder–decoder，一路走到 FlashAttention、DeepSeek MLA 与 Kimi K3。
+          </p>
+        </div>
+        <dl>
+          <div><dt>LINEAGE</dt><dd>2014 → 2026</dd></div>
+          <div><dt>PAPERS</dt><dd>40 个一手节点</dd></div>
+          <div><dt>LAB</dt><dd>QKV · Mask · 多头位置 · Block 成本</dd></div>
+        </dl>
+        <span class="release-arrow" aria-hidden="true">从软对齐进入现代 LLM 骨架 →</span>
+      </a>
       <a class="release-card foundation-release" href="/foundations/language-models/">
         <div>
           <p class="eyebrow"><span>NEW / CHAPTER 01</span> LANGUAGE MODELING ORIGINS</p>
@@ -474,6 +491,7 @@ const paths = [
       transition: transform 180ms ease, border-color 180ms ease;
     }
 
+    .transformer-release,
     .foundation-release,
     .numerics-release,
     .data-release,
@@ -500,6 +518,14 @@ const paths = [
         var(--paper-raised);
     }
 
+    .transformer-release {
+      background:
+        radial-gradient(circle at 82% 18%, rgba(56, 91, 128, 0.21), transparent 30%),
+        radial-gradient(circle at 60% 74%, rgba(159, 91, 52, 0.12), transparent 26%),
+        repeating-linear-gradient(90deg, transparent 0 64px, rgba(56, 91, 128, 0.04) 64px 65px),
+        var(--paper-raised);
+    }
+
     .data-release {
       background:
         radial-gradient(circle at 82% 18%, rgba(76, 118, 112, 0.2), transparent 30%),
@@ -590,6 +616,7 @@ const paths = [
         padding-bottom: 76px;
       }
 
+      .transformer-release,
       .foundation-release,
       .numerics-release,
       .data-release,