ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter2/agent-skills-ppt/runs/exp2-6-kimi-pptx-20260731-v1/workspace/build.js

@@ -0,0 +1,85 @@
+const pptxgen = require('pptxgenjs');
+const html2pptx = require('./kimi-skills/pptx/scripts/html2pptx.js');
+
+async function build() {
+  const pptx = new pptxgen();
+  pptx.layout = 'LAYOUT_16x9';
+  pptx.author = 'Kimi';
+  pptx.title = 'Attention Is All You Need';
+
+  // Slides 1-6
+  await html2pptx('slides/01-cover.html', pptx);
+  await html2pptx('slides/02-problem.html', pptx);
+  await html2pptx('slides/03-keyidea.html', pptx);
+  await html2pptx('slides/04-architecture.html', pptx);
+  await html2pptx('slides/05-attention.html', pptx);
+  await html2pptx('slides/06-building-blocks.html', pptx);
+
+  // Slide 7: complexity table (adapted from Table 1, p. 6)
+  const { slide: s7, placeholders: p7 } = await html2pptx('slides/07-why-self-attention.html', pptx);
+  const hdr = { fill: { color: '4338CA' }, color: 'FFFFFF', bold: true, fontSize: 10 };
+  const rows = [
+    [
+      { text: 'Layer Type', options: hdr }, { text: 'Complexity per Layer', options: hdr },
+      { text: 'Sequential Ops', options: hdr }, { text: 'Max Path Length', options: hdr }
+    ],
+    ['Self-Attention', 'O(n²·d)', 'O(1)', 'O(1)'],
+    ['Recurrent', 'O(n·d²)', 'O(n)', 'O(n)'],
+    ['Convolutional', 'O(k·n·d²)', 'O(1)', 'O(log_k n)'],
+    ['Self-Attention (restricted)', 'O(r·n·d)', 'O(1)', 'O(n/r)']
+  ];
+  s7.addTable(rows, {
+    ...p7[0],
+    colW: [1.55, 1.15, 1.0, 1.1],
+    fontSize: 9.5,
+    fontFace: 'Arial',
+    border: { pt: 0.75, color: 'C9C6E8' },
+    align: 'center',
+    valign: 'middle',
+    fill: { color: 'FFFFFF' }
+  });
+
+  // Slides 8-9
+  await html2pptx('slides/08-training.html', pptx);
+  await html2pptx('slides/09-results.html', pptx);
+
+  // Slide 10: EN-DE BLEU bar chart (values from Table 2, p. 8)
+  const { slide: s10, placeholders: p10 } = await html2pptx('slides/10-chart.html', pptx);
+  s10.addChart(pptx.charts.BAR, [{
+    name: 'BLEU',
+    labels: ['ByteNet', 'GNMT+RL', 'ConvS2S', 'MoE', 'ConvS2S Ens.', 'TF (base)', 'TF (big)'],
+    values: [23.75, 24.6, 25.16, 26.03, 26.36, 27.3, 28.4]
+  }], {
+    ...p10[0],
+    barDir: 'col',
+    showLegend: false,
+    showValue: true,
+    dataLabelPosition: 'outEnd',
+    dataLabelFontSize: 9,
+    dataLabelColor: '1A1633',
+    dataLabelFormatCode: '0.00',
+    showCatAxisTitle: false,
+    catAxisLabelFontSize: 9,
+    valAxisMinVal: 20,
+    valAxisMaxVal: 30,
+    valAxisMajorUnit: 2,
+    showValAxisTitle: true,
+    valAxisTitle: 'BLEU (newstest2014)',
+    valAxisLabelFontSize: 8,
+    valAxisTitleFontSize: 9,
+    chartColors: ['9B97B8', '9B97B8', '9B97B8', '9B97B8', '7C3AED', '4338CA', 'F59E0B'],
+    valAxisLineShow: false,
+    serAxisLineShow: false,
+    showCatName: true
+  });
+
+  // Slides 11-13
+  await html2pptx('slides/11-generalization.html', pptx);
+  await html2pptx('slides/12-interpretability.html', pptx);
+  await html2pptx('slides/13-conclusion.html', pptx);
+
+  await pptx.writeFile({ fileName: 'output/attention-is-all-you-need.pptx' });
+  console.log('Saved output/attention-is-all-you-need.pptx');
+}
+
+build().catch(e => { console.error(e); process.exit(1); });

chapter2/agent-skills-ppt/runs/exp2-6-kimi-pptx-20260731-v1/workspace/fix-bullets.py

@@ -0,0 +1,17 @@
+#!/usr/bin/env python3
+"""Post-process deck: remove duplicate mid-paragraph <a:pPr> elements that
+pptxgenjs emits when list items contain multiple inline-formatting runs.
+The duplicate (later) pPr carries buNone, which makes LibreOffice drop the
+bullet glyph. Keeping only the first pPr per <a:p> restores uniform bullets."""
+import re, glob, sys
+
+pattern = re.compile(r'(</a:r>)\s*<a:pPr\b.*?</a:pPr>', re.S)
+total = 0
+for path in glob.glob('deckbuild/ppt/slides/slide*.xml'):
+    xml = open(path, encoding='utf-8').read()
+    fixed, n = pattern.subn(r'\1', xml)
+    if n:
+        open(path, 'w', encoding='utf-8').write(fixed)
+        total += n
+        print(f'{path}: removed {n} duplicate pPr')
+print(f'total removed: {total}')

chapter2/agent-skills-ppt/runs/exp2-6-kimi-pptx-20260731-v1/workspace/make-assets.js

@@ -0,0 +1,15 @@
+const sharp = require('sharp');
+
+// Cover background: deep indigo -> violet diagonal gradient, rasterized (CSS gradients not supported)
+const cover = `<svg xmlns="http://www.w3.org/2000/svg" width="1920" height="1080">
+  <defs>
+    <linearGradient id="g" x1="0%" y1="0%" x2="100%" y2="100%">
+      <stop offset="0%" style="stop-color:#14102E"/>
+      <stop offset="55%" style="stop-color:#2A1668"/>
+      <stop offset="100%" style="stop-color:#5B21B6"/>
+    </linearGradient>
+  </defs>
+  <rect width="100%" height="100%" fill="url(#g)"/>
+</svg>`;
+
+sharp(Buffer.from(cover)).png().toFile('assets/cover-bg.png').then(() => console.log('assets ok'));

chapter2/agent-skills-ppt/runs/exp2-6-kimi-pptx-20260731-v1/workspace/package-lock.json

@@ -0,0 +1,805 @@
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+      }
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chapter2/agent-skills-ppt/runs/exp2-6-kimi-pptx-20260731-v1/workspace/package.json

@@ -0,0 +1,18 @@
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+  "main": "index.js",
+  "scripts": {
+    "test": "echo \"Error: no test specified\" && exit 1"
+  },
+  "keywords": [],
+  "author": "",
+  "license": "ISC",
+  "type": "commonjs",
+  "dependencies": {
+    "playwright": "^1.62.1",
+    "pptxgenjs": "^4.0.1",
+    "sharp": "^0.35.3"
+  }
+}

chapter2/agent-skills-ppt/runs/exp2-6-kimi-pptx-20260731-v1/workspace/slides/01-cover.html

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+<!DOCTYPE html>
+<html>
+<head><style>
+html { background: #14102E; }
+body { width: 720pt; height: 405pt; margin: 0; padding: 0; font-family: Arial, sans-serif; display: flex; background-image: url('../assets/cover-bg.png'); }
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+.card img { height: 200pt; display: block; margin: 0 auto; }
+.cardcap { font-size: 8pt; color: #5B5876; text-align: center; margin: 8pt 0 0 0; }
+</style></head>
+<body>
+<div class="left">
+  <p class="kicker">NEURIPS 2017 &middot; ARXIV:1706.03762</p>
+  <h1>Attention Is<br>All You Need</h1>
+  <p class="sub">The Transformer: sequence transduction based entirely on attention &mdash; no recurrence, no convolution.</p>
+  <div class="rule"></div>
+  <p class="authors">Ashish Vaswani &middot; Noam Shazeer &middot; Niki Parmar &middot; Jakob Uszkoreit<br>Llion Jones &middot; Aidan N. Gomez &middot; &#x141;ukasz Kaiser &middot; Illia Polosukhin</p>
+  <p class="affil">Google Brain &middot; Google Research &middot; University of Toronto</p>
+</div>
+<div class="card">
+  <img src="../source_visuals/fig1-03.png">
+  <p class="cardcap">The Transformer &mdash; model architecture<br>Figure 1, paper p. 3</p>
+</div>
+</body>
+</html>

chapter2/agent-skills-ppt/runs/exp2-6-kimi-pptx-20260731-v1/workspace/slides/02-problem.html

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+<!DOCTYPE html>
+<html>
+<head><style>
+html { background: #FFFFFF; }
+body { width: 720pt; height: 405pt; margin: 0; padding: 0; background: #FAFAFF; font-family: Arial, sans-serif; display: flex; }
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+.foot { font-size: 8pt; color: #8A86A3; margin-top: auto; margin-bottom: 0; }
+</style></head>
+<body>
+<div class="bar"></div>
+<div class="wrap">
+  <p class="kicker">01 &middot; BACKGROUND</p>
+  <h1>The Problem: The Limits of Recurrence</h1>
+  <div class="rule"></div>
+  <div class="cols">
+    <div class="txt">
+      <ul>
+        <li>Sequence transduction (e.g., machine translation) was dominated by <b>recurrent</b> (LSTM / GRU) and <b>convolutional</b> encoder&ndash;decoder models.</li>
+        <li>RNNs compute hidden states <b>one position at a time</b>: inherently sequential, so training cannot parallelize across positions in a sequence.</li>
+        <li>Signals between distant positions must travel a path of <b>O(n) operations</b> &mdash; the longer the path, the harder long-range dependencies are to learn.</li>
+        <li>Attention mechanisms already existed, but only as an <b>accessory</b> layered on top of RNNs or CNNs.</li>
+      </ul>
+    </div>
+    <div class="stat">
+      <p class="bignum">O(n)</p>
+      <p class="statlab">sequential operations per layer in a recurrent network</p>
+      <p class="bignum">O(n)</p>
+      <p class="statlab">maximum path length between any two positions</p>
+    </div>
+  </div>
+  <p class="foot">Source: Vaswani et al., &ldquo;Attention Is All You Need&rdquo;, &sect;1 Introduction (paper p. 1&ndash;2).</p>
+</div>
+</body>
+</html>

chapter2/agent-skills-ppt/runs/exp2-6-kimi-pptx-20260731-v1/workspace/slides/03-keyidea.html

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+<!DOCTYPE html>
+<html>
+<head><style>
+html { background: #FFFFFF; }
+body { width: 720pt; height: 405pt; margin: 0; padding: 0; background: #FAFAFF; font-family: Arial, sans-serif; display: flex; }
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+</style></head>
+<body>
+<div class="bar"></div>
+<div class="wrap">
+  <p class="kicker">02 &middot; KEY IDEA</p>
+  <h1>The Transformer: Attention Is All You Need</h1>
+  <div class="rule"></div>
+  <div class="cards">
+    <div class="card">
+      <p class="ct">No Recurrence</p>
+      <p class="cd">Sequential RNN hidden states are removed entirely &mdash; positions are processed in parallel.</p>
+    </div>
+    <div class="card">
+      <p class="ct">No Convolution</p>
+      <p class="cd">No fixed-width kernels; every position can connect to every other position directly.</p>
+    </div>
+    <div class="card" style="margin-right: 0;">
+      <p class="ct">Self-Attention Only</p>
+      <p class="cd">Multi-headed self-attention computes all representations, in both encoder and decoder.</p>
+    </div>
+  </div>
+  <ul>
+    <li>First sequence transduction model based <b>entirely on attention</b>, replacing recurrent layers with multi-headed self-attention.</li>
+    <li>Highly parallelizable &mdash; reaches a new state of the art in translation with a <b>fraction of the training cost</b>.</li>
+    <li>Generalizes beyond translation (e.g., English constituency parsing) and yields interpretable attention patterns.</li>
+  </ul>
+  <p class="foot">Source: Vaswani et al., Abstract and &sect;1 (paper p. 1&ndash;2).</p>
+</div>
+</body>
+</html>

chapter2/agent-skills-ppt/runs/exp2-6-kimi-pptx-20260731-v1/workspace/slides/04-architecture.html

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+<!DOCTYPE html>
+<html>
+<head><style>
+html { background: #FFFFFF; }
+body { width: 720pt; height: 405pt; margin: 0; padding: 0; background: #FAFAFF; font-family: Arial, sans-serif; display: flex; }
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+</style></head>
+<body>
+<div class="bar"></div>
+<div class="wrap">
+  <p class="kicker">03 &middot; METHOD</p>
+  <h1>Model Architecture: Stacked Self-Attention</h1>
+  <div class="rule"></div>
+  <div class="cols">
+    <div class="txt">
+      <ul>
+        <li><b>Encoder&ndash;decoder</b> structure built from stacked self-attention and point-wise, fully connected layers (left and right halves of Figure 1).</li>
+        <li><b>Encoder</b>: N = 6 identical layers &mdash; each with multi-head self-attention, then a position-wise feed-forward network.</li>
+        <li><b>Decoder</b>: N = 6 layers; adds a <b>masked</b> multi-head self-attention sub-layer (positions only attend to earlier positions) plus attention over the encoder output.</li>
+        <li>A <b>residual connection</b> wraps every sub-layer, followed by <b>layer normalization</b>.</li>
+        <li>Inputs/outputs are embedded (d_model = 512) and combined with <b>positional encodings</b>, since the model has no notion of order on its own.</li>
+      </ul>
+    </div>
+    <div class="fig">
+      <img src="../source_visuals/fig1-03.png">
+      <p class="figcap">Encoder (left) and decoder (right) stacks.<br>Figure 1, paper p. 3.</p>
+    </div>
+  </div>
+  <p class="foot">Visual: Figure 1, &ldquo;The Transformer - model architecture&rdquo; (paper p. 3); text: &sect;3.1 (paper p. 3).</p>
+</div>
+</body>
+</html>

chapter2/agent-skills-ppt/runs/exp2-6-kimi-pptx-20260731-v1/workspace/slides/05-attention.html

@@ -0,0 +1,48 @@
+<!DOCTYPE html>
+<html>
+<head><style>
+html { background: #FFFFFF; }
+body { width: 720pt; height: 405pt; margin: 0; padding: 0; background: #FAFAFF; font-family: Arial, sans-serif; display: flex; }
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+</style></head>
+<body>
+<div class="bar"></div>
+<div class="wrap">
+  <p class="kicker">04 &middot; METHOD</p>
+  <h1>Scaled Dot-Product &amp; Multi-Head Attention</h1>
+  <div class="rule"></div>
+  <div class="cols">
+    <div class="txt">
+      <div class="formula">
+        <p>Attention(Q,K,V) = softmax(QK^T / &radic;d_k)V</p>
+      </div>
+      <ul>
+        <li>Attention maps a <b>query</b> and <b>key&ndash;value</b> pairs to an output: a weighted sum of values, weights from query&ndash;key compatibility.</li>
+        <li>Dot products are scaled by <b>1/&radic;d_k</b> &mdash; for large d_k they would push softmax into tiny-gradient regions.</li>
+        <li><b>Multi-head</b>: queries, keys, values are linearly projected <b>h = 8</b> times (to d_k = d_v = 64), attention runs in parallel, outputs are concatenated and re-projected.</li>
+        <li>Different heads jointly attend to information from <b>different representation subspaces</b> at different positions.</li>
+      </ul>
+    </div>
+    <div class="fig">
+      <img src="../source_visuals/fig2-04.png">
+      <p class="figcap">(left) Scaled Dot-Product Attention; (right) Multi-Head Attention &mdash; several attention layers in parallel. Figure 2, paper p. 4.</p>
+    </div>
+  </div>
+  <p class="foot">Visual: Figure 2 (paper p. 4); formula and text: &sect;3.2 (paper p. 4&ndash;5).</p>
+</div>
+</body>
+</html>

chapter2/agent-skills-ppt/runs/exp2-6-kimi-pptx-20260731-v1/workspace/slides/06-building-blocks.html

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+<!DOCTYPE html>
+<html>
+<head><style>
+html { background: #FFFFFF; }
+body { width: 720pt; height: 405pt; margin: 0; padding: 0; background: #FAFAFF; font-family: Arial, sans-serif; display: flex; }
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+.foot { font-size: 8pt; color: #8A86A3; margin: 8pt 0 0 0; }
+</style></head>
+<body>
+<div class="bar"></div>
+<div class="wrap">
+  <p class="kicker">05 &middot; METHOD</p>
+  <h1>Anatomy of the Building Blocks</h1>
+  <div class="rule"></div>
+  <div class="grid">
+    <div class="card">
+      <p class="ct">Multi-Head Attention</p>
+      <p class="cd">h = 8 parallel heads; d_k = d_v = d_model/h = 64, so total compute stays close to single-head attention at full dimension.</p>
+    </div>
+    <div class="gap"></div>
+    <div class="card">
+      <p class="ct">Position-wise Feed-Forward</p>
+      <p class="cd">FFN(x) = max(0, xW&#x2081;+b&#x2081;)W&#x2082;+b&#x2082;, applied identically to every position; inner dimension d_ff = 2048.</p>
+    </div>
+  </div>
+  <div class="grid">
+    <div class="card">
+      <p class="ct">Add &amp; Norm</p>
+      <p class="cd">Residual connection around each sub-layer: LayerNorm(x + Sublayer(x)); all sub-layers and embeddings output d_model = 512.</p>
+    </div>
+    <div class="gap"></div>
+    <div class="card">
+      <p class="ct">Positional Encoding</p>
+      <p class="cd">Sine/cosine waves of different frequencies injected at the inputs, so the model can use token order; learned alternatives worked equally well.</p>
+    </div>
+  </div>
+  <div class="note">
+    <p><b>Two configurations:</b> base &mdash; d_model 512, d_ff 2048, h 8, dropout 0.1 &nbsp;|&nbsp; big &mdash; d_model 1024, d_ff 4096, h 16, dropout 0.3.</p>
+  </div>
+  <p class="foot">Source: &sect;3.2&ndash;3.5 (paper p. 4&ndash;5); &sect;5.4 and Table 3 footnotes (paper p. 7&ndash;8).</p>
+</div>
+</body>
+</html>

chapter2/agent-skills-ppt/runs/exp2-6-kimi-pptx-20260731-v1/workspace/slides/07-why-self-attention.html

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+<!DOCTYPE html>
+<html>
+<head><style>
+html { background: #FFFFFF; }
+body { width: 720pt; height: 405pt; margin: 0; padding: 0; background: #FAFAFF; font-family: Arial, sans-serif; display: flex; }
+.bar { width: 10pt; background: #4338CA; }
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+.foot { font-size: 8pt; color: #8A86A3; margin-top: auto; margin-bottom: 0; }
+</style></head>
+<body>
+<div class="bar"></div>
+<div class="wrap">
+  <p class="kicker">06 &middot; MOTIVATION</p>
+  <h1>Why Self-Attention Wins</h1>
+  <div class="rule"></div>
+  <div class="cols">
+    <div class="txt">
+      <ul>
+        <li><b>Parallelization:</b> a self-attention layer needs only O(1) sequential operations vs. O(n) for recurrence &mdash; training uses all positions at once.</li>
+        <li><b>Long-range learning:</b> any two positions connect through a path of <b>constant length O(1)</b>, instead of O(n) for RNNs or O(log_k n) for dilated convolutions.</li>
+        <li><b>Cost:</b> per-layer complexity O(n&sup2;&middot;d) beats recurrence O(n&middot;d&sup2;) whenever n &lt; d &mdash; the common case with word-piece / BPE representations.</li>
+        <li><b>Interpretability:</b> attention distributions expose what the model attends to; heads learn distinct, task-like behaviors.</li>
+      </ul>
+    </div>
+    <div class="tabwrap">
+      <p class="tablab">PER-LAYER COMPLEXITY AND PATH LENGTHS</p>
+      <div id="table-complexity" class="placeholder" style="width: 330pt; height: 150pt;"></div>
+      <p class="tabcap">n = sequence length, d = representation dimension, k = conv. kernel width, r = neighborhood. Adapted from Table 1, paper p. 6.</p>
+    </div>
+  </div>
+  <p class="foot">Table: adapted from Table 1 (paper p. 6); text: &sect;4 (paper p. 6&ndash;7).</p>
+</div>
+</body>
+</html>

chapter2/agent-skills-ppt/runs/exp2-6-kimi-pptx-20260731-v1/workspace/slides/08-training.html

@@ -0,0 +1,54 @@
+<!DOCTYPE html>
+<html>
+<head><style>
+html { background: #FFFFFF; }
+body { width: 720pt; height: 405pt; margin: 0; padding: 0; background: #FAFAFF; font-family: Arial, sans-serif; display: flex; }
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+</style></head>
+<body>
+<div class="bar"></div>
+<div class="wrap">
+  <p class="kicker">07 &middot; EXPERIMENTS</p>
+  <h1>Training Setup</h1>
+  <div class="rule"></div>
+  <div class="cols">
+    <div class="card">
+      <p class="ct">Data &amp; Batching</p>
+      <ul>
+        <li><b>WMT 2014 English&ndash;German</b>: 4.5M sentence pairs; <b>English&ndash;French</b>: 36M sentences.</li>
+        <li>Byte-pair encoding with a <b>shared source&ndash;target vocabulary of ~37K tokens</b>.</li>
+        <li>Batches of ~25,000 source and ~25,000 target tokens, grouped by approximate sequence length.</li>
+      </ul>
+    </div>
+    <div class="gap"></div>
+    <div class="card">
+      <p class="ct">Optimization &amp; Regularization</p>
+      <ul>
+        <li>Adam (&beta;&#x2081; = 0.9, &beta;&#x2082; = 0.98, &epsilon; = 10&#x207B;&#x2079;) with <b>warmup</b>: 4,000 linearly increasing steps, then inverse-square-root decay.</li>
+        <li>Residual dropout 0.1 (base) / 0.3 (big); attention dropout on each sub-layer.</li>
+        <li>Label smoothing &epsilon;_ls = 0.1 &mdash; hurts perplexity but <b>improves accuracy and BLEU</b>.</li>
+      </ul>
+    </div>
+  </div>
+  <div class="hw">
+    <p><b>Hardware &amp; time:</b> 8 &times; NVIDIA P100 GPUs &mdash; base model: 12 hours (100K steps); big model: 3.5 days (300K steps). Beam search with beam size 4, length penalty &alpha; = 0.6.</p>
+  </div>
+  <p class="foot">Source: &sect;5 Training (paper p. 7); &sect;6.1 (paper p. 8).</p>
+</div>
+</body>
+</html>

chapter2/agent-skills-ppt/runs/exp2-6-kimi-pptx-20260731-v1/workspace/slides/09-results.html

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+<!DOCTYPE html>
+<html>
+<head><style>
+html { background: #FFFFFF; }
+body { width: 720pt; height: 405pt; margin: 0; padding: 0; background: #FAFAFF; font-family: Arial, sans-serif; display: flex; }
+.bar { width: 10pt; background: #4338CA; }
+.wrap { width: 644pt; margin: 26pt 32pt 22pt 34pt; display: flex; flex-direction: column; }
+.kicker { font-size: 9.5pt; color: #7C3AED; font-weight: bold; letter-spacing: 2pt; margin: 0 0 4pt 0; }
+h1 { font-size: 23pt; color: #1A1633; margin: 0 0 8pt 0; }
+.rule { background: #F59E0B; height: 3pt; width: 60pt; margin: 0 0 12pt 0; }
+.cols { display: flex; }
+.fig { width: 396pt; background: #FFFFFF; border: 1px solid #E0DFF0; border-radius: 10pt; padding: 10pt; }
+.fig img { width: 376pt; display: block; margin: 0 auto; }
+.figcap { font-size: 8pt; color: #5B5876; text-align: center; margin: 6pt 0 0 0; }
+.stats { width: 196pt; margin-left: 22pt; }
+.stat { background: #4338CA; border-radius: 10pt; padding: 10pt 14pt; margin-bottom: 10pt; }
+.stat p { margin: 0; }
+.bignum { font-size: 22pt; color: #FFFFFF; font-weight: bold; }
+.statlab { font-size: 8.5pt; color: #DDD6FE; line-height: 1.3; margin-top: 2pt; }
+.stat.amber { background: #B45309; }
+.foot { font-size: 8pt; color: #8A86A3; margin-top: auto; margin-bottom: 0; }
+</style></head>
+<body>
+<div class="bar"></div>
+<div class="wrap">
+  <p class="kicker">08 &middot; RESULTS</p>
+  <h1>Machine Translation: New State of the Art</h1>
+  <div class="rule"></div>
+  <div class="cols">
+    <div class="fig">
+      <img src="../source_visuals/table2-08.png">
+      <p class="figcap">BLEU scores and training costs on WMT 2014 EN&ndash;DE / EN&ndash;FR (newstest2014). Table 2, paper p. 8.</p>
+    </div>
+    <div class="stats">
+      <div class="stat">
+        <p class="bignum">28.4 BLEU</p>
+        <p class="statlab">EN&rarr;DE: &gt;2.0 above the best previously reported model, including ensembles</p>
+      </div>
+      <div class="stat">
+        <p class="bignum">41.8 BLEU</p>
+        <p class="statlab">EN&rarr;FR: best single model, at under 1/4 of the prior training cost</p>
+      </div>
+      <div class="stat amber">
+        <p class="bignum">3.3&times;10^18</p>
+        <p class="statlab">FLOPs to train the base model &mdash; yet it already beats all prior models and ensembles</p>
+      </div>
+    </div>
+  </div>
+  <p class="foot">Visual: Table 2 (paper p. 8); text: &sect;6.1 Machine Translation (paper p. 8).</p>
+</div>
+</body>
+</html>

chapter2/agent-skills-ppt/runs/exp2-6-kimi-pptx-20260731-v1/workspace/slides/10-chart.html

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+<!DOCTYPE html>
+<html>
+<head><style>
+html { background: #FFFFFF; }
+body { width: 720pt; height: 405pt; margin: 0; padding: 0; background: #FAFAFF; font-family: Arial, sans-serif; display: flex; }
+.bar { width: 10pt; background: #4338CA; }
+.wrap { width: 644pt; margin: 26pt 32pt 22pt 34pt; display: flex; flex-direction: column; }
+.kicker { font-size: 9.5pt; color: #7C3AED; font-weight: bold; letter-spacing: 2pt; margin: 0 0 4pt 0; }
+h1 { font-size: 23pt; color: #1A1633; margin: 0 0 8pt 0; }
+.rule { background: #F59E0B; height: 3pt; width: 60pt; margin: 0 0 10pt 0; }
+.chartwrap { margin: 0 auto; }
+#chart-bleu { margin: 0 auto; }
+.chartcap { font-size: 9pt; color: #5B5876; text-align: center; margin: 6pt 0 0 0; }
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+.takeaway p { font-size: 10pt; color: #6B4E12; margin: 0; }
+.foot { font-size: 8pt; color: #8A86A3; margin: 8pt 0 0 0; }
+</style></head>
+<body>
+<div class="bar"></div>
+<div class="wrap">
+  <p class="kicker">09 &middot; RESULTS</p>
+  <h1>EN&rarr;DE BLEU: Transformer vs. Prior Art</h1>
+  <div class="rule"></div>
+  <div class="chartwrap">
+    <div id="chart-bleu" class="placeholder" style="width: 460pt; height: 218pt; margin: 0 auto;"></div>
+    <p class="chartcap">WMT 2014 English&rarr;German newstest2014 BLEU scores &mdash; values from Table 2, paper p. 8.</p>
+  </div>
+  <div class="takeaway">
+    <p>The big Transformer sets a new state of the art (28.4 BLEU) &mdash; more than 2.0 BLEU above the best previously reported models, including ensembles.</p>
+  </div>
+  <p class="foot">Data: Table 2 (paper p. 8); discussion: &sect;6.1 (paper p. 8).</p>
+</div>
+</body>
+</html>

chapter2/agent-skills-ppt/runs/exp2-6-kimi-pptx-20260731-v1/workspace/slides/11-generalization.html

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+<!DOCTYPE html>
+<html>
+<head><style>
+html { background: #FFFFFF; }
+body { width: 720pt; height: 405pt; margin: 0; padding: 0; background: #FAFAFF; font-family: Arial, sans-serif; display: flex; }
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+.rule { background: #F59E0B; height: 3pt; width: 60pt; margin: 0 0 14pt 0; }
+.cols { display: flex; }
+.txt { width: 380pt; }
+ul { margin: 0; padding-left: 14pt; font-size: 11pt; color: #2A2740; line-height: 1.4; }
+li { margin-bottom: 10pt; }
+.stats { width: 210pt; margin-left: 30pt; }
+.stat { background: #4338CA; border-radius: 10pt; padding: 12pt 16pt; margin-bottom: 12pt; }
+.stat p { margin: 0; }
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+.foot { font-size: 8pt; color: #8A86A3; margin-top: auto; margin-bottom: 0; }
+</style></head>
+<body>
+<div class="bar"></div>
+<div class="wrap">
+  <p class="kicker">10 &middot; RESULTS</p>
+  <h1>Beyond Translation: Constituency Parsing</h1>
+  <div class="rule"></div>
+  <div class="cols">
+    <div class="txt">
+      <ul>
+        <li>A <b>4-layer Transformer</b> was trained on English constituency parsing (Wall Street Journal portion of the Penn Treebank, ~40K training sentences).</li>
+        <li>With almost <b>no task-specific tuning</b>, it outperforms the BerkeleyParser &mdash; even when trained only on the WSJ training set.</li>
+        <li>In the <b>semi-supervised</b> setting (with a ~17M-sentence high-confidence corpus) it beats all previously reported models <b>except the Recurrent Neural Network Grammar</b>.</li>
+        <li>The same architecture generalizes across tasks without structural changes.</li>
+      </ul>
+    </div>
+    <div class="stats">
+      <div class="stat">
+        <p class="bignum">91.3 F1</p>
+        <p class="statlab">WSJ-only, discriminative setting (Section 23 of WSJ)</p>
+      </div>
+      <div class="stat">
+        <p class="bignum">92.7 F1</p>
+        <p class="statlab">Semi-supervised setting &mdash; above every prior model except RNNG</p>
+      </div>
+    </div>
+  </div>
+  <p class="foot">Source: Table 4 (paper p. 10) and &sect;6.2 / &ldquo;English Constituency Parsing&rdquo; (paper p. 9&ndash;10).</p>
+</div>
+</body>
+</html>

chapter2/agent-skills-ppt/runs/exp2-6-kimi-pptx-20260731-v1/workspace/slides/12-interpretability.html

@@ -0,0 +1,47 @@
+<!DOCTYPE html>
+<html>
+<head><style>
+html { background: #FFFFFF; }
+body { width: 720pt; height: 405pt; margin: 0; padding: 0; background: #FAFAFF; font-family: Arial, sans-serif; display: flex; }
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+.quote p { font-size: 9.5pt; color: #4338CA; font-style: italic; margin: 0; line-height: 1.35; }
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+.foot { font-size: 8pt; color: #8A86A3; margin-top: auto; margin-bottom: 0; }
+</style></head>
+<body>
+<div class="bar"></div>
+<div class="wrap">
+  <p class="kicker">11 &middot; RESULTS</p>
+  <h1>Interpretability: What Attention Sees</h1>
+  <div class="rule"></div>
+  <div class="cols">
+    <div class="txt">
+      <ul>
+        <li>Individual attention heads learn to perform <b>different tasks</b>; many exhibit behavior tied to the syntactic and semantic structure of sentences.</li>
+        <li>In this example (encoder self-attention, layer 5 of 6), many heads attend to a <b>distant dependency of the verb &ldquo;making&rdquo;</b>, completing the phrase &ldquo;making&hellip;more difficult&rdquo;.</li>
+        <li>Other heads are involved in <b>anaphora resolution</b> (Figure 4 of the paper).</li>
+      </ul>
+      <div class="quote">
+        <p>&ldquo;Not only do individual attention heads clearly learn to perform different tasks, many appear to exhibit behavior related to the syntactic and semantic structure of the sentences.&rdquo; &mdash; &sect;4, p. 7</p>
+      </div>
+    </div>
+    <div class="fig">
+      <img src="../source_visuals/fig3-13.png">
+      <p class="figcap">Attention for the word &ldquo;making&rdquo;; colors = different heads. Figure 3, paper p. 13.</p>
+    </div>
+  </div>
+  <p class="foot">Visual: Figure 3 (paper p. 13); text: &sect;4 (paper p. 7) and Appendix (paper p. 13).</p>
+</div>
+</body>
+</html>

chapter2/agent-skills-ppt/runs/exp2-6-kimi-pptx-20260731-v1/workspace/slides/13-conclusion.html

@@ -0,0 +1,33 @@
+<!DOCTYPE html>
+<html>
+<head><style>
+html { background: #14102E; }
+body { width: 720pt; height: 405pt; margin: 0; padding: 0; font-family: Arial, sans-serif; display: flex; background-image: url('../assets/cover-bg.png'); }
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+.foot { font-size: 8pt; color: #B8AEE8; margin: 10pt 0 0 0; }
+</style></head>
+<body>
+<div class="wrap">
+  <p class="kicker">12 &middot; CONCLUSION</p>
+  <h1>Conclusion &amp; Legacy</h1>
+  <div class="rule"></div>
+  <ul>
+    <li>The <b>Transformer</b>: the first sequence transduction model based entirely on attention &mdash; multi-headed self-attention replaces recurrent layers in encoder&ndash;decoder architectures.</li>
+    <li>Trains <b>significantly faster</b> than recurrent or convolutional architectures thanks to full parallelization.</li>
+    <li>New state of the art on <b>WMT 2014 EN&rarr;DE (28.4 BLEU)</b> and <b>EN&rarr;FR (41.8 BLEU)</b>, at a fraction of the previous training cost.</li>
+    <li>The architecture became the foundation of modern large language models (BERT, GPT, and their successors) &mdash; attention really was all you needed.</li>
+  </ul>
+  <div class="quote">
+    <p>&ldquo;We are excited about the future of attention-based models and plan to apply them to other tasks.&rdquo; &mdash; Vaswani et al., &sect;7 Conclusion (paper p. 10)</p>
+  </div>
+  <p class="foot">Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin. &ldquo;Attention Is All You Need.&rdquo; NeurIPS 2017 (arXiv:1706.03762). All figures/tables shown are cropped from the original paper PDF.</p>
+</div>
+</body>
+</html>

chapter2/agent-skills-ppt/runs/exp2-6-kimi-pptx-20260731-v1/workspace/source_visuals/manifest.json

@@ -0,0 +1,26 @@
+[
+  {
+    "file": "fig1-03.png",
+    "page": 3,
+    "label": "Figure 1",
+    "caption": "The Transformer - model architecture."
+  },
+  {
+    "file": "fig2-04.png",
+    "page": 4,
+    "label": "Figure 2",
+    "caption": "(left) Scaled Dot-Product Attention. (right) Multi-Head Attention consists of several attention layers running in parallel."
+  },
+  {
+    "file": "table2-08.png",
+    "page": 8,
+    "label": "Table 2",
+    "caption": "The Transformer achieves better BLEU scores than previous state-of-the-art models on the English-to-German and English-to-French newstest2014 tests at a fraction of the training cost."
+  },
+  {
+    "file": "fig3-13.png",
+    "page": 13,
+    "label": "Figure 3",
+    "caption": "An example of the attention mechanism following long-distance dependencies in the encoder self-attention in layer 5 of 6. Many of the attention heads attend to a distant dependency of the verb 'making', completing the phrase 'making...more difficult'. Attentions here shown only for the word 'making'. Different colors represent different heads. Best viewed in color."
+  }
+]