liqiang/ai-agent-book
1
0
Fork 0
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity

ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）
Build latest book artifacts / build (push) Canceled after 0s
Details
dependency resolution / resolve (3.11) (push) Canceled after 0s
Details
dependency resolution / resolve (3.13) (push) Canceled after 0s
Details
deploy-pages / build (push) Canceled after 0s
Details
deploy-pages / deploy (push) Canceled after 0s
Details
i18n consistency check / check (push) Canceled after 0s
Details
provider adoption tests / test (chapter2/context-compression) (push) Canceled after 0s
Details
provider adoption tests / test (chapter2/prompt-injection) (push) Canceled after 0s
Details
provider adoption tests / test (chapter2/system-hint) (push) Canceled after 0s
Details
provider adoption tests / test (chapter3/log-sanitization) (push) Canceled after 0s
Details
web-search-agent tests / test (push) Canceled after 0s
Details
web-search-agent tests / agentbook (push) Canceled after 0s
Details

Browse Source
This commit is contained in:
liqiang
2026-08-20 13:12:50 +00:00
commit b119135836
10275 changed files with 3284984 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
chapter2/agent-skills-ppt/runs/exp2-6-kimi-pptx-20260731-v1/workspace/slides/13-conclusion.html
+33
View File
@@ -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'); }
.wrap { width: 620pt; margin: 30pt 50pt 24pt 50pt; display: flex; flex-direction: column; }
.kicker { font-size: 9.5pt; color: #F59E0B; font-weight: bold; letter-spacing: 2pt; margin: 0 0 4pt 0; }
h1 { font-size: 24pt; color: #FFFFFF; margin: 0 0 8pt 0; }
.rule { background: #F59E0B; height: 3pt; width: 60pt; margin: 0 0 14pt 0; }
ul { margin: 0; padding-left: 15pt; font-size: 11pt; color: #EDE9FE; line-height: 1.42; }
li { margin-bottom: 8pt; }
.quote { background: #3B2380; border-left: 4pt solid #F59E0B; border-radius: 6pt; padding: 10pt 14pt; margin-top: auto; }
.quote p { font-size: 10pt; color: #DDD6FE; font-style: italic; margin: 0; line-height: 1.4; }
.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>