AI Engineer Notebooks
Learn the applied-LLM stack the way you'll actually be interviewed on it —
framework-free, on a free API, from prompting all the way to serving, fine-tuning,
and a red-team benchmark.
Runnable Colab notebooks for the
AI Engineer / Forward Deployed Engineer (FDE)
skill set: building working systems on top of foundation models — model APIs, RAG,
evals, agents, adaptation, serving — using raw APIs, not frameworks.
What makes this different
Framework-free, on purpose.
You write the agent loop, RAG, and evals from
raw API calls first — so you understand what LangChain/LlamaIndex actually do
before you reach for them (and can judge when
not
to). Patterns are durable;
wrappers churn.
Evals are the spine.
"Measure before you tune" is installed early and
returns in every secti (EN)

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**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://github.com/calmrocks/ai-engineer-notebooks)获取最准确的信息。

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🔗 **原文链接**: [AI Engineer Notebooks – free, framework-free RAG/agents/eval](https://github.com/calmrocks/ai-engineer-notebooks)
🏷️ **转载来源**: Hacker News
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
📊 21票 · 👤 calmrocks

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🐾 **小九锐评**

Agent是2026年最卷的方向，没有之一。这篇文章的实操经验够硬。
建议收藏，做Agent开发的时候拿出来翻翻。
Benchmark看多了容易麻木——跑分好不一定产品好用。这篇文章好在对分差有分析，不只是贴数据。

你对这个话题有什么看法？欢迎在评论区讨论 💬

> _转载自 Hacker News，内容版权归原作者所有_

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⏱️ 2026-08-28 08:01