# ai-engineering-hub 引用與授權說明 / ai-engineering-hub Citation & License Notice

> 🌐 語言：繁體中文 ｜ [English](AI_ENGINEERING_HUB_REFERENCE_EN.md) ｜ [Deutsch](AI_ENGINEERING_HUB_REFERENCE_DE.md) ｜ [Français](AI_ENGINEERING_HUB_REFERENCE_FR.md) ｜ [Español](AI_ENGINEERING_HUB_REFERENCE_ES.md) ｜ [日本語](AI_ENGINEERING_HUB_REFERENCE_JA.md) ｜ [한국어](AI_ENGINEERING_HUB_REFERENCE_KR.md)

本方法論的 [`04_Scenarios/LLM_APPS_CASE_INDEX.md`](../04_Scenarios/LLM_APPS_CASE_INDEX.md)（LLM 應用案例索引表）之**部分案例清單與分類**，參考自 **patchy631/ai-engineering-hub**。本檔詳列原始來源、授權條款與引用範圍。

The case list and categories in part of this methodology's [`04_Scenarios/LLM_APPS_CASE_INDEX.md`](../04_Scenarios/LLM_APPS_CASE_INDEX.md) are referenced from **patchy631/ai-engineering-hub**. This document records the upstream source, license terms, and scope of citation.

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## 1. 原始專案 / Upstream Project

| 項目 / Field | 內容 / Value |
| --- | --- |
| **專案名稱 / Project** | ai-engineering-hub |
| **作者 / Author** | patchy631（含 "Daily Dose of Data Science" newsletter） |
| **GitHub URL** | <https://github.com/patchy631/ai-engineering-hub> |
| **授權 / License** | **MIT License** |
| **內容 / Content** | 93+ 個可執行的 AI 工程專案，分初/中/進階三級；含 Jupyter Notebook 與 Python 原始碼 |

## 2. ai-engineering-hub 是什麼 / What It Is

一套 **code-first 的 AI 工程學習課程庫** — 93+ 個 production-ready 專案，涵蓋 OCR & Vision、Chat 介面、RAG 系統、Agents & Multi-Agent、Voice & Audio、Fine-tuning、進階基礎建設（MCP）、模型比較。與單純的連結清單不同，它提供**可實際執行的程式碼實作**與循序漸進的學習路徑。

A **code-first AI engineering learning hub** — 93+ production-ready projects across OCR & Vision, chat interfaces, RAG systems, agents & multi-agent, voice & audio, fine-tuning, advanced infrastructure (MCP), and model comparisons. Unlike a link directory, it provides runnable code implementations and a structured learning path.

## 3. 我們引用了什麼 / What We Cited

| 範圍 / Scope | 處理方式 / Treatment |
| --- | --- |
| **代表性專案名稱與分類** | 引用作為 `LLM_APPS_CASE_INDEX.md` 的索引條目（標示來源 H）/ Cited as index entries (source tag H) |
| **L1-L5 對應、課程對應、應用情境註記** | **本方法論原創** / This methodology's original work |
| **專案的原始碼 / Notebook** | **未重製、未 fork**；學員若要實作須自行從上游 clone / **Not reproduced, not forked** |

> 本表收錄 ai-engineering-hub **有明確名稱的代表性專案**；其 93+ 專案的完整清單請見上游 repo，未來可持續補入索引。
> The index covers the named, representative projects of ai-engineering-hub; for the full 93+ list see the upstream repo.

## 4. 授權條款摘要 / License Summary

ai-engineering-hub 採用 **MIT License** — 允許自由使用、修改、再散布、商業使用，並可作為閉源產品的一部分；**唯一條件**是再散布原始碼時保留 MIT 著作權聲明與授權文字。

本方法論**未重新散布**其原始碼，僅在 `LLM_APPS_CASE_INDEX.md` 中引用代表性專案名稱與分類作為索引。學員若要實際執行某個專案，請自行從上游 repo 取得並遵守其 MIT 條款。

ai-engineering-hub is under the **MIT License**. This methodology does not redistribute its source — it only cites representative project names and categories as an index in `LLM_APPS_CASE_INDEX.md`.

## 5. 學員引用建議格式 / Citation Format for Learners

> Projects indexed from ai-engineering-hub by patchy631 — <https://github.com/patchy631/ai-engineering-hub> (MIT License)
> 專案索引自 patchy631 之 ai-engineering-hub — <https://github.com/patchy631/ai-engineering-hub> (MIT 授權)

## 6. 免責聲明 / Disclaimer

本方法論中對 ai-engineering-hub 之專案對應 L1-L5 與課程之判斷，皆為本方法論作者（Morris Lu / Tiger AI 虎智科技）的詮釋，不代表 patchy631 之官方立場。ai-engineering-hub 的專案多為教學 / 起始實作，企業導入時須套用本方法論的治理機制（人工 Gate、Evidence、Stage Gate、資安檢核）。若上游專案清單有版本變動，請以 [上游 repo](https://github.com/patchy631/ai-engineering-hub) 之最新版本為準。

Any mapping of ai-engineering-hub projects onto L1-L5 and courses in this methodology represents the interpretation of the methodology author (Morris Lu / Tiger AI 虎智科技) and does not represent the official position of patchy631. The projects are learning / starter implementations; enterprise adoption must apply this methodology's governance. If the upstream project list changes, refer to the [upstream repository](https://github.com/patchy631/ai-engineering-hub).
