# zhangbc/eBooks

eBook分享大集合：主要以IT领域经典书籍收藏，以备不时之需。

Repository: https://github.com/zhangbc/eBooks
Canonical: https://ross.abutalabs.com/products/zhangbc-ebooks
License: LGPL-3.0
License Family: copyleft
Topics: it, ebook-reader
Last push: 2021-05-14T17:31:00+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2577, "days_push": 1937, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2106, forks 758 (observed 2026-08-28T04:06:13.992668+00:00)

## What it is
A curated collection of classic IT eBooks, primarily in Chinese, covering server systems, AI/ML, algorithms, networking, and programming languages like C/C++, Python, and Java. It serves as a shared repository of book files (including large files via LFS) for reference and study.

## Use cases
- download classic IT programming books for free
- find machine learning and deep learning textbooks
- get algorithm and data structure books like CLRS and剑指offer
- access Java and Python reference books
- build a personal IT ebook library
- find networking books like TCP/IP Illustrated

## When to choose
- you want a one-stop collection of well-known IT books in Chinese
- you need offline copies of classic CS textbooks for study
- you are preparing for interviews and want algorithm books

## When to avoid
- you need legally licensed or up-to-date editions of books
- you want English-language books only
- you need software or tools rather than reading material

## Facets
- artifact type: dataset
- maturity: maintenance
- function: documentation, developer-tools
- domain: developer-tools, education, tutorials
- platform: cross-platform
- tags: ebooks, it-books, book-collection, pdf-books, learning-resources, chinese-language

## Member repositories
- zhangbc/eBooks (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:13.992668+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:54:00.888146+00:00, confidence not recorded.
  - readme: https://github.com/zhangbc/eBooks (fetched 2026-08-28T04:06:13.992668+00:00, sha cb69ca4d71c6)
- Data as of 2026-08-30T08:39:29.467469+00:00.