# GrindGold/pdf

📚 计算机经典编程书籍、大黑书、编程电子书、电子书、编程书籍，包括计算机基础、C/C++、Java、Python、面试题、架构设计、算法系列等经典电子书。

Repository: https://github.com/GrindGold/pdf
Canonical: https://ross.abutalabs.com/products/pdf
License Family: other
Topics: book, books, computer-science, pdf
Last push: 2024-01-09T12:32:29+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": 1406, "days_push": 967, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4708, forks 515 (observed 2026-08-28T04:08:57.361520+00:00)

## What it is
A curated collection of classic computer science and programming ebooks in PDF format, covering C/C++, Java, Python, Go, Linux, algorithms, system design, and interview questions. It is a free resource repository intended for self-learners and IT professionals.

## Use cases
- download free programming ebooks in pdf
- find classic computer science books
- prepare for coding interviews with pdf materials
- learn c++ python java from recommended books
- collect algorithm and system design books
- self-study computer science fundamentals

## When to choose
- you want a one-stop free collection of programming ebooks
- you are self-studying computer science and need book recommendations
- you need offline pdf references for languages like C/C++, Python, or Go

## When to avoid
- you need a legal, license-cleared source for distributing copyrighted books
- you want interactive courses rather than static pdf files
- you require regularly updated technical documentation

## Facets
- artifact type: dataset
- maturity: active
- function: pdf, documentation
- domain: education, developer-tools
- platform: cross-platform
- tags: ebooks, programming-books, computer-science, pdf-collection, learning-resources, interview-preparation

## Member repositories
- GrindGold/pdf (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:57.361520+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 2026-08-29T18:19:09.921385+00:00, confidence not recorded.
  - readme: https://github.com/GrindGold/pdf (fetched 2026-08-28T04:08:57.361520+00:00, sha c0ffc4ad193c)
- Data as of 2026-08-30T08:39:29.467469+00:00.