# aspittel/coding-cheat-sheets

Various cheat sheets on CS stuff

Repository: https://github.com/aspittel/coding-cheat-sheets
Canonical: https://ross.abutalabs.com/products/coding-cheat-sheets
License Family: other
Topics: algorithms, datastructures, python
Last push: 2024-06-20T17:56:24+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3419, "days_push": 804, "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 1339, forks 236 (observed 2026-08-28T04:04:25.857886+00:00)

## What it is
A collection of concise Markdown cheat sheets covering core computer science topics like algorithms, data structures, sorting, searching, and Big-O notation. It serves as a quick reference for students and developers reviewing CS fundamentals.

## Use cases
- quickly review how binary search trees work before an interview
- look up differences between merge sort and quick sort
- refresh my memory on Big-O notation complexity
- study recursion and divide and conquer concepts
- get a refresher on hash tables and heaps
- prepare for coding interview data structure questions

## When to choose
- you want short, digestible reference notes on CS fundamentals
- you are preparing for technical interviews and need quick reviews
- you prefer plain Markdown notes over interactive courses

## When to avoid
- you need in-depth tutorials with runnable code examples
- you need up-to-date or comprehensive coverage of advanced topics
- you want an interactive learning platform with exercises

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation
- domain: algorithms, education, tutorials
- platform: cross-platform
- tags: cheat-sheets, interview-prep, big-o-notation, sorting, searching, markdown-notes, algorithms, computer-science

## Member repositories
- aspittel/coding-cheat-sheets (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:25.857886+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 2026-08-30T04:43:53.814835+00:00, confidence not recorded.
  - readme: https://github.com/aspittel/coding-cheat-sheets (fetched 2026-08-28T04:04:25.857886+00:00, sha ba9feae651f1)
- Data as of 2026-08-30T08:39:29.467469+00:00.