# espadrine/succinct-cybernetics

Computer Science Cheatsheets.

Repository: https://github.com/espadrine/succinct-cybernetics
Canonical: https://ross.abutalabs.com/products/succinct-cybernetics
Language: JavaScript
License Family: other
Last push: 2020-12-03T15:47:27+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": 3999, "days_push": 2099, "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 1591, forks 89 (observed 2026-08-28T04:05:08.803666+00:00)

## What it is
A collection of succinct computer science cheatsheets covering data structures like graphs, trees, lists, maps, and sets, plus topics such as complexity, memory, networking, and cryptography. It is a reference document rather than executable software.

## Use cases
- quickly review data structure properties before an interview
- look up time complexity of common algorithms
- refresh knowledge of graph and tree concepts
- study cryptography and networking fundamentals concisely
- find a compact reference for computer science theory

## When to choose
- you want concise, well-structured CS reference material
- you need a quick refresher on data structures or complexity
- you prefer cheatsheet-style documentation over long textbooks

## When to avoid
- you need executable code or a software library
- you want in-depth tutorials with worked examples
- you need a regularly updated, actively maintained resource

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation, developer-tools
- domain: education, tutorials
- platform: -
- tags: cheatsheets, computer-science, reference, data-structures, algorithms, web-server

## Member repositories
- espadrine/succinct-cybernetics (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:08.803666+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 2026-08-30T03:54:44.072671+00:00, confidence not recorded.
  - readme: https://github.com/espadrine/succinct-cybernetics (fetched 2026-08-28T04:05:08.803666+00:00, sha 0ee39888b7a0)
- Data as of 2026-08-30T08:39:29.467469+00:00.