# hwayne/awesome-cold-showers

For when people get too hyped up about things

Repository: https://github.com/hwayne/awesome-cold-showers
Canonical: https://ross.abutalabs.com/products/awesome-cold-showers
License: NOASSERTION
License Family: other
Topics: programming
Last push: 2024-01-05T01:14:55+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": 3172, "days_push": 972, "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 7345, forks 82 (observed 2026-08-28T04:09:58.354812+00:00)

## What it is
A curated list of articles and papers that temper hype around overhyped programming topics, such as formal verification, static typing, and big data systems. Each entry pairs a common hype claim with evidence-based counterpoints, caveats, and notes.

## Use cases
- find research questioning whether static typing reduces bugs
- check if big data tools are actually faster than a laptop
- read skeptical takes on formal verification
- ground a team before adopting an overhyped technology
- find balanced literature reviews on programming debates

## When to choose
- you want evidence-based counterpoints to popular programming hype
- you're evaluating a technology and want to see skeptical perspectives
- you enjoy curated, annotated reading lists on software engineering debates

## When to avoid
- you need tools or code rather than articles and papers
- you want one-sided advocacy for a technology
- you need up-to-date coverage, since some linked material is dated

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation
- domain: developer-tools, tutorials, awesome-lists
- platform: -
- tags: awesome-list, critical-thinking, hype-correction, curated-links, web-server

## Member repositories
- hwayne/awesome-cold-showers (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:58.354812+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 2026-08-29T17:38:27.344742+00:00, confidence not recorded.
  - readme: https://github.com/hwayne/awesome-cold-showers (fetched 2026-08-28T04:09:58.354812+00:00, sha 258cefd6070a)
- Data as of 2026-08-30T08:39:29.467469+00:00.