# quchen/articles

Miscellaneous articles. The readme is the table of contents.

Repository: https://github.com/quchen/articles
Canonical: https://ross.abutalabs.com/products/quchen-articles
Language: Haskell
License Family: other
Topics: haskell, hindley-milner, articles, tutorials
Last push: 2023-07-12T09:28:11+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": 4955, "days_push": 1148, "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 1311, forks 89 (observed 2026-08-28T04:04:20.117420+00:00)

## What it is
A collection of miscellaneous articles by quchen, mostly Haskell tutorials and essays, with the README serving as a table of contents. Topics include monads, type inference (Hindley-Damas-Milner), lens operators, and Haskell coding style.

## Use cases
- learn Haskell monads like Cont and Reader
- understand the Hindley-Milner type inference algorithm
- find explanations of common Haskell idioms and pitfalls
- get a lens infix operators cheat sheet
- read about lazy IO pitfalls in Haskell
- learn to write a Brainfuck interpreter in Haskell

## When to choose
- you want well-written, informal Haskell tutorials and explanations
- you need a readable introduction to type inference algorithms
- you are looking for practical Haskell style and technique advice

## When to avoid
- you need structured course material or exercises
- you want actively maintained documentation for a specific library version
- you need non-Haskell programming content

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation
- domain: tutorials, programming-languages, education
- platform: -
- tags: haskell, articles, type-systems, functional-programming, hindley-milner

## Member repositories
- quchen/articles (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:20.117420+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 2026-08-30T04:49:51.832108+00:00, confidence not recorded.
  - readme: https://github.com/quchen/articles (fetched 2026-08-28T04:04:20.117420+00:00, sha 37bd5c65292b)
- Data as of 2026-08-30T08:39:29.467469+00:00.