# dtolnay/case-studies

Analysis of various tricky Rust code

Repository: https://github.com/dtolnay/case-studies
Canonical: https://ross.abutalabs.com/products/case-studies
Language: Rust
License: Apache-2.0
License Family: permissive
Last push: 2026-07-18T21:19:01+00:00

## Health v2 (maintenance only)
Score: 74/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 93, release rhythm 35, longevity 100
- inputs: {"age_days": 2671, "days_push": 46, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2038, forks 53 (observed 2026-08-28T04:06:08.294239+00:00)

## What it is
A collection of case studies analyzing tricky Rust code patterns, drawn from dtolnay's experience with advanced macro libraries. It teaches that macro expertise comes from mastering ordinary Rust language features rather than macro mechanics themselves.

## Use cases
- learn advanced Rust code patterns
- understand what procedural macros actually generate
- improve my Rust macro development skills
- study tricky Rust code examples
- prepare for writing proc macro libraries
- understand macro_rules edge cases

## When to choose
- you are an intermediate-to-advanced Rust developer working on macros
- you want to understand the code emitted by 'magical' macro libraries
- you prefer learning through annotated real-world examples

## When to avoid
- you are a Rust beginner looking for introductory material
- you need a step-by-step procedural macro tutorial (use proc-macro-workshop instead)
- you need runnable library code rather than written analysis

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, developer-tools
- domain: programming-languages, tutorials, developer-tools
- platform: cross-platform
- tags: rust, macros, case-studies, procedural-macros, advanced-rust

## Member repositories
- dtolnay/case-studies (main) score 74

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:08.294239+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 2026-08-30T02:59:14.299204+00:00, confidence not recorded.
  - readme: https://github.com/dtolnay/case-studies (fetched 2026-08-28T04:06:08.294239+00:00, sha e2519930224b)
- Data as of 2026-08-30T08:39:29.467469+00:00.