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growth: measured 'built by AI, for AI agents' positioning to attract AI-assisted contributors (after #1275)

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Los mantenedores suelen responder en 1 día

Nadie ha tomado este issue todavía.

Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
25/100
Tipo de issue
Nueva funcionalidad
Claridad
Bien especificado
Estado de actividad
Activo
Stack tecnológico
c
Área
developer-experience, documentation, testing-qa

Línea de trabajo

Start by reading #1275 and the repository's existing contributor documentation, then map the benchmark script, docs site page, CONTRIBUTING.md, docs/llms.txt, make precheck, and issue forms. Done means a reproducible protocol, runs on at least three models and a baseline, a dated public results page, updated contributor guidance, and five qualifying good-first-issue issues.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

area:docs kind:decision

Goal. Bring in more AI-assisted contributors by making the claim "EigenScript is built by AI, for AI agents to work on" measured and public, not asserted.

Why this compounds. An AI contributor's cost is what #1275's benchmark measures: tokens, precheck/CI rounds, and whether the first push is green. Lower cost means more contributions. Each contribution surfaces a gap, and the forcing-function rule routes it into docs, gates or llms.txt, which lowers the next contributor's cost. Evidence both ways: the heaviest outside contributor (AI-assisted, 63 PRs) stopped on 2026-08-20, as contributor-facing burden grew (#1275).

First data point (2026-09-24, #1275). A fresh claude-sonnet-5, given only the repo's docs, fixed #1236 with a regression test in 9.8 min for $2.21. Its first push was green on 33/33 checks (#1300).

Sequencing. Starts after #1275 closes. Publishing the benchmark mid-consolidation would advertise the burden being removed.

Done when:

  • The benchmark protocol is a script in the repo. It covers the history-free snapshot, a stub origin/main, the suite run in the foreground, and the metrics captured from the agent's result event, so any run is reproducible by command.
  • It has run on ≥ 3 models (e.g. Sonnet 5, Opus 5.5, GPT-6 Sol) and on a baseline: a comparable one-line stdlib bug plus a test in an established language's repo, the same protocol. Without the baseline, the claim is a data point, not a comparison.
  • A public page (docs site) shows the table, the protocol and the date, with a re-run date stated.
  • CONTRIBUTING.md has a short "Contributing with an AI agent" section that points the agent at docs/llms.txt, make precheck and the issue forms' "Done when". It is kept lean, per the contributor-facing-docs rule.
  • ≥ 5 open issues carry good first issue with a "Done when" an agent can verify.
  • The wording says "for AI agents" / "AI-written software", not "AI language" (Mojo's meaning).
Lenguaje dominante
C
Estrellas
3
Forks
7
Merge medio
3 h 58 min
PR fusionados (30 d)
105

Preparar el entorno

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Inicia el contenedor de desarrollo del proyecto en tu navegador, con tu propia cuenta de GitHub.

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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