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github-star-benchmarks

AI research dataset on GitHub stars: 32.6M starred repositories, star thresholds (100/1k/10k/100k), language and age cohorts, plus star-buying and fake-star research context from ICSE 2026. Open CSVs and reproducible queries for empirical software engineering, scientometrics and LLM evaluation.

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GITHUB · launched Oct 18, 2026 · 16 upvotes (top 73%)

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Could you build this?

Partial The presentation layer (articles, CSV downloads, queries) is easy to build, but the core asset is a 32.6M-repository dataset that requires large-scale crawling and careful sampling, which needs real data infrastructure and research-grade methodology.

What it would actually take: Pull repository metadata from GitHub Archive/BigQuery or the GitHub API with rate-limit-aware crawlers, store it in a columnar database such as DuckDB or Parquet on object storage, and publish reproducible SQL and CSV exports. The hard parts are the cost and engineering of collecting and deduplicating tens of millions of records, and the statistical and methodological rigor needed for credible research claims about fake stars and benchmarks.

Competitors

Other products that read as similar to this one : 100 launches clear the similarity bar, closest 8 shown.

Attention rank: #82 of 101 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 342 days after the earliest competitor.

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