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Show HN: Devthropology – Better Insights for GitHub Repos

dpc94 2026年07月10日 00:47 1 次阅读 来源:HackerNews

Devthropology is a passion project built on top of GitHub pull data. The name is a play on developer anthropology. Pull request data can be cut a lot of ways. The functionality has been built out of curiosity as I want to see different insights into codebases that I work on. Some of the data is typical and other parts I haven't seen elsewhere. I think of this as an improved GitHub Insights page, with faster performance, more detail, and a focus on how work moves through a codebase. The main enti

Devthropology is a passion project built on top of GitHub pull data. The name is a play on developer anthropology. Pull request data can be cut a lot of ways. The functionality has been built out of curiosity as I want to see different insights into codebases that I work on. Some of the data is typical and other parts I haven't seen elsewhere. I think of this as an improved GitHub Insights page, with faster performance, more detail, and a focus on how work moves through a codebase. The main entity is a contributor, which has two sides: authoring PRs and reviewing/giving feedback to others. From there, you can see repository wide stats, user interactions, contribution trends, file health, and collaboration patterns. Some insights are useful for understanding velocity and code health in the AI era. Details for each page: - Homepage: A high level summary of the repository. Showing age, file types, active contributors, new and churned users. I track the author age at merge, so you can see the tenure of people shipping changes over time. - File explorer: One of my favorite parts. I build a graph of files, tracking renames and moves, to build a complete history. Rolling up, every file and folder is assigned an outlook such as active, developed, stale, touched by people who are likely gone. You can easily see contributor timelines, recent changes, and for some files, their rename/move history and related files that often change together (useful for a coding agent). - Trends: The densest page, showcasing the velocity of contributions and trying to understand if AI is helping ship more. Charts are cut by year for comparisons, tracking PR size, output, rounds of review, and approval latency by different percentiles. PRs are further cut into bucketed sizes to help drill in deeper. Helps to show that smaller PRs are likely still faster to ship while very large PRs (product of AI?) are slowing down. - Relationships: A graph of interactions between contributors, weighted by PR ac
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