在编程资源中，如何从信号中过滤噪声？
这些天，每当我查看各种包、库或项目的生态系统时，都会发现数百个甚至更多功能和行为重叠的项目。<p>其中一些项目乍一看似乎不错，拥有精美的自述文件、近期的活动、提交历史、功能列表等。但当你深入查看代码时，它们却往往令人失望，质量低劣、维护困难，补丁杂乱无章，每隔一个版本就会破坏兼容性。人工智能使这种情况变得更加严重。<p>过去，如果一个项目在其细分领域内拥有相对较高的 GitHub 星标和活跃度，那么这通常意味着该项目具有一定的质量、潜力或有效性，你可以审查1到3个项目，基本上这可以作为一个良好的初步筛选，而如今则有成百上千个项目。<p>你如何在噪音中找到信号，有哪些技巧和窍门？我使用的一些启发式方法包括：多个贡献者、每天的提交量越少越好。如果作者使用人工智能，一个小的信号是他们是否限制或阻止接受来自代码库或其他 GitHub 仓库的拉取请求，当然还有 AGENTS/CLAUDE.md，尤其是 CLAUDE.md。<p>我很好奇你发现哪些信号有用，特别是那些难以操控且可以半自动化的信号，这样最后你只需审查剩下的最少数量的项目？例如，位置、文件数量、其他注册表？
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These days, whenever I find myself looking at the ecosystem of packages, libraries or projects I find hundreds or even more projects that overlap in functionality/behavior.<p>Some of them look good at a quick glance, polished readmes, recent activity, commit history, features lists..etc. But when you look at the code they turn surprisingly bad, surprisingly fast, low effort, low quality, unmaintainable messes of patches that break compatibility every other version. AI has made this 100x worse.<p>In the past, if a project had a relative high number of gh stars and activity in relation to the niche that would mean the project had some standard of quality/potential/efficacy and you could review 1-3 projects, basically it acted as a good first filter, today there are hundreds.<p>How do you find the signal in the noise, tips/tricks? Some heuristics I use: multiple contributors, less is more when it comes to commits per day, if the author uses AI a small signal is if they limit/block acceptance of PRs, codeberg or outside github repos and of course AGENTS/CLAUDE.md, especially CLAUDE.md.<p>Curious what signals you found useful, especially ones that are difficult to game and can be semi-automated so at the end you only review the fewest number of them left? loc, file count, other registries?