有人让遗留代码库对 AI 编码助手更具可读性吗？
2 分•作者: iacobandrei•10 天前
快速摘要：你是否曾优化过遗留代码/vibe 代码库以提高 AI 驱动的开发结果，并避免修复一个 bug 导致出现另外两个 bug？我想听听你的经验。
首先，一些背景。大约 7 个月前，我加入了这家（现已成立）2 年的初创公司，成为第一位工程师。代码库最初是由我们的低层 CTO 构建的一个可爱的 MVP。一个月前，我们招聘了第二位开发人员，我们正在进一步扩大我们的工程团队，并为我晋升为 Staff 工程师做准备。
现在，正如你可能预料到的那样，代码库是一团糟，因为我们只是在第一个 MVP 的基础上进行构建，而且完全没有文档。我在代码库中发现的主要问题是：
* 重复的业务逻辑 → 没有单一事实来源/关注点分离不佳。
* 僵尸表和列 → 累积的模式/结构债务，其中大部分看起来是对的，但实际上并非如此。
* 我们手动跟踪下游影响，因为一切都以最混乱的方式分散和重复 → 隐含依赖、隐含架构和高变更耦合。在这里改变一个东西也需要在那里和那里改变（这主要可以通过代码库图索引器来修复）。
现在，为了不让你感到厌烦，我发现了一个介于速度和可靠性之间的最佳解决方案：为整个代码库进行适当的文档记录，并将其有效地存储为我们 AI 代理的“知识数据库”，这样它们至少能够了解已知的差距、约束、决策、业务逻辑、在哪里可以更改某物以及变更的原因和结果。
最接近且最有趣的文章，处理的是这个确切的问题，是这篇来自 Meta 的文章，我希望从这里开始我的方法。
现在我在这里询问的是一些类似的经验，其他初创公司的工程师是否经历过类似的方法，他们的方法、经验、结果是什么，以及关于我应该避免或注意的事项的任何建议。
任何帮助都将不胜感激。
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Quick Summary: Did you ever optimize a legacy/vibe-coded codebase to improve AI driven development results and avoid fixing a bug for 2 more to appear? i want to hear what your experience was.<p>First, some context. ~7 months ago i was the first Engineering hire at a ( now ) 2 years old startup. The codebase started as a Lovable MVP built by our low level CTO, one month ago we hired a second dev and we are further expanding our engineering team and preparing the terrain for me to move to Staff.
Now as you probably expected, the codebase is a mess since we simply built on top of the first MVP, of course with zero documentation, the main problems i identified in our codebase:
- Duplicated business logic → no single source of truth / poor separation of concerns.
- Zombie tables and columns → accumulated schema/structural debt, most of them look right, they are not
- We manually track downstream effects since everything is scattered and duplicated in the most confusing way → implicit dependencies, implicit architecture and high change coupling. Changing a thing here also needs changing there and there ( this is mainly fixable by a codebase graph indexer )<p>Now quickly, so you dont get bored, ive identified as the sweet spot solution between speed and reliability to properly document the whole codebase and store that efficiently as a 'knowledge database' for our AI agents, so they are at least aware of the known gaps, constraints, decisions, business logic, where else to change something and the causes and effects of changes.<p>The closest and most interesting article that treats this exact issue is this one from Meta, which i want to start my approach from.<p>Now what im asking here is for some similar experiences, other startup engineers that had to go through a similar approach, what was their approach, experience, outcome and any tips on what should i avoid or be aware of.<p>Any help will be much appreciated