## The Overlooked Time Black Hole

The Overlooked Time Black Hole

Two hours writing code, two hours writing the README—this is probably the most accurate portrait of many indie developers.

The project code has long been working, but as soon as you think about releasing it publicly, you have to add: project introduction, installation steps, dependency notes, configuration explanations, quick start examples, FAQs... None of these items are difficult on their own, but together they become a war of attrition. Even more troublesome are the example code samples: you have to make sure they actually run when others copy and paste them. If anything goes wrong—the local environment, API keys, dependency versions—the user just closes the repo and walks away.

I've tracked my own situation: for a medium-sized open source tool library, writing a complete README plus a runnable demo by hand takes an average of 3 to 4 hours. That doesn't even count the documentation rework caused by subsequent version updates. The situation is often worse for small teams—no one wants to write the docs, so they either end up abandoned or hastily cobbled together by the person who understands the project least.

Let's do the math: suppose you maintain two projects per month, investing 3 hours in documentation each time. That's 72 hours a year—nearly two full work weeks. Those 72 hours could have been spent writing new features, fixing bugs, or resting.

What AI Can Do for This

Let's clarify the division of labor: AI doesn't decide how your project should be presented. Instead, it takes what's already in your head and quickly structures it into documentation that others can understand.

Specifically, AI excels at handling these areas:

1. Deriving a documentation framework from code. Feed it your core code files, and it can extract function signatures, parameters, and dependency relationships, generating a README skeleton and a first draft of API documentation. You only need to fix the parts it misunderstood.

2. Generating runnable examples. Tell it the target user's runtime environment and entry point, and it can produce a minimal runnable demo script, including dependency installation commands and environment variable configuration notes. The key is to require "copy-paste ready to run" when prompting—the AI will proactively handle details like version compatibility and error handling.

3. Multi-language documentation sync. After writing the Chinese version, have it translate to English while keeping the Markdown formatting and code blocks untouched. This step is nearly zero cost.

4. Continuous documentation updates. When the code changes, send it the diff and have it update the corresponding README sections in sync, preventing documentation from drifting out of step with the code.

My Actual Workflow

I've now standardized the entire documentation production process into four steps, all done with AI assistance:

Step 1: Prepare materials (10 minutes). Organize the project's core code files, dependency list, and a one-sentence positioning statement. No need to clean up the code—AI understands "not elegant but functionally clear" code quite well.

Step 2: Generate the README first draft (5 minutes). Use the prompt template below to generate a structurally complete first draft in one go.

Step 3: Generate a runnable example (5 minutes). Send a separate round of prompts for the example, requiring the demo script to include four parts: installation, configuration, running, and expected output.

Step 4: Manual verification (10-20 minutes). Actually run the example on your own machine, check that the API descriptions are accurate, and adjust the tone and emphasis.

Below is the prompt template I've refined and settled on—just replace the bracketed content and it's ready to use:

你是一位资深开源项目维护者。请根据我提供的代码，生成一份高质量的项目README。

【项目信息】
- 项目名称：[名称]
- 一句话定位：[这个项目解决什么问题]
- 目标用户：[谁会用它，他们的技术水平]
- 运行环境：[如 Python 3.10+ / Node 18+]

【要求】
1. 结构包含：项目标题与简介（含badge占位）、核心特性（不超过5条）、
   快速开始、安装步骤、使用示例、配置项说明、目录结构、License
2. 快速开始部分必须做到：用户复制粘贴命令即可完成安装和首次运行
3. 所有示例代码必须是完整可运行的，不要用省略号
4. 语气面向[独立开发者]，避免过度营销用语
5. 配置项用表格呈现，标注必填/可选

【代码如下】
[粘贴核心代码，建议不超过3个主文件]

When generating example code, I append one more sentence: "Please additionally output a minimal example in the examples/quickstart directory, including the complete steps mentioned in the README, with comments in the code indicating where each parameter comes from."

Before and After: The Efficiency Math

Using this workflow, I've tracked my documentation production time. Here's the before-and-after comparison:

That's about 3 hours saved per round, roughly a 6x efficiency improvement. More important is the change in quality: in the handwritten era, I often skipped time-consuming but useful sections like "FAQ" and "configuration tables"—now these are standard. With AI filling in error handling for the example code, user feedback about "can't get it to run" has noticeably decreased.

On cost: these documentation generation tasks don't consume many tokens. For specific pricing, refer to the official pricing page, but compared to the 3 hours of human time saved, the cost is negligible. For anyone maintaining multiple projects per month, the accumulated time saved over a year, based on the numbers above, is enough to develop an entire new feature module.

A Few Practical Tips

• Don't feed all your code at once. Prioritize entry files and core modules—too much mixed context actually reduces generation quality.
• Always run the examples yourself. AI-generated examples occasionally have outdated version numbers; don't skip the 5-minute verification.
• Save your prompts as templates. Only change the bracketed content each time to keep the output format stable and easy to reuse across projects.
• Use this workflow for documentation updates too. During version iterations, send the old/new code differences to AI and have it output incremental README changes—it's faster and more accurate than generating from scratch.

Documentation is no longer a burden before release, but a step that gets done naturally along the way in the development process. If you're maintaining open source projects, or want to organize and publish your pile of half-finished code, start with this most easily quantifiable scenario to experience the efficiency gap AI brings.

The multi-model calling service I currently use is ThisToken.AI, which supports calling mainstream models through a unified interface. For documentation generation tasks like these, a cost-effective model is sufficient—just switch to a more powerful model when encountering complex code comprehension. New users can register and try it here: https://api.thistoken.ai/register

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Ready to try it yourself? Sign up at https://api.thistoken.ai/register to get your API key and start building.