There's a line often repeated in open source circles: the code is free, the code is open, anyone can use it. And that's true, as far as it goes. But there's a quiet assumption buried in that promise. It assumes you can read the documentation. It assumes the getting-started guide, the troubleshooting page, the architecture overview are all written in a language you understand. For most open source projects, that language is English. For the hundreds of millions of potential users who don't read English fluently, the door is only half open.
I've been thinking about this problem in concrete terms because I just finished translating the entire documentation set for OpenClaw, the open source AI assistant project, into 22 languages. All 312 documents. Every getting-started guide, every configuration reference, every troubleshooting runbook. Japanese, Korean, Simplified and Traditional Chinese, Spanish, French, German, Portuguese, Arabic, Hindi, Urdu, Vietnamese, Thai, Filipino, Swedish, Danish, Dutch, Polish, Turkish, Russian, Burmese, and Uzbek. That's 574,000 translated strings covering the languages most requested by the community. The link to the Crowdin site is below.
https://crowdin.com/project/66aa453b091ff31e16a4cecfeef31b17
The technical details matter here, because they illustrate what's now possible. I used OpenAI's latest model to generate base translations, with per-language glossaries to ensure that technical terms like "gateway," "session," and "skill" were handled consistently. The translations were then uploaded to Crowdin, an open translation management platform, where the community can review, suggest improvements, and vote on alternatives. The whole pipeline is programmatic: when the English docs change, the system identifies new and modified strings automatically. The infrastructure is designed to be maintained, not just built once and abandoned.
But the technical work isn't really the point. The point is what it enables.
Open source projects talk a lot about contributor accessibility. There are mentorship programs, "good first issue" labels, contributor guides. These are valuable. But they all assume a baseline: you need to be a coder, or at least code-adjacent, to contribute meaningfully. Documentation localization changes that equation entirely. If you speak Korean and English, you can review a Korean translation and flag where the AI produced something unnatural. If you're a native Arabic speaker who uses OpenClaw, you know whether a translated troubleshooting guide actually makes sense to someone working through a problem in Arabic. You don't need to write a single line of code to make a material contribution to the project.
This is especially relevant for a project like OpenClaw, which is growing rapidly and attracting users from communities that are underserved by English-only documentation. The GitHub issue tracking internationalization requests had comments from volunteers in over a dozen countries, many of them offering to help translate in their native language. The infrastructure is now in place for them to do exactly that. The Crowdin project is public. Anyone can sign in, pick a language, and start reviewing.
There's a broader principle at work here. The value of open source software scales with the size of its community. A project that only serves English speakers is leaving most of the world's developers, tinkerers, and enthusiasts on the other side of a language barrier. Removing that barrier doesn't just help those users. It helps the project. Every new user who can actually read the docs is a potential contributor, bug reporter, or evangelist. The return on investment for documentation localization is asymmetric in the best way: relatively low cost, with compounding benefits as the community grows.
And the way to harness that bigger community is by inviting them to do what AI can't do alone. There's a reasonable counterargument that AI-generated translations are good enough, that the base quality from a model like GPT-5.2 is sufficient and human review is unnecessary overhead. I think that undersells both the problem and the opportunity. AI translations are a strong starting point, but they have predictable failure modes: overly literal phrasing, context-dependent terms translated inconsistently, cultural nuances in formality and tone that a model trained primarily on English text will miss. Native speakers catch these things in seconds. The AI gets you to 90%; the community gets you to 100%.
The total cost tells a story worth hearing. The first attempt used OpenAI's premium model, gpt-5.2-pro, and ran up $1,700 before being caught. That was a painful lesson in model selection. I limited gpt-5.2-pro to glossary development, and switched to gpt-5.2-chat-latest with support from that scaffolding. After rebuilding the pipeline, I completed the remaining translations for roughly $215. That's $215 to translate 312 documents into 22 languages, covering ~574,000 strings. The Crowdin platform is free for open source projects. The ongoing maintenance cost is close to zero for unchanged documents and marginal for updated ones. The lesson isn't that this is free. It's that it's affordable enough to be a continuing positive for any project with a global user base, as long as you pick the right tools and learn from the expensive mistakes quickly.
My ask to those who read this is straightforward: if you speak one of these 22 languages, go to the Crowdin project and spend fifteen minutes reviewing translations in your language. That's it. You don't need to be a developer. You don't need to set up a build environment. You just need to know your language well enough to tell when a sentence sounds right and when it doesn't. That's a contribution that matters, and it's one that only a human can make.
Open source means the code is open. It should also mean the documentation is open, in every language the community needs it to be. We have the tools to make that happen now. We just have to use them.