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Docusaurus i18n: How to keep translations in sync (manual vs Crowdin vs GitHub Action)

If you maintain a Docusaurus site in more than one language, you already know the actual problem isn't translation — it's staying in sync . Someone updates three paragraphs in the English docs, and six months later the Chinese (or Spanish, or whatever) version is quietly wrong, and nobody notices until a user files an issue about it. I went looking at how teams actually solve this, and it mostly comes down to three approaches. Writing this down mostly for my own reference, but sharing in case it saves someone else the research. Approach 1: Just do it manually This is what most small-to-mid docs sites do, at least at first. A maintainer (or a translator on Slack) watches for doc PRs and manually updates the other language folders. It works fine until it doesn't. The failure mode is always the same: it's invisible. Nobody gets paged when a translated page goes stale — it just sits there, slightly wrong, until a reader notices the code sample doesn't match anymore. For a project with a handful of docs and one contributor doing translations, this is honestly fine. Past ~50-100 pages or more than one language, it stops scaling — not because the translation work is hard, but because tracking what changed becomes a full-time job nobody signed up for. Approach 2: A translation management platform (Crowdin, Lokalise, etc.) These are built for exactly this problem and they're genuinely good at it — string extraction, translator workflows, in-context editing, the works. If you have a dedicated localization team or professional translators involved, this is probably still the right call. The tradeoff for a docs-only, engineering-driven project: they're built around the assumption that there's a human translator (or a review pipeline) doing the actual translating, plus a separate sync step to pull translations back into your repo. That's the right tool when translation quality and nuance matter enormously (marketing copy, legal text) or when you have translators who aren't devel

2026-07-28 原文 →
AI 资讯

Nine Months of Nagging, Zero Reading

🦄 I shipped a linter that fails your commit if you won't admit AI touched the code, and then did the most predictable thing possible—let nine months of the data sit there untouched while I busied myself with other things. Then I actually looked at it: nine months of footers piled up in git log like a lonely change jar. Every one of them said how much of those commits were mine, but I hadn't ever sat down and actually counted the jar. So I built the thing to count it. 🪙 The Jar Nobody Counted 🫙 Nine months of commits, every single one carrying a footer that states how much of it I actually wrote, and I could not have told you the number—not roughly or even within twenty points. It was all sitting in git log , structured, and enforced on every commit by a hook I built specifically for that purpose. But it was completely inert. Dropping change in a jar isn't the same as knowing how much money is in it. rai-lint will block your commit until you write the footer, but then it's done—the pile just sits there, and I never built the thing that adds it up. anchildress1 / rai-lint Dual-language linter for Responsible AI commit footers — shared logic for Node (commitlint) and Python (gitlint). Stop playing hide-and-seek with AI in your commits. A dual-language validation framework that makes AI attribution non-negotiable. 📊 Project Stats 🗣️ Languages 📦 Packages 🤖 AI & Automation 🔧 Quality & Standards Installation • Quick Start • Required Commit Footers • Documentation What is this? 🤖 RAI Lint enforces Responsible AI (RAI) attribution in every commit. No more "who wrote this?" moments. No more mystery code. Just honest, trackable AI contributions. Read the full story: Did AI Erase Attribution? Your Git History Is Missing a Co-Author %%{init: {'theme':'dark'}}%% flowchart LR A[Developer Commits] --> B{Has AI Footer?} B -->|Yes| C[Commit Accepted ✅] B -->|No| D[Commit Rejected ❌] C --> E[Clear AI Attribution] D --> F[Add Footer & Retry] Loading Why does this exist? Because transpa

2026-07-28 原文 →
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Turning surprise AI bills into accountable growth

This is a story of how you receive a higher bill than expected and how GitHub's Billing Controls help clarity and predictability. Let's begin our story. Confusion: The bill is higher than expected The AI bill is higher than expected. Now what? Finance wants to understand what is driving the cost. Engineering leaders, on the other hand, want to preserve the productivity gains behind the increased usage. The administrator needs to balance both priorities and put a policy in place that the business can understand. One question drives the investigation: Where is the increase coming from, and how do we control it without disrupting valuable work? To answer it, we first need to follow the spend. Once we know who owns it, we can apply guardrails at the right level. Investigation: Follow the spend Setting a limit too early could restrict useful adoption without addressing the main source of cost. So, let's find out what changed and who can act on it. The investigation begins in the billing administration portal, where usage and budget settings appear in one place. Fig 01: A unified billing workspace connects usage evidence to budget controls. Identify the consumption category First, we identify which product changed. GitHub reports this by SKU , which simply means the billing category for a product or service. Fig 02: Grouping metered usage by billing category highlights Copilot Enterprise usage. Grouping usage by billing category shows which product is driving the increase. In this example, the chart points to Copilot Enterprise usage. Leaders can then ask whether that growth comes from valuable adoption, an unusual workload, or demand that has outgrown its budget. Locate the accountable organization Once you know which product is driving the cost, find out who owns the usage. An enterprise-wide total can hide a sharp increase in one organization. Fig 03: Organization-level grouping identifies the business unit accountable for demand. The organization view points to the le

2026-07-28 原文 →
AI 资讯

The harness is all you need (mostly)

A practical GitHub Copilot workflow for prototyping, planning, implementing, and reviewing software without chasing every new AI tool. The post The harness is all you need (mostly) appeared first on The GitHub Blog .

2026-07-28 原文 →
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