# ZecHub DAO — Proposal A149: Phase 1 Progress Report **URL:** \ **Category:** Community Collaborations\ **Created:** [June 5, 2026, 2:30pm UTC](https://forum.zcashcommunity.com/t/zechub-dao-proposal-a149-phase-1-progress-report/56030 "2026-06-05T14:30:19Z")\ **Posts on this page:** 1\ **Showing post:** 2
**Author:** ![bloxster](https://sea2.discourse-cdn.com/zcash/user_avatar/forum.zcashcommunity.com/bloxster/32/20512_2.png) [@bloxster](https://forum.zcashcommunity.com/u/bloxster)\ **Post date:** [July 6, 2026, 11:12am UTC](https://forum.zcashcommunity.com/t/zechub-dao-proposal-a149-phase-1-progress-report/56030/2 "2026-07-06T11:12:06Z")
# A149 progress update — from the Italian pilot to a fully-private, multilingual rollout ## Summary The Phase 1 framework is proven, so the work since then has been about **scale** and **staying true to the privacy goal end-to-end**. The pilot has grown from one language to a multilingual rollout on a shared ~180-page curated set. For the translation job I used both ChatGPT 5.4 and a local model. This is a progress report and the plan ahead. ## Language coverage | Locale | Status | | --- | --- | | English, Italian | ✅ Baseline (Phase 1) | | French, Spanish, German, Portuguese, Arabic, Chinese | ✅ Full curated set translated + UI dictionaries + wiring | | Hindi, Russian, Japanese, Korean, Turkish, Ukrainian | ✅ Full curated set translated + UI dictionaries + wiring | | Swahili, Yoruba, Igbo, Twi, Ewe | 🔄 Underway now (specialized local model — see below) | That’s **all 12 curated languages** beyond the English/Italian baseline drafted end-to-end — the full ~180-page set, UI dictionaries, and frontend wiring — now consolidated and staged for review. The five African languages are actively being translated as I write this. Same principles as the pilot throughout: repository-controlled content, English fallback, protected-terms CI, no runtime Google Translate. ## What’s new **A private translation pipeline.** Rather than one cloud API, each language is routed to the best-fit engine, chosen partly for privacy: - ChatGPT 5.4 for high-resource languages, - a **local open model running on my own GPU** for others, - a **specialized open translation model (NLLB-200), also fully local**, for low-resource languages. **Quality measured against the live baseline.** Google Translate is what readers get today, so it’s my yardstick. Every language is scored with an automatic round-trip check against GT before shipping. Result so far: high/medium-resource languages are **on par with or better than Google Translate** ; nothing ships that regresses below the GT baseline users already have. **Terminology at scale.** The protected-terms glossary + CI now enforces the Zcash term list across every language, not just Italian — so “Arborist Calls” stays “Arborist Calls” everywhere. **A broader privacy audit.** The original scope was Google Translate; I went further and audited the whole site for other third-party touchpoints (fonts, embeds, CDNs, map tiles, the AI assistant, analytics). Findings were cross-checked by an independent second model. Removing Google Translate is by far the biggest win; the rest are queued as small, self-contained follow-ups. ## African languages — the honest part General-purpose LLMs are strong on major languages but **unreliable on low-resource ones** , in testing they produced confident-looking output that mistranslated core meaning. So these five use a **specialized open translation model built for exactly these languages** (Meta’s NLLB-200), run entirely on my own GPU. This track is **now in progress** : the pipeline is markdown- and terminology-aware — code, links and protected terms are shielded during translation, and any line where a link or term can’t be safely preserved falls back to English rather than risk a broken page. Each language is held to a simple bar: **ship only where quality is at least as good as the Google Translate already live** (a pure privacy win, no regression), measured with the same automatic round-trip check as every other language. Native speakers can then refine pages over time — improvement is welcome, not a blocker. ## Status against the plan | Item | Status | | --- | --- | | Framework, glossary, contributor docs, Italian pilot | ✅ Delivered (Phase 1) | | Multi-language expansion (12 curated languages) | ✅ Drafted + staged for review | | Private, local translation pipeline | ✅ Built | | Automatic quality benchmark vs Google Translate | ✅ Built | | Low-resource (African) languages | 🔄 In progress (specialized local model) | | Full-site privacy hardening (beyond GT) | ⏭ Follow-ups | | Community refinement | ⏭ Ongoing | ## Next steps 1. Settle the merge structure for the batch and open the 12 curated languages for community/editorial review; ship each as it clears the quality bar. 2. Finish the African languages on the specialized-model path, run each through the no-regression benchmark, and ship the ones that pass — with community refinement to follow. 3. Land the deferred privacy follow-ups as small PRs. I am also considering implementing a system that will update translated pages semi-automatically when an English update is released. However, it is important to remember that **machine drafts still need people.** Automatic scores tell me a translation didn’t _regress_; they don’t replace a native read. Static translation remains an ongoing community effort, not a one-time automation. Also, **round-trip metrics are a screen, not a verdict.** They’re biased toward the tool used to measure them, so “within a few points of Google Translate” should be read as “effectively on par,” and native corrections are always welcome. Your opinion and comments welcome! --- _[View the full topic](https://forum.zcashcommunity.com/t/zechub-dao-proposal-a149-phase-1-progress-report/56030)._