I built a pSEO site for 182 bilingual books in African languages

Hacker News

Hey HN, I'm a solopreneur from Burundi, now in France. I saw how fast kids in the diaspora lose their mother tongues. So, I self-published 182 bilingual children's books in 26 African languages (Kirundi, Yoruba, Swahili, etc.) on Amazon. To share them, I built Lingu.Africa, a Next.js site with programmatic SEO. It auto-generates pages for each book-language combo. I also built a content system that posts a new blog daily, storing content as MDX files in the repo. Tech: Next.js (SSG), TypeScript, TailwindCSS, MDX blog. Built with Cursor IDE and Claude 3.5. Would love feedback on architecture and SEO approach. Lionel

Visit product View on Hacker NewsMar 20, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, claude, new · Missing: mac, agents, macos

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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