Parallel Arabic – Egyptian Arabic reading and writing practice

Hacker News

I have made a website called Parallel Arabic which enables users to read Egyptian Arabic texts alongside the same text in English. Each story features a dictionary of key words, transliteration of the text, and full native audio recordings, creating a fully self-contained learning environment without the need for outside resources. https://parallel-arabic.com/stories There is also a section for writing where you can practice writing over 4000 words, with realtime spellchecking. The tool contains a fully featured Arabic virtual keyboard, built for english speakers. There is also a section to learn and practice the arabic alphabet

Visit product View on Hacker NewsApr 24, 2024

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Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released

70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: users, para · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, recordings · Missing: mac, agents, macos

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

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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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