I

I made an app that checks your brand name for vulgarity in other langs

Hacker News

I made an app that checks your brand name for vulgarity in other langs

Hey HN! I am Roland and I made an app that checks whether your brand or product name is safe to use around the world. Imagine you are founding your startup or launching your first product to sell and you choose the word "Pepeka" as your brand name! Pe-pe-ka! Pepeka! Pepeka! Sounds nice, right? I think so, too! It has a nice flow, looks great and is short. Now imagine after your launch you find out that "pepeka" means actually "female genitalia" as a slang in Brazilian Portuguese! Gente!!! Puta que me pariu! What a fail to use it as a brand name. To avoid this, you can use now https://safename.rowu.me . We check in all languages if there is any bad connotation attached to the word. Be it sexual, vulgar or just strange to use for business purposes. Check it out and let me know! You can try for free! Roland

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
28%28% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
14%14% predicted probability of success on AppSumo, 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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