No
Non Agentic international calling app with local rates
Hacker NewsNon Agentic international calling app with local rates
I built something, it's nice, it's an international calling app, it has faces behind it, no shady rates, no shady spams, built with love, really. It works on iOS/Android and Web. And the best thing about it? It's cheap, if you want to give it a whirl, and let me know what you think. p.s: Non-agentic means no AI has been shipped into it at all.
Share cardActual performance
1points
Did not reach leaderboard
Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, agentic · Missing: mac, agents, macos80%80% 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 HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released38%38% 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, revenue23%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, crypto2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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