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Online SMS (Weekend Project - Week 2)

Hacker News

Online SMS (Weekend Project - Week 2)

Having launched Online SMS last week after 8 hours of work, I enhanced the product some more this week and released an update today. Please send me your feedback/advice. Here are the links Online SMS Android App https://play.google.com/store/apps/details?id=com.fauzism.onlinesms Online SMS Website http://sms.fauzism.com App Code on Github https://github.com/spicavigo/onlinesms_android Server Code on Github https://github.com/spicavigo/onlinesms_server First blog post http://fauzism.com/post/37587975976/once-upon-a-saturday Second blog post http://fauzism.com/post/38055171351/life-of-fy I would really appreciate your feedback/advice here. Thanks

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
58%58% 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.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released
45%45% 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
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, google · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: google, apps, code · Missing: mac, agents, macos
21%21% 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
18%18% 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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