Tw
TwLng.com – Tweet with more than 140 characters
Hacker NewsTwLng.com – Tweet with more than 140 characters
TwLng.com is a side project that lets users tweet with more than 140 characters. I wanted to experiment with Google's PHP AppEngine runtime and am using the awesome PHP-GDS [1] library as an API to the Google DataStore. Feedback and ideas for how to take this further would be appreciated :) [1] https://github.com/tomwalder/php-gds
Share cardActual performance
3points
4comments
Did not reach leaderboard
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TrustMRRFits verified-revenue profile · Strong signals: google, users · Missing: mobile apps, ios, personal59%59% predicted probability of success on TrustMRR, 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, podcasting47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: google, user, using · Missing: mac, agents, macos45%45% 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 · Missing: https docs, excited, just released43%43% 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, revenue14%14% 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, crypto12%12% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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