Li
Livestream content on-demand
Hacker NewsLivestream content on-demand
In light of all the Meerkat press, I've made a new product(beta) and would love your feedback. Goal: to give the people live entertainment when they want it. Basically you can tip strangers to do random things - so its kinda like chatroulette with tipping. URL: http://cashcam.tv Node.js/WebRTC combination with french fries on the side (disclaimer: this site is only 5 days old and only works if other people are on it). Don't hold back. Thanks in advance! peter
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Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos62%62% 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 · Missing: supports, reddit linkedin, podcasting55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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