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I made a stupid/fun website, now what?
Hacker NewsI made a stupid/fun website, now what?
http://www.infinitepoppedmolly.com/ Based on the line "I pop a molly, I'm sweating" by Trinidad James - this song's currently pretty popular among college and high school kids, and that line is the catchphrase. So last night, I decided to see if I could make an infinite looping site in a couple of hours (turned out to be pretty easy with video.js), and this was the result. Now that I have this built, any ideas about what to do? Obviously this is just a fun/silly idea but it'd be cool to spread it around and see it getting some publicity!
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting81%81% 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 NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos30%30% 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, revenue16%16% 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, crypto1%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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