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A classy way to ask for donations on projects

Hacker News

A classy way to ask for donations on projects

The tip jar line. Added to here: https://github.com/fulldecent/FDWaveformView https://github.com/fulldecent/system-bus-radio https://github.com/fulldecent/FDTake https://github.com/fulldecent/FDChessboardView https://github.com/fulldecent/FDSoundActivatedRecorder And more emoji: https://gist.github.com/rxaviers/7360908

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Actual performance

2points
1comments
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
55%55% 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 · Strong signals: way · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
23%23% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
22%22% 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
13%13% 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
10%10% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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