Mood dots

Mood dots

Indie Hackers

Text how you feel without saying a word...

I have no coding or tech background. Just an idea that solves a real communication problem: tone is constantly misunderstood over text, so I created MoodDots.

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Actual performance

1followers
Did not reach leaderboard

Launch Intel predictions

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Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting
40%40% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
31%31% 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
24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: coding · Missing: mac, agents, macos
18%18% 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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Word2Bits – Quantized Word Vectors44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

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Probabilistic Spellchecker – You may have misspelled a word33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

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EtymologyExplorer – word origin trees52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

EtymologyExplorer – word origin trees

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Where I get the last word33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Where I get the last word

Hacker News8