diip

diip

Indie Hackers

Increase your social profiles with a tip catalogues

Sometimes we run out of things to watch or read, or maybe we are not sure of which pair of shoes to buy...Never run out of recommendations by real people that share the same interests as you do!

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Actual performance

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
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
45%45% predicted probability of success on Product Hunt, 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
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real people · Missing: web3, chat, crypto
25%25% predicted probability of success on BetaList, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
14%14% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.

Correct prediction on native model

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