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Indie Hackers

AI Document Analyst

To help people to solve the same problem I had when working with documents and improve the way we do

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Actual performance

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released
42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
37%37% 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
27%27% 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, revenue
14%14% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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