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Web0.cc – Generate clutter, ad and tracker free article pages to share
Hacker NewsWeb0.cc – Generate clutter, ad and tracker free article pages to share
I recently observed that majority of my family members and friends are not using ad blockers and reader mode for reasons suck as lack of knowledge about plugins & laziness. So their online reading experience is not pleasant as a result very less amount of them read anything including what i share. Its an attempt to give them clutter, tracker & ad free reading experience right off the bat.
Share cardActual performance
136points
61comments
Made the leaderboard
Launch Intel predictions
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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting51%51% 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 NewsMay not resonate with HN audience · Strong signals: including · Missing: https docs, excited, just released43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue14%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, crypto10%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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