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A Platform where you can import all your reviews from various platform
Hacker NewsA Platform where you can import all your reviews from various platform
Import, Secure and Showcase all your reviews on one single platform. With Feedspace you can import your reviews from Google, Play Store, Amazon, Facebook, Product Hunt, Triadvisor, Trutpilot etc. You can create a wall of love with these reviews. Showcase them on your website/ Social media to build trust and credibility. Businesses have increased their revenue by 3X with this import feature of feedspace.
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Did not reach leaderboard
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting76%76% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: google, single · Missing: mac, agents, macos69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, reviews · Missing: plus, intuitive, host44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released30%30% 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: google · Missing: mobile apps, ios, personal29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: revenue · Missing: arr, mrr, profit21%21% 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, crypto11%11% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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VARIETY, VARIETY, VARIET Do not always do the same trainin
Indie Hackers