FeedGuardians
Indie HackersAI that cleans your comment section and recovers lost sales
I built FeedGuardians after seeing too many ad campaigns get wrecked by spam, trolls, and unanswered buyer questions.
Share cardActual performance
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting38%38% 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: io · Missing: https docs, excited, just released37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto30%30% 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, revenue25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Correct prediction on native model
Similar products
Op
OpIn – Comment Anywhere33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
OpIn – Comment Anywhere
Hacker News2Lo
Lost Ethereum40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Lost Ethereum
Hacker News18TL
TL;DR for every comment on HN44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
TL;DR for every comment on HN
Hacker News1LO
LOST/LONDON52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
LOST/LONDON
Hacker News1Lo
Lost Trail – App for Hiking38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Lost Trail – App for Hiking
Hacker News1Voize24%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Comment the Web
Indie Hackers1communicationSe
Search HN for interesting comment sections59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Search HN for interesting comment sections
Hacker News60Co
Comment on Any Website61%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Comment on Any Website
Hacker News1Ne
New Comment Marker33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
New Comment Marker
Hacker News2I Ain't Lost44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
A game with mazes
Indie Hackers1games