I
I built a free backlink checker – ReviewMySiteNow.com
Hacker NewsI built a free backlink checker – ReviewMySiteNow.com
Hi HN, I built https://reviewmysitenow.com as a side project to learn more about SEO and web tools. It’s a 100% free backlink checker that lets anyone check their website’s backlink profile — with no signup and no limits. I was frustrated with tools like Ahrefs and SEMrush having high paywalls just to check basic backlinks, so I built a free alternative. Would love to get your thoughts, feedback, or suggestions for making it more useful! Thanks!
Share cardActual performance
2points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting71%71% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released42%42% 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 · Missing: mobile apps, ios, personal27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos17%17% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue16%16% 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, crypto0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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