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Domain Locker – Track and manage all your domain names
Hacker NewsDomain Locker – Track and manage all your domain names
I built Domain Locker to keep track of all my domain names in one place. It shows registrar, expiry, DNS, SSL, subdomains, and more - plus alerts you before something important changes or expires. It's free and open source, can either be used at domain-locker.com, or self-hosted with Docker. The source is on GitHub at https://github.com/lissy93/domain-locker Would love to hear your thoughts, feedback, or ideas!
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, podcasting59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide · Missing: https docs, excited, just released54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, host · Missing: platform, intuitive, reviews49%49% 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, personal48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: dock, open · Missing: mac, agents, macos45%45% 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, revenue12%12% 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, crypto4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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