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Report malicious domains easily without manual whois lookups

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Report malicious domains easily without manual whois lookups

I get at least one phishing text message every week, and discovered that reporting malicious domains was a major hassle. You have to first manually do a whois lookup, and since not every registrar includes an abuse contact email in their whois record, you often have to then go to the registrar's website and try to find their abuse reporting section before you can report the domain. I figured something had to exist already for reporting domains directly to their registrars, but I couldn't find anything, so I spun up DomainReporter mostly as a tool to use myself, but also to make the process simpler for everyone else, as well.

Visit productView on Hacker NewsOct 17, 2024
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Hacker NewsStrong engagement from HN community · Strong signals: exist, io · Missing: https docs, excited, just released
50%50% 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 · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: email · Missing: mac, agents, macos
48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
40%40% 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, revenue
14%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, crypto
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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