A

A kind of looking glass for web abuse

Hacker News

A kind of looking glass for web abuse

It kind of occurs to me that maybe this would be generally useful to people with exposed web servers, so I've made it generally available at least for the moment. You should be able to hit it as often as once per hour without getting banned. It would be better if other people offered the same looking glass to give a better global picture. I will be happy to get on a video call and help with the installs and setup, it's all on GitHub: https://github.com/m3047 I'd be perfectly happy with somebody putting a web interface in front of it, just reach out and let me know you're going to do it so that I can whitelist you and have contact info for advance notice of anything important. [Edit:] I should note that that server is not available from some cloud services for $reasons, so you really do need to reach out to me if you plan on hosting your service in the cloud. # dig @athena.m3047.net 'web_page\;\*.keys.redis.athena.m3047' txt +short | perl -ne 'm/;([^;,]+),40.;athena/ && print "$1\n";' | sort | uniq ab2g ab2h alive.php apple-touch-icon.png apple-touch-icon-precomposed.png apply.cgi aws-secret.yaml check.js chitoge.php?chitoge config config.php connect.cgi .env env.backup .env.local .env.production .env.staging eval-stdin.php example.com/ faspex/ favicon.svg formlogin hetong.js index.cgi index.html index.php?file=tf2rghf.jpg index.php?lang=en info.php input license.txt login login.asp login.esp login?lang=en logon.html phpinfo _phpinfo.php phpinfo.php powershell reportserver routes sh shell.php teorema505?t=1 web/ wp404.php wp-config wp-json wp-login.php x.js

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
79%79% 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 HuntUnlikely to reach the leaderboard · Strong signals: apple · Missing: mac, agents, macos
44%44% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, interface · Missing: plus, platform, intuitive
44%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: ide, io · Missing: https docs, excited, just released
31%31% 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: video · Missing: mobile apps, ios, personal
30%30% 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
20%20% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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