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I build a new website vulnerability scanner

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I build a new website vulnerability scanner

i have build a new website vulnerability scanner that aims to search and find security related problems and vulnerabilities from diffirent severity, this scanner can detect a wide range of vulnerabilities including owasp top 10 and others using both active and passive scanning. in the active scanning, the scanner interact directly to the live website endpoints by sending multiple http requests with special payloads, in passive scanning, the scanner fetch data from various source like the Wayback Machine. the scanner also scan the subdomains to discover hidden or forgotten endpoint which may be outdated and vulnerable. The scanner will be launched soon, if you are interested in getting early access, leave your contact information at: https://forms.gle/dqvC2G5UVK1xopo18 or contact me at: aimedguendouz@gmail.com

Visit productView on Hacker NewsOct 16, 2024
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Actual performance

2points
1comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
71%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.
TrustMRRFits verified-revenue profile · Strong signals: way, scanner · Missing: mobile apps, ios, personal
59%59% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: soon · Missing: plus, platform, intuitive
55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, new, using · Missing: agents, macos, agent
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io, including · Missing: https docs, excited, just released
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
15%15% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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