Sa
SaaS Vulnerability Scanner for network perimeter
Hacker NewsSaaS Vulnerability Scanner for network perimeter
Dear community members, Would appreciate if you could have a look and test our SaaS Vulnerability Scanner for network perimeter from a Czech based startup Cybersecurity Help s.r.o. We offer a free-trial, do not ask for credit card details and would be grateful for all suggestions and your opinion. Website: https://www.cybersecurity-help.cz/ Thank you!
Share cardActual performance
2points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
TrustMRRLess likely to generate early MRR · Strong signals: scanner · Missing: mobile apps, ios, personal42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting39%39% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto23%23% predicted probability of success on BetaList, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos21%21% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Correct prediction on native model
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