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Get a report on your compliance to CIS Benchmarks (Azure and AWS)
Hacker NewsGet a report on your compliance to CIS Benchmarks (Azure and AWS)
Easy to run script that allow you to have a report on your compliance to CIS Benchmarks rules. Get the YAML rules from the repository, run the script, and get your results by SMS, Email, Teams, or simply by reading the logs. Currently available for AWS & Azure CIS Benchmarks, else you can check the rules folder in the repository to find rules you want to apply for the next report !
Share cardActual performance
7points
Made the leaderboard
Launch Intel predictions
Analyze your own launch →
Product HuntUnlikely to reach the leaderboard · Strong signals: email · Missing: mac, agents, macos48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal43%43% predicted probability of success on TrustMRR, 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 · Missing: https docs, excited, just released34%34% 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 · Missing: arr, mrr, revenue23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive21%21% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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