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SecureIT – Easy to setup cybersecurity service for free

Hacker News

SecureIT – Easy to setup cybersecurity service for free

I have built this service to allow every project to have basic cybersecurity, in an easy to setup way. It will basically alert you when your service/server is running on vulnerable software. The website is: https://secureit.io You can even try it anonymously at: https://beta.secureit.io/tryit And you can also join in on the discussion and show us some love on ProductHunt as we launched there today https://www.producthunt.com/posts/secureit Thanks for caring, I did this mainly to help out starting businesses/projects - and it will always stay free for those without revenue, as I can afford to do so. All feedback is welcome! Cheers!

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Actual performance

12points
4comments
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
67%67% 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.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
47%47% 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: way · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: revenue · Missing: arr, mrr, profit
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
7%7% predicted probability of success on Product Hunt, 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.

Incorrect prediction on native model

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