Device security tool built on Osquery

Hacker News

If you’ve ever wondered -- “How can I make sure every device, managed or not, is secure before granting access?” -- “Am I at risk for this CVE or zero day exploit?” -- “How do I know that the controls I configured via MDM are actually enforced?” Device360 gives you the ability to answer those for yourself. Built on Osquery, the product lets you run real-time or scheduled queries to assess security posture, get a report of vulnerabilities, and test access policies. You can even deploy the authenticator on unmanaged devices. Open to feedback!

Visit product View on Hacker NewsFeb 21, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos

71%71% 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

61%61% 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 released

45%45% 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 · Missing: mobile apps, ios, personal

34%34% 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

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% 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.

Incorrect prediction on native model

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