Releasing Vulnerabilities of Open Source Software

Hacker News

Thrilled to announce that very soon The List Of Vulnerabilities collected from multiple platforms including ( npm, Maven, Go, NuGet, PyPI, RubyGems, crates.io, Packagist, Linux, OSS-Fuzz ) will be live at https://vulert.com/vuln-list. In short we are making sure that no vulnerability is left unreported and your software/service is always protected from unexpected attacks. Who doesn’t know Vulert Yet: Vulert, without any integration, notifies you if a Security Issue is found in any of the open-source software you are relying upon. HOW DOES VULERT PROTECT YOU ? PRIVACY: You don't need to share your codebase or integrate anything, all you need to do is to upload the list of open-source software you use. IMPROVED SECURITY: Existing solutions check the Current Security of your software. Unlike them we track your application not only for current but also for Future Security Issues. ECONOMICAL: You don’t need security staff just to keep track of your dependencies, Vulert can do it efficiently and it's very economical. HOW DOES VULERT WORK ? We keep an eye on open source code, our security team analyzes changes in the open source software. On an hourly basis, we gather the security advisories of the Vendors / Security Researchers. In case we find any security advisory for a software used by our customers, We notify them. Feel free to give feedback or ask any query, to contact info@vulert.com

Visit product View on Hacker NewsMay 20, 2022

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

74%74% 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.

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

70%70% 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: exist, open source, existing · Missing: https docs, excited, just released

50%50% 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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, soon, efficient · Missing: plus, intuitive, reviews

38%38% 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

10%10% 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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