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Introducing Vulert, the Revolutionary SCA Tool

Hacker News

Introducing Vulert, the Revolutionary SCA Tool

Hey there, fellow hackers! We're thrilled to introduce you to Vulert, an advanced SCA tool that makes monitoring and identifying open-source vulnerabilities in your software a breeze. With Vulert, you don't need to give us access to your code or go through a complicated installation process. Simply provide a manifest file (e.g. package-lock.json), and we'll do the rest for you in real-time! Want to give Vulert a try without signing up? Check out our demo at https://vulert.com/abom! We're passionate about creating a tool that truly helps you, and we'd love to hear your thoughts and ideas. Your feedback is invaluable to us, and it'll help us improve Vulert and make it even more effective for you. Thank you for your support, and we can't wait to see how Vulert can help you take your software security to the next level! Best Regards, The Vulert Team

Visit productView on Hacker NewsMar 15, 2023
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
78%78% 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
56%56% 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: lua, ide, io · 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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