CyberAlerts.io - A free real-time vulnerability alerting tool

Hacker News

Hi! I'm Ryan, I previously created Damn Vulnerable Web App (DVWA) and WPScan (acquired by Automattic). I recently built CyberAlerts to help security teams track vulnerabilities and security threats in real-time. Most existing solutions are expensive, or not flexible enough in filtering out the noise. CyberAlerts lets you: - Filter vulnerabilities by exact match keywords or Regular Expressions (regex) - Get instant alerts when a source posts a new vulnerability, threat, or news article - Stay ahead of emerging threats We enrich each "article" (vulnerability, news article, etc.) with severity, known exploited, and other data points. Alerts can either be instant email, daily email, RSS, API, custom dashboard, or Slack. We have a free tier, and I’d love feedback! Would this be useful in your security workflow? Anything I could improve upon? Try it here: https://cyberalerts.io Thank you, Ryan

Visit product View on Hacker NewsFeb 21, 2025

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

75%75% 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: slack, new, email · Missing: mac, agents, macos

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, io · Missing: https docs, excited, just released

38%38% 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

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

13%13% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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