XposedOrNot – Open-Source Data Breach Monitoring Solution

Hacker News

Hello HN! I'm excited to share XposedOrNot, an open-source initiative I've developed focused on improving how we monitor and analyze data breaches. It's more than just a tool; it's a platform designed to bring clarity and actionability to information security. Highlights of XposedOrNot: Fully Open Source & Free API Access: Built on the principles of openness and collaboration. Interactive API Playground & No Paywalls: Accessibility for all, encouraging experimentation. CXO Dashboard & Email Risk Scoring: Making complex data understandable and actionable for executives. In-depth Data Breach Insights: From analytics to visualizations, get the full picture of any breach. Community-Driven Development: With an open roadmap and a hall of fame to recognize your contributions. We're continuously evolving, with plans to introduce features like multi-channel alerts and further dashboard enhancements. And yes, we have a dark mode! Check it out and let me know your thoughts: I'm here to discuss, answer questions, and absorb all your feedback. Together, let's push the boundaries of open-source security solutions.

Visit product View on Hacker NewsFeb 14, 2024

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

89%89% 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

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, open source, io · Missing: https docs, just released, exist

60%60% 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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

9%9% 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

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

Incorrect prediction on native model

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