Cyberintel.info Cybersecurity News Aggregation Using LLM and NER

Hacker News

I built CyberIntel.info a lightweight cybersecurity news aggregator that pulls articles from credible sources and organizes content based on different personas: Public Practitioners Researchers It leverages AI techniques like Named Entity Recognition (NER) and intelligent tagging to categorize news efficiently, making it easier to find relevant updates whether you're a casual reader, security practitioner, or researcher. It also offers a bookmark functionality to save articles for later read I built this because I found it frustrating to track cybersecurity news across multiple sources. Would love to hear your thoughts! What features would make this more useful for you?

Visit product View on Hacker NewsFeb 22, 2025

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Actual performance

2points

Did not reach leaderboard

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

84%84% 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: new, using · Missing: mac, agents, macos

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: efficient · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

41%41% 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

28%28% 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

14%14% 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.

Correct prediction on native model

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