Ti

Titan Breach – AI-driven cybersecurity platform

Hacker News

Titan Breach – AI-driven cybersecurity platform

Hi HN, I built Titan Breach ( https://platform.titanbreach.com ) to solve the pain of juggling multiple tools for cybersecurity intelligence. It’s an AI-powered toolkit that unifies URL phishing detection, IP infrastructure mapping, typo-domain/DNS monitoring, ransomware insights, and website availability checks. Why I built this: As a security researcher, I was frustrated by switching between disjointed tools to track emerging threats. I wanted a single platform that delivers fast, cohesive intel using AI. What it does: - Detects phishing via URL scoring and social-engineering pattern analysis. - Maps infrastructure with port scans, fingerprinting, and visualizations. - Tracks typo-squatted domains and suspicious DNS registrations. - Monitors DNS for anomalies and takeover risks. - Aggregates ransomware intel from public leaks and threat group trends. - Verifies website availability with multi-technique checks. Tech stack: Built with Next.js for server-side rendering, Python for backend logic, Elasticsearch for indexing, MongoDB for storage, and LLMs for analyzing patterns (e.g., phishing intent from URL structures). Caveats: - It’s an MVP—some features are limited, but the free tier requires no signup. - Stable for now, but heavy traffic might stress it. I’m ready to scale if needed. I’d love help with: - UX: Is the dashboard intuitive? Any clunky workflows? - Features: What needs more polish—phishing detection, visualizations, or something else? - Bugs: Notice any glitches or inconsistencies? Just let me know. Thanks for checking it out. Happy to answer questions or hear what you'd like to see next!

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Actual performance

3points
Did not reach leaderboard

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best fitHighest predicted score across all platforms for this description.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, intuitive · Missing: plus, reviews, host
36%36% 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
28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: visual, single, using · Missing: mac, agents, macos
28%28% 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: ide, io · Missing: https docs, excited, just released
24%24% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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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