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Cerast Intelligence – A Bug Bounty Target Identification Project

Hacker News

Cerast Intelligence – A Bug Bounty Target Identification Project

Hi HN, I'm happy to share the latest version of my project, Bug Bounty Intelligence Stream. Since my initial post, I've made several updates based on the feedback I received. What's New: - Pricing: Introduced a free tier, no coupon needed. Overall pricing has been reduced to be more accessible. - Monitoring Page: Overhauled with new features like category filters, regex searches, and the ability to save, export, and share results. - Infrastructure: Now fully redundant for higher reliability. - New Detections: Added numerous new detections based on user suggestions. How It Works: Subscribe to an event stream that delivers pre-verified targets for potential bug bounties and security research. Findings are yours to report and claim. Useful for both bounty hunters and academic researchers. Think of it like a sushi belt—opportunities roll by, and you grab what interests you. Example Detections: - Exposed .git, ssh-keys, docker-compose, .env, config Files - Extremely outdated PHP, WordPress, Apache, FTP - Public phpinfo() - Leaked database backups Upcoming Features: - Access to historical data - Automatic extraction of contacts from security.txt files - More complex detection capabilities You can use the API Key: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VybmFtZSI6ImhuQGV4YW1wbGUuY29tIiwiZXhwIjoxNzE4NjEzMjg2LCJwcmVtaXVtIjp0cnVlfQ.h5tPCTQExJeFXfxVo2jGeEIgtXsf2TdSHtmVHnneiz0 The key is valid until Mon Jun 17 2024 URL: https://cerast-intelligence.com/ Twitter: https://x.com/CerastIntel Looking forward to your feedback!

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Actual performance

1points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
85%85% 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: user, dock, new · Missing: mac, agents, macos
78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
40%40% 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
29%29% predicted probability of success on TrustMRR, 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
28%28% 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
13%13% 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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