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Bug Bounty on a Sushi Belt – Automated Target Identification

Hacker News

Bug Bounty on a Sushi Belt – Automated Target Identification

Hi HN, I'm excited to share a project of mine called Bug Bounty Intelligence Stream. While automating my own bug bounty hunting, I realized the system detected more vulnerabilities than I could handle alone. Hence, I built this product. The concept is simple: users can subscribe to an event stream that delivers pre-verified targets for potential bug bounty and security research. I take no credit for any findings and do not report them myself, so any potential bounty earnings are entirely yours. This tool might also interest academics studying the state of web security. I talked to a lawyer in my home country and from a legal standpoint I should be in no trouble. You can imagine the product like a sushi belt. You just sit there and watch as the tasty stuff comes your way and you grab whatever you want. This is my first ever-product and I am hungry for feedback from you. Please use the coupon code HN100 to test for free for one month in the “per month” subscription. Some example detections include: Exposed .git, ssh-keys, docker-compose, .env, config Files Extremely outdated PHP, WordPress, Apache, FTP Public phpinfo() Leaked database backups On my to-do list are features like: Access to historical data Automatic extraction of contacts from security.txt files More complex detection capabilities Looking forward to your feedback! URL: https://cerast-intelligence.com/

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
88%88% 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, code · Missing: mac, agents, macos
77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist
45%45% 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 · Strong signals: month, users, way · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
12%12% 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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