Domain Public Data Collection Service Got Updated

Hacker News

Hello everyone! Some days ago I've published a post here about DPULSE which is domain OSINT tool. Аor those who haven't seen it I'll give Github link ( https://github.com/OSINT-TECHNOLOGIES/dpulse ). In general, this tool allows you to gather a lot of open-source information about domain such as WHOIS information, subdomains, mentions of the domain's owner organization in some social networks (+ organization profiles in social networks), IP addresses, public documents and many more using custom Google Dorking queries, InternetDB search results (possible vulnerabilities, open ports and so on), used web-technologies, sitemap, robots.txt files, SSL certificate info. And in v1.0.2 update I've added possibility to form results as XLSX file additionally to PDF file. Also I've added roadmap on GitHub page (projects tab), so everyone interested is able to see how development process is going on. Will be glad to see everyone in my repository and, moreover, to hear your opinion and tips!

Visit product View on Hacker NewsJun 14, 2024

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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: google, using, open · Missing: mac, agents, macos

62%62% 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

47%47% 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

43%43% 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: google · Missing: mobile apps, ios, personal

43%43% 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

11%11% 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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