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GrabbrApp – Securely Grab Certs and Malware from Sketchy Infrastructure

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GrabbrApp – Securely Grab Certs and Malware from Sketchy Infrastructure

I've been in the information security industry for quite some time now and at every company I worked at, I always ran into the problem of grabbing malware samples, certificate information and port scan information from malicious or sketchy infrastructure. There were hacked together solutions, but they were bad, and they didn't scale. So, I built GrabbrApp, a SaaS platform that uses short-lived VPS infrastructure to grab malware and certificates before detonating, leaving no trace of your research. GrabbrApp has been live for several months now with over 200 users and a ton of requests. I'd love to get HN's feedback, though if it's about the UI, I know, it's not pretty ;)

Visit productView on Hacker NewsSep 21, 2023
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
75%75% 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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: user · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, users, way · Missing: mobile apps, ios, personal
32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
16%16% 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.

Incorrect prediction on native model

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