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Watchdog – Buisness Credential monitoring and guided remediations

Hacker News

Watchdog – Buisness Credential monitoring and guided remediations

Hey everyone! My name is Leonard Melnik, I am the CEO/and developer of ThreatRecord and our product Watchdog. To make it simple, Watchdog notifies businesses of compromised credentials and guides employees to resolve the issue and prevent future incidents. Since stolen credentials are the most frequent initial attack vector for businesses and humans are the weakest part of any security, we decided to make a solution that integrates both. We are looking for initial customers to work with to further build out our product and providing the best possible solution for a low cost. If you have any questions/ideas/suggestions or are interested in our service please reach out to me: leonard at threatrecord.io

Visit product View on Hacker NewsJun 7, 2023

Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

51%51% 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 · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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