Hack Dojo - Search engine for cybersecurity research with AI summary

Hacker News

Excited to present Hack Dojo, a unique search engine with over 3,000 research presentations across cybersecurity, DevOps, and AI. Our mission: to make it easier for tech professionals and enthusiasts to stay updated and ahead of the curve. We've integrated an AI-powered TL;DR feature that quickly summarizes key points from presentations and news. We invite you to explore Hack Dojo and share your thoughts. Your feedback will help shape our platform. Thanks for your support! (We also launched on Product Hunt today! https://www.producthunt.com/posts/hack-dojo )

Visit product View on Hacker NewsJun 6, 2023

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Actual performance

3points

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Did not reach leaderboard

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

71%71% 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: new, presentations · Missing: mac, agents, macos

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, 000, io · Missing: https docs, just released, exist

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.

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

34%34% 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

31%31% 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

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.

Correct prediction on native model

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