A
A Post-Trust Vulnerability Disclosure Platform
Hacker NewsA Post-Trust Vulnerability Disclosure Platform
I’m 18. We’re building aud1t because vulnerability disclosure still runs on trust and email. That’s broken. aud1t is a centralized platform with zero-trust principles. You control your keys. Submissions are end-to-end encrypted, cryptographically signed, and tamper-evident. No assumptions. Just proof. Manifesto: https://blog.aud1t.xyz/manifesto Twitter: @Goofygiraffe06
Share cardActual performance
2points
Did not reach leaderboard
Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Strong signals: email · Missing: mac, agents, macos68%68% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released59%59% 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, reviews36%36% 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, personal32%32% 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, revenue21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting20%20% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, cryptocurrency4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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