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Secria – Private, Secure Email Built for the Quantum Era

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Secria – Private, Secure Email Built for the Quantum Era

REFERRAL/ACCESS CODE: G5062 Sign up here: https://app.secria.me/app/signup Product info: https://secria.me Hey HN! Email hasn’t evolved much in decades. It’s still vulnerable to spoofing, surveillance, phishing, and soon quantum attacks. Secria is a post-quantum secure, privacy-first email service built to fix that. We don’t scan messages. We don’t track users. We don’t store what we don’t need. Key features: -Post-quantum encryption by default (ML-KEM) -One-click alias rotation (easily generate new identities) -User verification system to prevent impersonation (code swap = trust) -No tracking pixels, no data collection, no content scanning. Ever. The idea is simple: privacy and trust, without sacrificing usability. It’s fast, clean, and built for real-world communication. We’re in early access and shipping fast. Would love your feedback, critiques, or any edge cases you think we’re missing. REFERRAL/ACCESS CODE: G5062 Sign up here: https://app.secria.me/app/signup Product info: https://secria.me We’re two full-time founders, moving quickly and want to be as transparent as possible. AMA.

Visit productView on Hacker NewsMay 20, 2025
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, email · Missing: mac, agents, macos
63%63% 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.
AppSumoStrong fit for a featured deal · Strong signals: soon, users · Missing: plus, platform, intuitive
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
39%39% 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: users · Missing: mobile apps, ios, personal
26%26% 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
17%17% 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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