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Anonymous Dead Man's Switch

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Anonymous Dead Man's Switch

Hey HN, I'm John from Alcazar. We just launched a Dead Man’s Switch that uses anonymous accounts instead of email+password. I'm a fan of Mullvad and their approach to accounts, so I copied it. But instead of 16 digits (53 bits of entropy), we generate 12 random words from the Monero wordlist (128 bits) and derive the account identifier+"password" using libsodium. The app is built on top of our existing Flare code and infrastructure. It uses SvelteKit, Shadcn, Tailwind CSS on the frontend, and FastAPI, SQLAlchemy, Celery on the backend. It’s hosted on Hetzner and uses Scaleway for sending email. I’d appreciate any feedback (positive or negative) on the product. I’ll be in the comments responding to questions. Website: https://alcazarsec.com/deadmanswitch Signup (quick & anonymous): https://switch.alcazarsec.com/signup

Visit productView on Hacker NewsFeb 25, 2026
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: email, using, code · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
53%53% 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: way · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
40%40% 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
19%19% 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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