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Secs-man, a secrets manager you can (not) rely on

Hacker News

Secs-man, a secrets manager you can (not) rely on

This is a tool to manage encrypted local backups of secrets. The core idea is that it aims to be usable without depending on it, meaning that even if the software disappeared from the face of Earth tomorrow, your data would still be recoverable. It also integrates nicely with NixOS (which is what I use, though it does not require NixOS to be used). I have summed up a bit of explanation and some answers to reasonable questions in a blog post: https://baldino.dev/blog/secs-man/

Visit productView on Hacker NewsJun 25, 2026
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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: answers · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
29%29% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
28%28% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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