Ward – a file vault written in bash

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just wanted to share ward, a tool i hacked together to encrypt and manage sensitive files in a vault folder. it's written in bash and meant to be cloned and then stored using git or version control. you don't have to store it that way, it's just why i created it. what ward does: encrypts the files in ./vault/ into a single .gpg file, checks to see if your files have been tampered with, generates totp codes if you need them to recover critical accounts. how to use it: clone the repo, toss your files into the vault directory, and run yarn encrypt. now commit your new [private] repository or save it somewhere. that's it. repo link: https://github.com/oeo/ward feedback welcome, or just let me know if you find it useful.

Visit product View on Hacker NewsAug 26, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, single, using · Missing: mac, agents, macos

57%57% 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.

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

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

37%37% 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 · Missing: plus, platform, intuitive

32%32% 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

11%11% 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.

Incorrect prediction on native model

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