Pa

Partially encrypt a file based on its HEREDOCs

Hacker News

Partially encrypt a file based on its HEREDOCs

Hi HN! I wrote a tool that partially encrypts files based on the presence of a HEREDOC. Check it out: https://github.com/higgins/privatize When added to a git repo, it will automatically transparently encrypt/decrypt files you want privatized. For example if you configured your repo to privatize the file `example.txt`, you could write: ``` Today I a burrito. <<PRIVATE I was on the toilet for hours. PRIVATE I got a lot of reading done. ``` but when git-commit'ed would become: ``` Today I a burrito. <<PRIVATE xuJ0fld2vmNWaVLogTIufmWsiFso PRIVATE I got a lot of reading done. ``` Diffing works as you expect (on the unencrypted source) and only those with the `privatize` symmetric key would be able to unlock and decrypt these files. Why did I do this? I keep a public log of what I plan to accomplish and what I'm working on both personally/professionally. At the end of the day, I write a summary of everything that happened. Naturally, there are some details of my life that should be kept private (details of too-be-launched projects, sensitive family events, etc). It's helpful for me to track everything in one file so as to keep the day's context together. Would love to know what you think! Justin

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Product HuntOn track for Day 1 leaderboard · Strong signals: context · Missing: mac, agents, macos
74%74% 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: io · Missing: https docs, excited, just released
65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
59%59% 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
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
31%31% 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
16%16% 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.

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