Nightly Backup of Uncommitted Changes

Hacker News

A few days ago I decided to start off the New Year on the right foot by cleaning up my repositories. Unfortunately, I accidently deleted a repository that had some uncommitted changes. I lost about 2 days of work. I was pretty disappointed, so I wrote this script to backup changes I'm not ready to commit and push yet. I'm not a great bash scripter, so I'm sure the script is not that great, but hopefully this script can prevent someone out there from getting in the position I found myself a few days ago. https://gist.github.com/steven-p-walsh/b4f00f7b38587405099b3fcc8a5375c0

Visit product View on Hacker NewsJan 6, 2020

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

64%64% 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 HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

43%43% 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

35%35% 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

15%15% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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