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Gak: Be more efficient with a Git repository

Hacker News

Gak: Be more efficient with a Git repository

Hi Hacker News, I want to show this new Python tool I've made for myself and my organization to work get things done with our Git repositories. It's basically an extension to Git, called through the command "gak <command>" that is higher level for doing things like switching branch with a wildcard, going to directories in centralized repositories and building in a generic way. It also has Jira integration, assuming some basic convention for branch names. It's also extendable, allowing you to add new subcommands by simply adding a file in a directory. It's been really useful to speed up my development process, and so I wish to gather feedback before I publish it officially on PyPi. I'll accept pull request and welcome issues and enhancement suggestions in the Github repo. Article explaining how to use it: http://softwarebeyondcode.blogspot.com/2018/08/gak-be-more-e... The source code is here: https://github.com/SamuelLongchamps/gak Keep hacking!

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, code, plain · Missing: mac, agents, macos
72%72% 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.
AppSumoStrong fit for a featured deal · Strong signals: efficient · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: hacker news, io · Missing: https docs, excited, just released
44%44% 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
38%38% 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
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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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