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Interactive Git Training Use Cases

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Interactive Git Training Use Cases

Hello, I have been working for years with junior developers and I find that many of them struggle to get out of some "delicate situations". Example "detached head", "wrong rebase", "for some reason I see duplicate commits" and so on. To help my colleagues I came up with a list of git uses cases that can be tested in a sort of interactive playground using a shell script that will set-up the use case and then we can go ahead and try to solve the problem. The main advantage here is that all use cases are reproducible and that by looking at the shell script we can see how we got in the mess in the first place. Everything is written in posix shell and should work on linux, macos and windows (with git bash). Any comments, feedback and contributions are very welcome ! PS: This is somewhat inspired by https://ohshitgit.com/

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, using · Missing: agents, agent, cursor
68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
56%56% 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 · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, 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 · Strong signals: training, active · Missing: arr, mrr, revenue
12%12% 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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