Git-branches – list of branches with behind/ahead counts

Hacker News

git-branches is a go gettable command that displays branches with behind/ahead commit counts. If you've ever used GitHub, you'll notice their branches page displays this info. This is that, but for your local git repo, in the terminal. Go to any git repo you're working on and type: $ git branch feature/sanitized_anchor_name_context * master unfinished-attempt/alternative-sort-order wip/get-tweet-body Now, do `go get -u github.com/shurcooL/cmd/git-branches` on any machine with Go installed, and then: $ git-branches | Branch | Behind | Ahead | |-------------------------------------------|-------:|:------| | feature/sanitized_anchor_name_context | 119 | 1 | | **master** | 0 | 0 | | unfinished-attempt/alternative-sort-order | 67 | 1 | | wip/get-tweet-body | 40 | 1 | It supports -base option if you want to compare against non-master branch. It also supports -remote mode, where it compares local branches to their remotes: $ git-branches -remote | Branch | Remote | Behind | Ahead | |-------------------------------------------|--------------------------------------------------|-------:|:------| | feature/sanitized_anchor_name_context | origin/feature/sanitized_anchor_name_context | 5 | 0 | | **master** | origin/master | 0 | 1 | | unfinished-attempt/alternative-sort-order | origin/unfinished-attempt/alternative-sort-order | 0 | 0 | | wip/get-tweet-body | | | | It's easy to see which branches are up to date with remote, which ones need to be pushed/pulled.

Visit product View on Hacker NewsJun 13, 2015

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, context · Missing: agents, macos, agent

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.

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

51%51% 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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% 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

29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% 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.

Correct prediction on native model

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