Plot lines of code over time in Git repositories

Hacker News

It's a small wrapper around cloc ( https://github.com/AlDanial/cloc ), which just provides a convenient way to plot lines of code over time in a Git repository. The script runs cloc on each commit in your current branch, starting from HEAD tracing back until the first commit. For large repositories with many commits, you can tell it to only count every n:th commit with the '--step' option to speed up processing. The way it works is quite sub-optimal, since every single file is counted in each commit, even if the file didn't change since last iteration. Might be a fun project later to find some nice optimizations, maybe by counting files one by one and keeping a dict of line count by file hash, or something along those lines. Maybe someone will find this script useful, maybe not, anyways happy to hear if someone has any thoughts :)

Visit product View on Hacker NewsMay 20, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: single, code · 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 NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

45%45% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

42%42% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

39%39% 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

24%24% 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

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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