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Gitstats - your coding stats, private and work repos, no GitHub token

Hacker News

Gitstats - your coding stats, private and work repos, no GitHub token

Hey HN. I built this dashboard because I wanted to see how much code I write every day and how my productivity changes. You can preview it with seeded demo data without registering here: https://gitstats.org/demo I'm a software engineer at a startup and I constantly build my own projects, but most of my GitHub activity is in private repos and I often was curious how much code do I produce in total and how do I compare to my colleagues/friends. So I built this dashboard to show all the info you could want, made it look cool and added some competitive aspect to it. I'm aware that line-counts and commits are not good metrics for productivity, but it is still interesting to track them. This is not a commercial product, just an interesting toy for developers which can also be useful to notice trends in your work. While building it I found that GitHub doesn't have any native API to share only the contribution activity without requiring read or write permissions on the repos. I didn't want the app to require any tokens with access to your repos, so I landed on a solution where an npm command (npx @yaroslavhaidash/gitstats-cli@latest link) would count the activity using the git logs locally and then only upload the numbers. This way no sensitive data ever leaves your computer. To use it, simply sign in with GitHub, run one npm command and your statistics will automatically backfill from up to one year ago. One limitation is that it will only backfill statistics found on the device you connected, if you use multiple computers for different repos, you will have to run the npm command from all of them. There is more information about how it works in https://gitstats.org/docs . The server is closed-source, but https://github.com/yaroslavhaidash/gitstats-cli is public and open-source with MIT so anyone can check out how it works. Any feedback on the mechanism or the UI is very welcome!

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Product HuntOn track for Day 1 leaderboard · Strong signals: computer, activity, using · Missing: mac, agents, macos
97%97% 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 · Missing: supports, reddit linkedin, podcasting
75%75% 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: io · Missing: https docs, excited, just released
49%49% 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
48%48% 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
23%23% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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