Gm

Gmap: A fast command-line tool to explore Git activity

Hacker News

Gmap: A fast command-line tool to explore Git activity

Hey folks, I just released a new CLI tool called gmap, built in Rust, focused on exploring Git history visually and efficiently from your terminal. Highlights: • Heatmap view: weekly commit activity with churn and delta stats • Filetype breakdown: see which file extensions are most active • Authorship insight: per-week top contributors • Timeline and trends: sparkline and stats over time • TUI mode (–tui): navigate interactively, search, filter, view stats • Export mode: get all Git stats as JSON for further processing Install with: cargo install gmap Or check it out here: https://github.com/seeyebe/gmap Let me know what you think. Feedback welcome!

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Actual performance

2points
2comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, visual, activity · Missing: mac, agents, macos
70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: just released, io · Missing: https docs, excited, exist
70%70% 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 HackersFits the IH revenue-focused audience · Strong signals: efficiently · Missing: supports, reddit linkedin, podcasting
57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
46%46% 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
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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