Mi

Minimal Start/Stop app for tracking billable hours (Tauri)

Hacker News

Minimal Start/Stop app for tracking billable hours (Tauri)

I needed a super simple way to track billable hours, just Start when working and Stop when done. Existing tools felt heavy. So I built a tiny desktop app (macOS + Windows): - start/stop only - project/client - data stored locally - free & open source Demo: https://drive.google.com/file/d/19ijgUzdIawszL2qVmWDaF3lkQvg... Code: https://github.com/HustleCoding/time-tracker Feedback welcome, especially what would make it useful for real freelance work.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, google · Missing: agents, agent, cursor
71%71% 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
54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, way · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, open source, existing · Missing: https docs, excited, just released
37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
35%35% 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
16%16% 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.

Correct prediction on native model

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