Quarter – a time-tracker for personal use

Hacker News

I am working on a personal time-tracker where I try to make it as effortless as possible to keep track of what activities you spend time on. Rationale, as a consultant I am often required to use multiple time-tracking software, all made for accountants, all terrible to use. I made this little web application where I can track my daily activities and at the end of the week transfer the result to whatever system needed. The desktop is the most polished mode, mobile is functional but I mostly use if for viewing. It's open source, the hosted version has open registrations given a GitHub or GMail account. I would appreciate feedback on basically anything. I have been using the same system (during many different rewrites) for years so I am pretty blind to what needs improvement. https://github.com/eliasson/quarter https://quarterapp.com/ Thanks!

Visit product View on Hacker NewsOct 15, 2023

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

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released

53%53% 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: personal · Missing: mobile apps, ios, entrepreneurs

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

32%32% 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

12%12% 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.

Incorrect prediction on native model

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