My wife needed a cycle tracker. Everything out there was either Flo (which got sued twice for sharing health data) or an abandoned GitHub project. So I built Ovumcy. Single Go binary, SQLite, Docker-ready. No analytics, no third-party APIs, no cloud. Your data stays on your server. Features: period tracking, symptom logging, predictions (ovulation, fertile window), statistics, CSV/JSON export, dark mode, Russian and English. Just pushed v0.2.5. Looking for feedback from real users.
Speak for yourself, I always did that and I found it easier with LLMs nowadays.
I hate most AI shite with a passion but when it helps my colleagues write commits which are more than “add stuff”, “fix some things” I’m fine with it.
I rarely use AI to generate code, usually only when I need a starting point. It’s much easier to unfuck AI code than to stare blankly at a screen for an hour. I’d never commit code I don’t fully understand or have read to the last byte.
I hope OP is doing the same. LLMs fail at 90% of coding tasks for me but for the other 10% (mostly writing tests, readmes, boilerplate) it’s really OK for productivity.
Ethics of LLMs aside, if you use them for exactly what they’re built for – being a supercharged glorified autocomplete – they’re cool. As soon as you try to use them for something else like “autocompletion from zero” aka “creativity”, they fail spectacularly.