u/eroxy

Solo dev, day job in software. WorkFlow started because I kept forgetting to track my own hours and every existing app annoyed me in a different way: cloud account here, subscription there, geofencing behind a paywall everywhere.

The design decision that shaped everything: no backend. Not as a privacy marketing line, but literally the app has no server side.

What it does today (just shipped v1.5): auto clock-in/out via geofencing at up to 5 workplaces, overtime balance, PDF/CSV export, backups you can share or save to the phone. German and English.

Some numbers people ask about: roughly 31k lines of hand-written code, a third of that tests - the suite runs on an Android emulator before every push, because with local-only data a data-loss bug would be unrecoverable. About 80% of the actual work was edge cases I never planned for: OEM battery killers, GPS drift at the geofence boundary (fixed with hysteresis - exit radius is 1.2x entry), double notifications from two detection layers racing each other.

The part I haven't cracked: distribution. The app sits at a handful of installs because Play Store search is brutal for new apps in a category owned by Toggl and Clockify. This post is part of me finally doing marketing instead of adding features.

Play Store: https://play.google.com/store/apps/details?id=com.timetracker.workflow

Ask me anything about the no-backend constraint, the geofencing stack (Flutter + native Kotlin), or what I would do differently.

The Pentagon released 132 declassified UAP PDFs on war.gov last week, around 4,157 pages total.

I wanted a faster way to search through them than opening PDFs one by one, so I built this:
https://uap-rag.pages.dev

What it does:

• semantic search across the indexed documents
• source cards with snippet, source, date, and page number
• click-through citations back to the underlying PDF page
• optional AI answers grounded in retrieved documents

Built with:

• Astro + Svelte on Cloudflare Pages
• Cloudflare Pages Functions for search / chat / usage cap
• Cloudflare Vectorize for retrieval
• OpenAI text-embedding-3-small for embeddings
• Cloudflare R2 + KV for chunk hydration, caching, and rate limiting
• Anthropic Haiku 4.5 for the cited answer stream

Total cost so far is about $1.20, mostly from generating embeddings for 5,694 chunks.

To keep it from getting abused, I limited AI usage to 3 queries per user every 24 hours and set a hard monthly spend cap of $30 across the model providers.

Big shortcut: I used the alex-zhang42/ufo-pursue-open-atlas Hugging Face dataset as the text layer, which saved me from having to OCR the scanned files myself.

Happy to share implementation details or what broke along the way.