I spent one year building a photo search app because I couldn't find a photo of my son I knew existed somewhere on my PC
Like many side projects, this one started with a specific, annoying problem.
I had 2TB of photos across three external drives going back to 2001. I knew a photo existed — somewhere — but finding it meant opening drive after drive, folder after folder, hoping to get lucky. Cloud tools were a non-starter for me. I didn't want to upload decades of personal photos to Google or Amazon.
So I built Pitara.
It reads the EXIF metadata already embedded in every photo — GPS coordinates, timestamps, camera model, altitude, builds a local search index using Apache Lucene, and lets you search in plain English. Results in under half a second, no GPU needed, no internet required.
Queries that actually work:
```
summer 2019 in paris
father's day 2019
above 8000 feet with canon
sunday evenings from 10 years
weekend in peru above 12000 feet
```
One year later, 3,500+ downloads, and I still use it every day.
Some things I learned building it:
- Lucene.NET is incredibly powerful but the .NET wrapper documentation is sparse. Learned mostly by reading the Java docs and translating.
- EXIF metadata is a mess. Every camera manufacturer does something slightly different and edge cases are endless.
- The hardest feature wasn't search. It was making external drives work seamlessly when they reconnect.
- On-device face recognition with ONNX is very doable without a GPU. Slower than cloud, but users don't mind when their photos never leave their machine.
Windows only for now. Free. getpitara.com
Happy to talk technical details — the Lucene schema, the offline drive architecture, the NLP query pipeline, anything.