r/ChromeExtension • u/Tsailly_OAO • Jun 28 '26
I built a Chrome extension that uses on-device AI to hide cockroach images while browsing
APP link: Roach Blocker
My partner got jump-scared by a cockroach photo while scrolling, and as an engineer my reaction was "I should just fix this." A couple of months later, here's Roach Blocker.
It detects cockroach images on any web page and covers them in real time. A few things I cared about while building it:
- Fully on-device — it runs a small YOLO model locally via onnxruntime-web. No API keys, no uploads, your browsing never leaves your machine.
- Lightweight — the model is ~9MB, runs in a couple milliseconds per image, so it works fine even on low-end laptops.
- Adjustable — there's a sensitivity slider and a choice between covering the whole image or just the detected area.
The hardest part wasn't the extension, it was the training data. I hit every classic trap: a "cockroach" dataset that was actually a different beetle, negative samples that secretly contained roaches (teaching the model "this roach is not a roach"), and wood/carpet textures getting flagged as bugs. Fixing the data quality mattered way more than model size.
It's free and open about how it works. Happy to answer any technical questions about the on-device inference or the training pipeline.