Launching today
Mirowl
Search all your screenshots via a local OCR-powered AI
72 followers
Search all your screenshots via a local OCR-powered AI
72 followers
A frictionless, local-first Mac app to index and search all your screenshots and image assets. Built with Rust/Tauri for zero footprint and powered by native macOS Vision for 10x OCR accuracy. Optional cloud, no tracking—just pure utility.
Mirowl
Built a K1 document OCR pipeline professionally and spent the last week generating hundreds of AI video storyboard screenshots across GPT and Kling. The screenshot search problem is real — I was manually scrolling through folders looking for specific reference images. Curious how Mirowl handles images with minimal text — like a cinematic still or a product photo with just a logo. Does the AI understand visual content beyond just OCR, or is it purely text extraction from images?
Mirowl
Local OCR is a smart call, especially for screenshots that often have personal stuff in them. Curious which engine you went with under the hood, Apple Vision or something custom? I work with on-device OCR too and the accuracy on dense text was the hardest part to get right.
Mirowl
@erdembilgin Great question! The accuracy on dense text is definitely the 'final boss' of local OCR.
For Mirowl, I moved entirely to the native Apple Vision framework for the text extraction. In my testing for v1.1.0, it was spot on compared to general models, especially for high-res Retina shots.
For the auto-categorization and tagging, I’m using a local model so that we can keep the entire pipeline on-device.
@safiullah_mohamed Makes sense. Keeping the whole pipeline on-device is the right call for screenshots. Nice work, will keep an eye on Mirowl.
Mirowl
@erdembilgin Thanks, Erdem! You are spot on, privacy is exactly why I started with screenshots.
But I actually designed the engine to be flexible, you can point Mirowl at any folder on your Mac (like project assets, downloads or design archives) and it will index and tag them exactly the same way. The goal is to make all your visual knowledge searchable, no matter where it's stored. Appreciate you following along!
Thoth - Your Private AI Scribe
Nice that everything happens on device, as i state with thoth, our devices have more computing power than the Apollo guidance computer so why offload computing to the cloud !
Quick question: does Mirowl rename the files or propose titles based on the content, or is it search-only? Auto-naming "Screen Shot 2026..." into something findable would be huge for me. To keep that local it could be done with apple intelligence
Mirowl
@matthieu_v Love the Apollo Guidance Computer analogy! You’re absolutely right - our local hardware is more than capable of handling these workflows without a 'cloud tax.'
Regarding re-naming, Currently, Mirowl keeps the original file name on your disk to ensure we don't disrupt your existing filing system, but allows you to rename the asset within the app for searchability.
However, auto-naming 'Screen Shot' files is one of my top roadmap items. I’m actually exploring how to leverage Apple Intelligence and local LLMs to do exactly that in the next version. It’s the final step to truly killing the desktop clutter!
Nice one @safiullah_mohamed , upvoted :)
When you say local OCR powered AI, how much extra infra would it need to run?
Mirowl
@aiswarya_s Great question! The answer is surprisingly, Almost none!
Because Mirowl uses the native macOS Vision framework for OCR and on-device processing, we are not spinning up a heavy, power-hungry model from scratch. We’re leveraging the hardware-accelerated neural engine already built into our Mac.
This is why we chose the Rust/Tauri stack - to keep the background footprint near-zero while letting the OS do the heavy lifting natively. It won't slow down your machine or eat your RAM like a cloud-syncing Electron app would.
Thanks for the upvote!
Mirowl
@aiswarya_s Great question! The answer is surprisingly, Almost none!
Because Mirowl uses the native macOS Vision framework for OCR and on-device processing, we are not spinning up a heavy, power-hungry model from scratch. We’re leveraging the hardware-accelerated neural engine already built into our Mac.
This is why we chose the Rust/Tauri stack - to keep the background footprint near-zero while letting the OS do the heavy lifting natively. It won't slow down your machine or eat your RAM like a cloud-syncing Electron app would.
Thanks for the upvote!
The local-first approach and text search features look interesting. Is Mirowl currently macOS-only, or are there plans for a Windows version in the future?