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Piqt (iOS)

on device photo and video curator

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I've just released Piqt, an on-device photo/video curator. For years I've wanted to get my Apple Photos library under control, but there just aren't enough hours in the day. I started out in Jupyter notebooks to see how much I can use AI models to automate it and Piqt is the result.My goals with Piqt:- Fully on device: I use a number of ML models, Apple Vision API, clustering algorithms to rank and sort your photos. Everything runs on device.- Private and Safe: Piqt has no accounts, no cloud services, no personal identifiers. Images or personal information never leave the device. Piqt also CAN'T delete your photos. I have a test and validation step in the release process to ensure no delete APIs exist in the code. Instead, Piqt stages things you want to clear out in an album, which you can delete when you're ready.- A library you can use. I didn't just want to clean out images or clear up space. There are plenty of apps for this. My ultimate goal is to make your (and my) photos easier to navigateI'm just looking for feedback on the app and the approach. I'd love to chat about the vision models I'm using and hear from you about any research or techniques I've missed.App Store Link: https://apps.apple.com/us/app/piqt-photo-video-curator/id677...Blog with detail on technical decisions: https://piqt.app/blog/piqt-design-values/

HN · launched Aug 30, 2026 · 5 upvotes (top 87%) · 0 views

Niche: photo library cleanup apps · 92 launches, 20 in the last 7 days ▲ +122% · Explore this niche →

Category and tags

  • Horizontal
  • Workflow automation
  • B2C
  • AI feature
  • Commercial product

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Could you build this?

Partial While the SwiftUI UI and integration with PhotoKit are straightforward, building high-performance on-device ML scoring pipelines across thousands of local photos without thermal throttling or battery drain requires deep iOS optimization and model tuning.

What it would actually take: A production app needs an iOS/SwiftUI frontend integrated with Apple's PhotoKit and Vision frameworks, coupled with CoreML conversion of aesthetic scoring models (like Google's NIMA) and image feature printing. The hard part is memory-efficient batch processing, background execution limits, and maintaining responsive UI while processing gigabytes of raw images on-device. It requires iOS systems-level performance tuning and CoreML quantization expertise.

Discussion

No comments on this launch.

Competitors

Other products that read as similar to this one : 160 launches clear the similarity bar, closest 8 shown.

Attention rank: #156 of 161 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 270 days after the earliest competitor.

The niche, labels, vibe code rating and competitor comparison on this page are analysis by Nicheloom. If you use them, please credit nicheloom.com. How you may use our data.