Piqt (iOS) – on device photo and video curator

Hacker News

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 navigate I'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/

Visit product View on Hacker NewsAug 30, 2026

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, ios · Missing: supports, reddit linkedin, podcasting

90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: model, apple, apps · Missing: mac, agents, macos

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, personal, apps · Missing: mobile apps, entrepreneurs, month

59%59% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: just released, exist, ide · Missing: https docs, excited, lua

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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