HEVCut, AVIF photo encoding for iPhone, iPad and Mac

Hacker News

HEVCut is a photo and video compression app for Apple platforms I have been working on. Today's update adds AVIF as an output format for photos, which I believe makes it the first app on the App Store to ship AVIF encoding on iOS. Apple added AVIF decode in 2022 (iOS 16, macOS Ventura) but never shipped an encoder, public or private. Photos cannot export AVIF, ImageIO cannot write it, and no other third party app on the store does it either. So if you wanted AVIF files coming off an iPhone, your options were "send the original somewhere else and re-encode it" or "wait." On typical iPhone photos, AVIF lands around half the size of HEIC at matched visual quality. A 5 MB HEIC compresses to roughly 700 KB. HDR (including gain map HDR content from newer iPhones) is preserved, SDR works correctly across readers, everything runs on device. The rest of the app does the usual library cleanup things: HEIC and HEVC recompression, duplicate detection, surfacing space hogs, swipe to delete, a private vault. AVIF is the new piece.

Visit product View on Hacker NewsMay 22, 2026

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

92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: ios, video · Missing: mobile apps, personal, entrepreneurs

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% 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 · Strong signals: platform · Missing: plus, intuitive, reviews

26%26% 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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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