React/web component for responsive images on steroids

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Hey, OP here. This is hopefully interesting, especially for front-end developers. Nowadays images (JPEG, PNG, ...) can contain so-called Image Regions in their metadata [1] like cropping regions or regions of interest. So we've created a React component [2] that responsively displays the "best" cropping region, i.e. the one with the ratio closest to the current `<img>` element size. If you don't use React we have also built a web component. [3] With this approach you no longer have to worry about whether the size of your assets will match the ratio of their containers on all devices. The displayed image will always make sense and you don't need to maintain a bunch of hard crops anymore. Check out our demo [4] to see it in action. Feel free to ask us any questions, provide feedback, or contribute. We hope this will be useful to someone! [1]: https://iptc.org/std/photometadata/specification/IPTC-PhotoM... [2]: https://github.com/Frameright/react-image-display-control [3]: https://github.com/Frameright/image-display-control-web-comp... [4]: https://react.frameright.io/

Visit product View on Hacker NewsJun 13, 2023

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

63%63% 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 · Missing: mac, agents, macos

55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

45%45% 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

11%11% 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

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

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