Ayin – An open-source photo editing software

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Hello HN, I'm Fares A. Bakhit a junior CS student at Cairo University and I'm happy to announce my latest project, "Ayin" is an open-source photo editing software available on Windows, Linux, and MacOS (Only a Windows build is available now on GitHub but you can compile it yourself to other platforms) with an interactive real-time graphical user interface. Feel free to use and study the source code of Ayin, it's available on GitHub under the GPLv3 open-source license. I've made this project in part of the Winged Dragon Competition at the Faculty of Computers and Artificial Intelligence (FCAI-CU) Cairo University. I've used ImGui, SDL2, and OpenGL (for image rendering, not processing) A showcase video in Arabic is available, showing the functionalities of Ayin: https://youtu.be/ogkteQkJb0I Any feedback is much appreciated, thank you!

Visit product View on Hacker NewsApr 18, 2024

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

77%77% 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 · Missing: supports, reddit linkedin, podcasting

69%69% predicted probability of success on Indie Hackers, 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

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, interface · Missing: plus, intuitive, reviews

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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

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

Correct prediction on native model

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