Duotone image filter to show your support for Ukraine

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This duotone art project is inspired by the stories coming out of Ukraine when they were first invaded. Being outside the conflict itself and wanting to help, my thinking was along the lines of, “How can I demonstrate my support for Ukraine and get involved?” Well, creating an image filter seemed like a good way to do that. I’ve been learning to _paint_ with p5, and was experimenting with pixel manipulation the weekend before the invasion. I haven’t really manipulated pixels outside of photoshop-like tools, and _painting_ with p5 has brought me a renewed sense of joy for programming. This is where the duotone idea of the filter came from, “Can I turn any image into the colors of the Ukraine flag and it still be recognizable?" After a day or two of creating and polishing the duotone method with static images, it was looking pretty good. Made source images dynamic, and I could ship faster by not breaking out image processing into a separate service (as a bonus there’s privacy too). At this point, I do not want to think about having to manage all the I/O that comes with an image processing service. KISS, and see if people want/use it before putting in more effort there. Dress it up, add in a few resources to help others get involved too, and that’s how we get this first version of ukrainespirit.art.

Visit product View on Hacker NewsMar 9, 2022

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

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

38%38% 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

13%13% 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.

Incorrect prediction on native model

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