Painting app with many brushes, reference drawing and auto color picker

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It is a free drawing app that I took the courage to finally reveal after years of development. Main idea was to make drawing from reference effortless but also make an app that makes sketching very realistic especially with pencil. After Importing a reference image the "peek" button and additional slider are unlocked, allowing for quick preview of the reference image and fast changing of the reference image opacity so that you can trace or paint over the reference. The app is still in beta so expect bugs and crashes. In the near future I am going to create a website to describe its features in detail and post some tutorial videos. Apart from the "peek" button and the reference opacity slider there are more unique features for example each brush has 5 toggles, eraser allows you to erase with the given brush, pen pressure and pen opacity toggles, blending allows to blend colors with that brush and the last one is auto color picking, when reference image is available the brush will automatically peek color from the reference image while you paint - simple magic ;). Thus far I was the only one testing the app so I am quite scared about the feedback especially worried that it will crash for you, in case it happens please let me know here or via email contact.kreska@gmail.com I am exhausted today (working to publish the app) but will do my best to reply to any questions you may have. Thank you and enjoy! With regards, Michal Bednarczyk contact.kreska@gmail.com

Visit product View on Hacker NewsApr 24, 2024

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

64%64% 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 HuntUnlikely to reach the leaderboard · Strong signals: email · Missing: mac, agents, macos

49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

45%45% 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

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

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

17%17% 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.

Correct prediction on native model

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