ArtistAssistApp – a web app to paint better with ease

Hacker News

Hey HN! I want to show my new open-source project ArtistAssistApp . ArtistAssistApp - the web app to paint better with ease. Tools for realistic color mixing based on real paints, tonal value drawing, simplified sketching, and more. Import your own photos, select any desired color directly from the image, and learn how to mix it with your paints. The web app provides a step-by-step guide on how to precisely mix that color using your own paints using atomic or optical mixing. Atomic mixing is the physical mixing of colors together, while optical mixing is the result of placing a transparent layer of color over another color (glaze technique). Save instructions on how to mix your favorite colors from the paints you have for quick reference. Smooth your photo to reduce detail and focus on the big shapes and proportions of your subject, and learn how to simplify and abstract your paintings. Use tonal value sketches that capture the light and shadow of your subject to learn how to create contrast and depth in your paintings. Works on desktops, laptops, tablets and smartphones. You can try it at < https://artistassistapp.com/ >. No login or registration required. The source code is available on GitHub < https://github.com/eugene-khyst/artistassistapp >.

Visit product View on Hacker NewsOct 25, 2023

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

81%81% 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 · Strong signals: new, physical, using · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

47%47% 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 · Missing: https docs, excited, just released

46%46% 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

28%28% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Incorrect prediction on native model

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