Browser-Based Dithering Playground for Artists and Tinkerers

Hacker News

Hi Everybody, Last year I built Chromadith, a browser-based image dithering tool that lets you transform photos into pixelated, stylized art using a variety of dithering algorithms and custom color palettes. It runs entirely in the browser with no dependencies or backend — just plain HTML, CSS, and JS. Features: - Pick or extract a color palette (k-means / median-cut) - Choose from several dithering methods (Error Diffusion, Ordered, ...) - Tweak exposure, contrast, and output size It’s built for fun, but I’d love to hear your feedback: - Any extra features you'd be interested in? - Are there palettes, effects, or export options you'd want? - Any improvements to the general usability / workflow? Thanks! Happy to answer any questions if anyone's curious.

Visit product View on Hacker NewsMay 26, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, plain · Missing: mac, agents, macos

65%65% 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

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

50%50% 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 · Missing: mobile apps, ios, personal

29%29% 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

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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